{"meta":{"query_hash":"470292174217","filters":{"topic":"Hate Speech and Cyberbullying Detection"},"cohort_total":719,"direct_labels_cover":2,"predictions_cover":719,"exported":719,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/470292174217","api":"https://metacan.xera.ac/api/v1/cohort?topic=Hate+Speech+and+Cyberbullying+Detection"},"results":[{"id":"W1153515617","doi":"10.60082/2817-5069.2807","title":"Book Note: Hate Crimes in Cyberspace, by Danielle Keats Citron","year":2015,"lang":"en","type":"article","venue":"Osgoode Hall law journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cyberspace; Political science; Criminology; Law and economics; Psychology; Sociology; Computer security; Law; Computer science; The Internet; World Wide Web","score_opus":0.012401891350482185,"score_gpt":0.2379123412882997,"score_spread":0.2255104499378175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1153515617","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004054984,0.16030577,0.0010329318,0.5716916,0.1054804,0.00006150088,0.0006184846,0.00022481139,0.160179],"genre_scores_gemma":[0.0035553514,0.045497775,0.00049378275,0.13686213,0.03137736,0.00007090506,0.00021668573,0.00018079979,0.78174525],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887854,0.00018704211,0.000056260254,0.00020004113,0.0005926746,0.00008552908],"domain_scores_gemma":[0.99596846,0.0021223375,0.0001426392,0.00013749514,0.0012580414,0.00037102983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012584572,0.0011984315,0.0009997088,0.0022562784,0.006582836,0.0072505586,0.0015994734,0.010769794,0.022942916],"category_scores_gemma":[0.005450797,0.0009038256,0.00043737676,0.0017019017,0.0030799683,0.0076826075,0.002248888,0.009798212,0.020685064],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.2830235e-7,0.0000018829066,0.000017656477,0.000009160749,4.2825337e-7,0.000005102738,0.00003383856,0.000010120694,0.000007069744,0.00073405466,0.99627775,0.002902105],"study_design_scores_gemma":[0.0000020558648,0.0000044049298,0.00020058139,0.00010938575,0.0000015162033,0.00003946018,0.00018400433,0.00003368269,0.00003052386,0.0015794788,0.9978065,0.000008350212],"about_ca_topic_score_codex":0.05332662,"about_ca_topic_score_gemma":0.17347991,"teacher_disagreement_score":0.05332662,"about_ca_system_score_codex":0.0038541842,"about_ca_system_score_gemma":0.0044634426,"threshold_uncertainty_score":0.10603243},"labels":[],"label_agreement":null},{"id":"W145671193","doi":"10.33972/jhs.35","title":"A Crime By Any Other Name: The Semantics of Hate","year":2005,"lang":"en","type":"article","venue":"Journal of Hate Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Criminalization; Hatred; Phrase; Hate crime; Sociology; Semantics (computer science); Wish; Criminology; Law; Heuristic; Linguistics; Political science; Computer science; Epistemology; Philosophy; Politics","score_opus":0.01997001109949486,"score_gpt":0.27830772961086614,"score_spread":0.2583377185113713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W145671193","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07043599,0.032092366,0.13277476,0.11240581,0.0047075246,0.0000706228,0.00059433596,0.0002634165,0.64665514],"genre_scores_gemma":[0.9716608,0.0037665907,0.009023887,0.0050049177,0.001928818,0.000060044888,0.00018030647,0.0001526533,0.008221994],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99738926,0.0017531428,0.00013963664,0.00023049914,0.00032442875,0.00016305882],"domain_scores_gemma":[0.99411833,0.00357994,0.00056432054,0.00070210634,0.0006730971,0.00036211926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027747748,0.0005641801,0.00045447773,0.0027680171,0.005037074,0.00779772,0.001272573,0.0030889064,0.003266842],"category_scores_gemma":[0.0075408644,0.0003377683,0.00048823253,0.0027482805,0.027646963,0.017214464,0.00330751,0.0045211916,0.00065523066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010116288,0.000004694579,0.00025836285,0.000025122867,0.0000035641826,0.00006317322,0.008738798,0.000051337374,0.00005313198,0.9851238,0.003181166,0.0024867535],"study_design_scores_gemma":[0.0000083210625,0.000019555291,0.0011136385,0.00015931511,0.000010144561,0.0005402207,0.009844898,0.0008987421,0.0001681535,0.8837245,0.10348953,0.000022950313],"about_ca_topic_score_codex":0.0050558494,"about_ca_topic_score_gemma":0.004506277,"teacher_disagreement_score":0.00779772,"about_ca_system_score_codex":0.0026926987,"about_ca_system_score_gemma":0.0010593008,"threshold_uncertainty_score":0.019536972},"labels":[],"label_agreement":null},{"id":"W146019151","doi":"","title":"Are Hate Speech Provisions Anti-democratic?: An International Perspective","year":2010,"lang":"en","type":"article","venue":"American University international law review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"International law; Democracy; Perspective (graphical); Political science; Law; Law and economics; Sociology; Computer science; Politics","score_opus":0.01069265217976472,"score_gpt":0.2668040976850524,"score_spread":0.2561114455052877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W146019151","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036802683,0.09941038,0.0010686704,0.231984,0.0020424398,0.00012869749,0.0014481148,0.00008236953,0.62703264],"genre_scores_gemma":[0.73555994,0.046284456,0.0010441202,0.061625935,0.0010343633,0.00013431402,0.00037471802,0.00009237908,0.1538498],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9932545,0.0012916058,0.00023836728,0.0008086247,0.0014441232,0.0029629362],"domain_scores_gemma":[0.98973244,0.005117802,0.0010082992,0.00048290633,0.0029800164,0.0006784614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047294823,0.0008566285,0.0006053742,0.004150633,0.016746014,0.018491497,0.0030105093,0.013691989,0.010883252],"category_scores_gemma":[0.00895559,0.0006706461,0.0005548981,0.0048595727,0.015938027,0.006357787,0.00242796,0.009321449,0.00068406475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006441045,0.00006578013,0.0059115742,0.00026976434,0.000039717645,0.00090595044,0.0153006045,0.0003460401,0.00027049184,0.85476935,0.09102547,0.031030744],"study_design_scores_gemma":[0.000030918676,0.00007042264,0.057675786,0.002214563,0.0002248527,0.0003480113,0.030445615,0.00050743535,0.0009010913,0.03859013,0.8687218,0.00026930726],"about_ca_topic_score_codex":0.91637456,"about_ca_topic_score_gemma":0.9517168,"teacher_disagreement_score":0.91637456,"about_ca_system_score_codex":0.062401902,"about_ca_system_score_gemma":0.045042988,"threshold_uncertainty_score":0.45275962},"labels":[],"label_agreement":null},{"id":"W1486271789","doi":"10.1017/cbo9780511576614.013","title":"Sampling and coding of media dissent and discipline","year":2009,"lang":"en","type":"other","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dissent; Coding (social sciences); Sampling (signal processing); Statistics; Computer science; Political science; Mathematics; Law; Telecommunications","score_opus":0.01448043315542063,"score_gpt":0.25001866674863427,"score_spread":0.23553823359321363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486271789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16765831,0.00057021086,0.23202808,0.003566033,0.0023336331,0.18845433,0.17038363,0.0032550779,0.23175068],"genre_scores_gemma":[0.11807676,0.0012710128,0.3287002,0.0014197081,0.0005049057,0.33771026,0.076496534,0.001470112,0.1343505],"study_design_codex":"not_applicable","study_design_gemma":"design_other","domain_scores_codex":[0.9769518,0.010287371,0.004089057,0.0016814545,0.0059127114,0.0010775172],"domain_scores_gemma":[0.91554344,0.030702138,0.005686291,0.012816729,0.03374324,0.0015082691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019874945,0.0008791156,0.0006436951,0.007655775,0.002431216,0.001564403,0.002072755,0.000935458,0.056921925],"category_scores_gemma":[0.103039056,0.0007937733,0.00032560405,0.008434804,0.0012673482,0.0010461968,0.002769647,0.0018294295,0.021474242],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005183513,0.0009733837,0.031055145,0.0016696332,0.000026939984,0.0012661559,0.07198879,0.001921561,0.008293291,0.047288626,0.4232645,0.41173354],"study_design_scores_gemma":[0.00016971526,0.00040284893,0.053896096,0.0014006973,0.000022911714,0.0003920624,0.045563105,0.00438071,0.011553472,0.019349815,0.86270815,0.00016050645],"about_ca_topic_score_codex":0.012396411,"about_ca_topic_score_gemma":0.019443825,"teacher_disagreement_score":0.056921925,"about_ca_system_score_codex":0.003129983,"about_ca_system_score_gemma":0.0054772976,"threshold_uncertainty_score":0.19042283},"labels":[],"label_agreement":null},{"id":"W148763511","doi":"","title":"Hate Speech: North and South","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Individualism; Egalitarianism; Libertarianism; Collectivism; Politics; Sociology; Political science; Law; Political economy; Media studies","score_opus":0.00518733603198577,"score_gpt":0.18949871784517647,"score_spread":0.1843113818131907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W148763511","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81512463,0.0037818253,0.00064282137,0.019737484,0.000276742,0.00007012553,0.0009873994,0.000028148143,0.15935084],"genre_scores_gemma":[0.9886495,0.0013835479,0.00013935356,0.0031975713,0.00005327334,0.0000193708,0.00028684337,0.00003173866,0.006238893],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99747247,0.00023478197,0.00007907243,0.00041673132,0.0006774685,0.001119482],"domain_scores_gemma":[0.995771,0.000652228,0.0007710194,0.00026860286,0.001688577,0.0008485364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002101443,0.00034586512,0.00054209714,0.003241411,0.011899314,0.004391415,0.0006510399,0.0011126668,0.0070403456],"category_scores_gemma":[0.0052743796,0.00032165082,0.00036931774,0.0041677975,0.0074602654,0.0021890712,0.003511592,0.0023455112,0.0007104076],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023763716,0.0000618327,0.25894117,0.0002689694,0.000076316355,0.00087877,0.6346877,0.00006017945,0.0028156037,0.016937064,0.021228425,0.06380627],"study_design_scores_gemma":[0.00000740727,0.000022391474,0.53844535,0.00022749264,0.000036746376,0.00043854283,0.42029774,0.0000703858,0.00046881914,0.0011935164,0.038745433,0.00004623702],"about_ca_topic_score_codex":0.8993259,"about_ca_topic_score_gemma":0.95167166,"teacher_disagreement_score":0.8993259,"about_ca_system_score_codex":0.016282944,"about_ca_system_score_gemma":0.016786395,"threshold_uncertainty_score":0.2025339},"labels":[],"label_agreement":null},{"id":"W1488413727","doi":"10.1093/acprof:oso/9780199548781.001.0001","title":"Extreme Speech and Democracy","year":2009,"lang":"en","type":"book","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":258,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Incitement; Dignity; Constitutionality; Hatred; Democracy; Supreme court; Law; Racism; Ideology; Political science; Terrorism; Xenophobia; Sociology; Politics","score_opus":0.018016677792033656,"score_gpt":0.21874649654289585,"score_spread":0.2007298187508622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488413727","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066096117,0.014471642,0.004286328,0.010011689,0.0010692208,0.000017804829,0.00004298469,0.00007417016,0.96341646],"genre_scores_gemma":[0.3935062,0.012389747,0.0021221156,0.004475794,0.0014014535,0.00004847399,0.000112277354,0.00009002847,0.585854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994578,0.00024417977,0.00001419196,0.000062073595,0.0001766155,0.000045190303],"domain_scores_gemma":[0.9993144,0.00044976023,0.000042650132,0.0000643754,0.000079305224,0.0000494239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039949006,0.00021652321,0.00016253404,0.00044220162,0.0015061551,0.0035676109,0.00028944,0.0009881472,0.009383668],"category_scores_gemma":[0.0012843766,0.00009329279,0.00013183389,0.00042812846,0.0045836293,0.0022582735,0.0012394824,0.00205183,0.0016928005],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006645374,0.000011568437,0.00010603496,0.00005658407,0.0000017685112,0.00006062873,0.0028690775,0.00024528496,0.0001777222,0.9179907,0.044880506,0.033593446],"study_design_scores_gemma":[0.000005088544,0.000016923586,0.000454412,0.00023592835,0.0000026359198,0.00018635883,0.0015472252,0.00036192834,0.00040384816,0.26580244,0.730976,0.000007252593],"about_ca_topic_score_codex":0.0009932455,"about_ca_topic_score_gemma":0.001376587,"teacher_disagreement_score":0.009383668,"about_ca_system_score_codex":0.001226906,"about_ca_system_score_gemma":0.000803477,"threshold_uncertainty_score":0.03139156},"labels":[],"label_agreement":null},{"id":"W1491124351","doi":"","title":"Freedom and Responsibility: Discussion on Transmission Ethics From the Perspective of New Media","year":2015,"lang":"en","type":"article","venue":"Canadian social science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Phenomenon; Misconduct; Perspective (graphical); Freedom of the press; Transmission (telecommunications); Sociology; Public relations; Law; Political science; Telecommunications; Computer science; Epistemology","score_opus":0.04782364048739499,"score_gpt":0.2894533894646255,"score_spread":0.24162974897723052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491124351","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08607899,0.014753104,0.09164689,0.41447216,0.0021403139,0.00028686583,0.000053544114,0.000047642337,0.3905206],"genre_scores_gemma":[0.971066,0.0037247946,0.004421635,0.011134387,0.0011492508,0.00017971247,0.000012771231,0.00003981599,0.008271596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9694624,0.021797528,0.0007166796,0.0015071038,0.004888785,0.0016274913],"domain_scores_gemma":[0.952229,0.041422583,0.0016787102,0.0011602923,0.002345078,0.0011642604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029544033,0.0005051765,0.0006691403,0.0024681261,0.010381669,0.011463425,0.002189995,0.011752003,0.002604369],"category_scores_gemma":[0.030551724,0.00036852396,0.0008509406,0.0016139792,0.080534756,0.022700746,0.007882495,0.009719962,0.00026124212],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003602156,0.000008952467,0.00017482162,0.000031147418,0.000002252156,0.00022843588,0.027618328,0.00009517086,0.000054879485,0.9690258,0.00081098423,0.0019456985],"study_design_scores_gemma":[0.000017004088,0.000034390272,0.0007987172,0.00059822976,0.00000846895,0.00068062946,0.04659316,0.0013068172,0.00045609206,0.8540753,0.09538832,0.00004280804],"about_ca_topic_score_codex":0.0069894567,"about_ca_topic_score_gemma":0.00407327,"teacher_disagreement_score":0.029544033,"about_ca_system_score_codex":0.0098866215,"about_ca_system_score_gemma":0.008141654,"threshold_uncertainty_score":0.15624571},"labels":[],"label_agreement":null},{"id":"W1492045964","doi":"","title":"Canada's Anti-Spam Legislation: A Constitutional Analysis","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislation; Scrutiny; Government (linguistics); Freedom of expression; The Internet; Constitutional law; Law; Political science; Business; Freedom of information; Internet privacy; Computer science; World Wide Web; Human rights","score_opus":0.004281330976392101,"score_gpt":0.19622256957164932,"score_spread":0.19194123859525722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492045964","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15356934,0.0071489015,0.0026306086,0.08269021,0.00066993217,0.0008733764,0.009934529,0.00021223756,0.74227095],"genre_scores_gemma":[0.8572982,0.005084499,0.002242415,0.03163496,0.00031423563,0.00032754394,0.0019365358,0.000077664256,0.101084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97211087,0.0010148253,0.00044429733,0.0013495861,0.01717531,0.007905171],"domain_scores_gemma":[0.97284704,0.007623263,0.001251925,0.0008241682,0.0151659455,0.0022876472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006704797,0.00063170894,0.00097492995,0.0076225917,0.023806991,0.014597339,0.0034380436,0.0055126557,0.008803746],"category_scores_gemma":[0.025867734,0.0010905624,0.0010767151,0.009404581,0.008464531,0.0022502611,0.0022353758,0.0072764116,0.00068061467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00009432046,0.00013486065,0.022250442,0.00026897754,0.000077495606,0.00068959844,0.0058484594,0.0009421523,0.00053525204,0.8269538,0.11951802,0.022686543],"study_design_scores_gemma":[0.00021671448,0.00014718431,0.18385203,0.0013171699,0.000715428,0.00036445528,0.01557939,0.004291289,0.0024755662,0.039414853,0.7512073,0.00041869687],"about_ca_topic_score_codex":0.9936358,"about_ca_topic_score_gemma":0.9958397,"teacher_disagreement_score":0.8410534,"about_ca_system_score_codex":0.15894659,"about_ca_system_score_gemma":0.29219118,"threshold_uncertainty_score":0.9755027},"labels":[],"label_agreement":null},{"id":"W1497088928","doi":"10.17159/2413-3108/2012/v0i40a841","title":"The code of silence: Revisiting South African police integrity","year":2016,"lang":"en","type":"article","venue":"South African Crime Quarterly","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Criminal Justice Association","funders":"","keywords":"Misconduct; Silence; Officer; Criminology; Seriousness; Political science; Law; Law enforcement; Psychology","score_opus":0.01841715919926395,"score_gpt":0.24831848802430626,"score_spread":0.2299013288250423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497088928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8799102,0.004408767,0.015143365,0.043789554,0.0004363999,0.00024333583,0.00017766823,0.000033382075,0.05585733],"genre_scores_gemma":[0.99511623,0.0009606005,0.0017410343,0.0008898191,0.00007191886,0.00006094334,0.000046065397,0.00002115034,0.0010921585],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97971916,0.010605226,0.0014636726,0.0009899143,0.0051962812,0.002025835],"domain_scores_gemma":[0.8831917,0.059912175,0.024955053,0.008429948,0.020691732,0.0028193805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026124302,0.00040449266,0.00048336943,0.0069233505,0.007079408,0.007567394,0.0023428202,0.0019335129,0.001977664],"category_scores_gemma":[0.14116915,0.0006939096,0.000504284,0.005634012,0.024146635,0.016420921,0.011019888,0.005162106,0.0002604905],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088220295,0.000035052875,0.09557342,0.0004685146,0.000020626383,0.00056994054,0.73958975,0.00026191736,0.0010222278,0.07406201,0.0030393756,0.08526902],"study_design_scores_gemma":[0.000009095733,0.00013596598,0.14854975,0.0026144735,0.000042032818,0.0007822608,0.7172685,0.0019399639,0.0015267391,0.041182213,0.085839756,0.00010933001],"about_ca_topic_score_codex":0.044456907,"about_ca_topic_score_gemma":0.055061962,"teacher_disagreement_score":0.044456907,"about_ca_system_score_codex":0.008809048,"about_ca_system_score_gemma":0.015103035,"threshold_uncertainty_score":0.13816017},"labels":[],"label_agreement":null},{"id":"W1505233863","doi":"10.22329/wyaj.v31i1.4309","title":"LA CYBERJUSTICE COMME RÉPONSE AUX BESOINS JURIDIQUES DES PERSONNES ITINÉRANTES: SON POTENTIEL ET SES EMBÛCHES","year":2013,"lang":"en","type":"article","venue":"Windsor Yearbook of Access to Justice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Humanities; Economic Justice; Political science; Philosophy","score_opus":0.050029427945846174,"score_gpt":0.30590958870803,"score_spread":0.2558801607621838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1505233863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8068722,0.04182732,0.0047186045,0.020722773,0.00078488345,0.00008793179,0.0005124318,0.00006183335,0.12441206],"genre_scores_gemma":[0.97240025,0.013952468,0.0010436524,0.0006830726,0.00043026058,0.000046391742,0.0001081935,0.000019825638,0.011315761],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971071,0.0015677997,0.00010625237,0.00022859163,0.0006614943,0.00032879764],"domain_scores_gemma":[0.985005,0.008296084,0.0037357165,0.0003311197,0.0017297557,0.00090228155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037028298,0.00034166648,0.00045106933,0.0024366272,0.0021772129,0.005479142,0.000720597,0.0021505277,0.010718409],"category_scores_gemma":[0.029207116,0.00029023527,0.000387942,0.0016150614,0.003869191,0.0048935884,0.0021176704,0.003909381,0.0012437067],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006214111,0.00055186567,0.3741693,0.0009407627,0.0001518839,0.005654708,0.14171554,0.00076672796,0.002396346,0.11813836,0.016787963,0.33810517],"study_design_scores_gemma":[0.000027890796,0.00040596531,0.72818214,0.0022239368,0.00010577751,0.008652387,0.117179304,0.001252652,0.0017918615,0.020326106,0.1196173,0.0002347034],"about_ca_topic_score_codex":0.017895686,"about_ca_topic_score_gemma":0.025358398,"teacher_disagreement_score":0.017895686,"about_ca_system_score_codex":0.0026182313,"about_ca_system_score_gemma":0.0018453697,"threshold_uncertainty_score":0.035856664},"labels":[],"label_agreement":null},{"id":"W1509599484","doi":"","title":"Report to the Canadian Human Rights Commission Concerning Section 13 of the Canadian Human Rights Act and the Regulation of Hate Speech on the Internet","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Human rights; Commission; Charter; Political science; International human rights law; Fundamental rights; Statutory law; Law; Linguistic rights; Government (linguistics); Reservation of rights; Right to property","score_opus":0.013082234603719066,"score_gpt":0.2255005346684778,"score_spread":0.21241830006475873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509599484","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011104371,0.00933759,0.0032521314,0.49178416,0.030851334,0.0029062396,0.01629415,0.0008346407,0.43363544],"genre_scores_gemma":[0.032934364,0.006505623,0.0053952183,0.23150165,0.0018715129,0.00069165236,0.0055717044,0.0003141135,0.7152141],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.982809,0.000677282,0.0005056223,0.0005494996,0.012603357,0.0028552175],"domain_scores_gemma":[0.9772271,0.002648861,0.0006209219,0.00044517493,0.016603757,0.0024542622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077714897,0.00079702423,0.00067151646,0.002560649,0.0145951025,0.0072827553,0.0028557845,0.01610471,0.016976768],"category_scores_gemma":[0.023411606,0.0008945069,0.0010941431,0.0027203176,0.0027038853,0.002034087,0.001999144,0.014569847,0.005315661],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001602099,0.000024988098,0.00065987464,0.000032962907,0.000005651381,0.00014066286,0.0005516215,0.00007458539,0.00025326005,0.008389465,0.98549587,0.004355127],"study_design_scores_gemma":[0.000012225685,0.000010089698,0.0031956376,0.0000924259,0.000011173945,0.000032445223,0.0010692048,0.00013131408,0.00032893696,0.0006936942,0.9943591,0.00006388585],"about_ca_topic_score_codex":0.97149503,"about_ca_topic_score_gemma":0.9771327,"teacher_disagreement_score":0.9492648,"about_ca_system_score_codex":0.050735187,"about_ca_system_score_gemma":0.24436726,"threshold_uncertainty_score":0.3681113},"labels":[],"label_agreement":null},{"id":"W1516799452","doi":"10.37974/alf.125","title":"Discussion: Should Hate Speech be allowed on the Internet? A Reply to Raphael Cohen-Almagor","year":2010,"lang":"en","type":"article","venue":"Amsterdam Law Forum","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Theme (computing); The Internet; Section (typography); Media studies; Library science; Political science; Law; Sociology; Computer science; World Wide Web","score_opus":0.019920742441279764,"score_gpt":0.2562980832976147,"score_spread":0.23637734085633497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516799452","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028233006,0.0013114124,0.00008077621,0.9926156,0.0049659577,0.0000046359405,0.000032558073,0.000010381266,0.00069637364],"genre_scores_gemma":[0.010112576,0.0026459636,0.00030569444,0.9685925,0.010474718,0.000047914145,0.0000401252,0.000050275074,0.0077302046],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9921421,0.0042183646,0.00044718335,0.0010997055,0.001585392,0.00050718593],"domain_scores_gemma":[0.91929567,0.065228984,0.002495443,0.00089740206,0.00791474,0.004167801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013639106,0.0009948967,0.0014128038,0.001596932,0.007148601,0.006125457,0.0027351193,0.032891713,0.009383805],"category_scores_gemma":[0.06640517,0.00068549137,0.0009412805,0.0017989016,0.0058282674,0.0110583855,0.0022148378,0.029566916,0.004621062],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020333377,0.000015851832,0.00034091022,0.000056082747,0.000006155958,0.00011217838,0.0012838974,0.000022724704,0.000045083827,0.0022425002,0.9909883,0.004865881],"study_design_scores_gemma":[0.00003643676,0.000038329144,0.0031632013,0.00070961926,0.000030929223,0.00056872633,0.01443065,0.0003615098,0.0002719804,0.0064140344,0.9738642,0.0001104143],"about_ca_topic_score_codex":0.01644102,"about_ca_topic_score_gemma":0.021275727,"teacher_disagreement_score":0.032891713,"about_ca_system_score_codex":0.0050507165,"about_ca_system_score_gemma":0.004658989,"threshold_uncertainty_score":0.072131336},"labels":[],"label_agreement":null},{"id":"W1523048464","doi":"","title":"Should groups in liberal democracies have special rights to limit speech that is offensive to their culture or religion","year":2008,"lang":"en","type":"dissertation","venue":"ResearchSpace (University of KwaZulu-Natal)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Offensive; Political science; Limit (mathematics); Free speech; Law; Mathematics; Mathematical analysis; Operations research","score_opus":0.03931593196172454,"score_gpt":0.26984122757586304,"score_spread":0.2305252956141385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523048464","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07027539,0.0019158694,0.003414472,0.30679542,0.002217561,0.00009237731,0.0002255394,0.000082468716,0.6149809],"genre_scores_gemma":[0.8691519,0.0011790599,0.0015550683,0.06803548,0.0017288212,0.00019443703,0.00016346826,0.00009838021,0.057893462],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948547,0.002330533,0.000111882444,0.00060020294,0.0006819877,0.0014207176],"domain_scores_gemma":[0.98588437,0.006096966,0.0026734746,0.0017888707,0.0014879232,0.0020684125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009441104,0.00029701146,0.00042244932,0.0010390967,0.007737767,0.008906272,0.0011120113,0.007729355,0.019172477],"category_scores_gemma":[0.018413916,0.00025906807,0.00038362664,0.0010462815,0.01567396,0.010934811,0.0040194932,0.0045509846,0.0044148327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003972622,0.0000732962,0.0037155426,0.00006090903,0.000010318254,0.00007096229,0.016645031,0.00006512376,0.00018230682,0.9211009,0.044917416,0.01311837],"study_design_scores_gemma":[0.00006531012,0.00008397519,0.009920236,0.0003666137,0.00001930956,0.00014338491,0.03607914,0.00023597716,0.00092516374,0.4187686,0.5333403,0.000052027135],"about_ca_topic_score_codex":0.002737731,"about_ca_topic_score_gemma":0.0042112884,"teacher_disagreement_score":0.019172477,"about_ca_system_score_codex":0.0029107234,"about_ca_system_score_gemma":0.0023213152,"threshold_uncertainty_score":0.06413835},"labels":[],"label_agreement":null},{"id":"W1525246304","doi":"10.2139/ssrn.2448480","title":"Time to Unpack the Juggernaut?: Reflections on the Canadian Federal Parliamentary Debates on 'Cyberbullying'","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Political science; Public administration; Public relations; Business","score_opus":0.012306636093455918,"score_gpt":0.23639366754093702,"score_spread":0.2240870314474811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1525246304","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024306392,0.0057561463,0.0005237873,0.91719,0.0034260398,0.000053736214,0.00030921568,0.00003977779,0.048394844],"genre_scores_gemma":[0.44862756,0.00422486,0.00092179014,0.47398707,0.0015372382,0.00012818744,0.00015601578,0.00026249455,0.07015471],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.96675104,0.005109142,0.0006712405,0.0019555425,0.009614425,0.015898667],"domain_scores_gemma":[0.95015866,0.022752624,0.0019390461,0.00086216285,0.013620222,0.010667318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027502494,0.0010225046,0.0015845998,0.004541468,0.08704559,0.032126274,0.008505205,0.040292736,0.022871716],"category_scores_gemma":[0.06396273,0.0016924508,0.0013333871,0.0075181667,0.049777463,0.010026937,0.010853259,0.044821154,0.0018076668],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016878094,0.000090368296,0.004292942,0.00033283705,0.000053659216,0.00048680676,0.21376187,0.00040878638,0.00071315066,0.25012857,0.50506824,0.024493985],"study_design_scores_gemma":[0.00006112574,0.000027102145,0.015694274,0.0007094099,0.000063321975,0.00007394974,0.20458537,0.00024058147,0.00044320402,0.014555567,0.76315945,0.0003866332],"about_ca_topic_score_codex":0.99482805,"about_ca_topic_score_gemma":0.99690515,"teacher_disagreement_score":0.21964951,"about_ca_system_score_codex":0.21964951,"about_ca_system_score_gemma":0.31285545,"threshold_uncertainty_score":0.90509593},"labels":[],"label_agreement":null},{"id":"W1531604128","doi":"10.1080/03071840701472331","title":"Radicalization on the Internet?","year":2007,"lang":"en","type":"article","venue":"The RUSI Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Radicalization; The Internet; Computer science; Political science; World Wide Web; Terrorism; Law","score_opus":0.014418801944508584,"score_gpt":0.22949820256512,"score_spread":0.2150794006206114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531604128","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008565988,0.012045165,0.0005463005,0.117208816,0.008683534,0.00004572742,0.00075715216,0.0004381845,0.85170907],"genre_scores_gemma":[0.21217042,0.03480436,0.0022203184,0.07435323,0.010654071,0.00025749303,0.0017628855,0.00073046196,0.6630468],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9993274,0.00014642416,0.00002198977,0.00008805711,0.00027228124,0.00014376649],"domain_scores_gemma":[0.9987729,0.00026169105,0.000128673,0.0001608089,0.00022489688,0.0004510031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000734875,0.00027725537,0.00021707409,0.0015074449,0.0020235463,0.00677867,0.00060853135,0.0018009852,0.18908113],"category_scores_gemma":[0.00431811,0.00011757842,0.0003039403,0.0016053176,0.0013986443,0.009391964,0.0027502757,0.0029158222,0.032604218],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002211791,0.000064258755,0.00086568337,0.00019321938,0.0000064740657,0.00014748797,0.0021739586,0.000016116785,0.00023149913,0.068566404,0.73216224,0.19555052],"study_design_scores_gemma":[0.0000043971218,0.000008365185,0.0009437834,0.00014099742,0.0000021154112,0.00009390707,0.0016148355,0.000011526618,0.00008800687,0.0055999816,0.9914868,0.0000051908924],"about_ca_topic_score_codex":0.001151405,"about_ca_topic_score_gemma":0.0031829071,"teacher_disagreement_score":0.18908113,"about_ca_system_score_codex":0.0010174129,"about_ca_system_score_gemma":0.00078186434,"threshold_uncertainty_score":0.6325394},"labels":[],"label_agreement":null},{"id":"W1547440466","doi":"10.17159/obiter.v32i2.12258","title":"HATE SPEECH ON SOCIAL NETWORK SITES: PERPETRATOR AND SERVICE PROVIDERS’ LIABILITY","year":2021,"lang":"en","type":"article","venue":"Obiter","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cyberspace; Liability; The Internet; Hatred; Service provider; Service (business); Political science; European union; Internet service provider; Law; Business; Sociology; Internet privacy; Computer science; Politics; International trade","score_opus":0.01938135336999929,"score_gpt":0.23803109722956314,"score_spread":0.21864974385956384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547440466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8828386,0.00569099,0.007534028,0.009375347,0.00015404334,0.0001808698,0.00021442185,0.000032869768,0.09397875],"genre_scores_gemma":[0.9939177,0.0018070348,0.00062787574,0.0005117818,0.000068339556,0.000033919565,0.000043213702,0.0000059133,0.002984131],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99256647,0.0037989472,0.00058494206,0.00041975008,0.0018255505,0.00080419035],"domain_scores_gemma":[0.96884656,0.017056236,0.0097514745,0.0013657126,0.0019657926,0.0010141142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005028037,0.00031755542,0.00039517303,0.003358787,0.003753443,0.0034769732,0.00052355434,0.0019221223,0.006528005],"category_scores_gemma":[0.031750433,0.00027261637,0.00027314396,0.001962119,0.0035651897,0.004635162,0.003763724,0.002141664,0.0008342309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019150415,0.00029547137,0.52388996,0.000876367,0.000094031755,0.010136955,0.17158265,0.00049475627,0.0019772067,0.115438856,0.011051251,0.16397104],"study_design_scores_gemma":[0.000017815559,0.00041828735,0.40928718,0.0038061312,0.0001855,0.046179898,0.41232768,0.0065684477,0.005814599,0.037612885,0.0776271,0.00015449278],"about_ca_topic_score_codex":0.0025863317,"about_ca_topic_score_gemma":0.0025992452,"teacher_disagreement_score":0.006528005,"about_ca_system_score_codex":0.001372929,"about_ca_system_score_gemma":0.0009919088,"threshold_uncertainty_score":0.026591122},"labels":[],"label_agreement":null},{"id":"W1551608788","doi":"10.2139/ssrn.2568611","title":"Bill C-51 Backgrounder #4: The Terrorism Propaganda Provisions","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of Toronto","funders":"","keywords":"Terrorism; Political science; Law; Criminology; Sociology","score_opus":0.01916281844190669,"score_gpt":0.23784437825169977,"score_spread":0.2186815598097931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551608788","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009927011,0.0014197964,0.002871932,0.23433027,0.013065134,0.00061122037,0.002292295,0.001221286,0.73426104],"genre_scores_gemma":[0.04908128,0.000592104,0.0017727293,0.3357411,0.005836624,0.0006399088,0.00057469605,0.00043812732,0.6053234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98662853,0.001536793,0.00063056534,0.0011080279,0.006503137,0.0035928737],"domain_scores_gemma":[0.97546595,0.013743907,0.0012339777,0.0019646941,0.0056610284,0.0019304048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008201501,0.0011321418,0.0010039596,0.002631679,0.008871213,0.009771572,0.0039148345,0.04966913,0.05187392],"category_scores_gemma":[0.05312932,0.0016554183,0.0015241643,0.0012009192,0.0030711433,0.0036957136,0.003286277,0.022397988,0.031539496],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009996946,0.00011051299,0.0007908956,0.00006243763,0.000011648151,0.00030585824,0.0005440641,0.00011347194,0.0009187306,0.1081294,0.879084,0.009828948],"study_design_scores_gemma":[0.00010173906,0.000104386905,0.0053524696,0.00045755407,0.00005210063,0.00020293907,0.0005268168,0.0005396981,0.00203534,0.017978746,0.9725256,0.00012268715],"about_ca_topic_score_codex":0.043062907,"about_ca_topic_score_gemma":0.06268346,"teacher_disagreement_score":0.9569371,"about_ca_system_score_codex":0.0059804744,"about_ca_system_score_gemma":0.011981717,"threshold_uncertainty_score":0.17353559},"labels":[],"label_agreement":null},{"id":"W1560504338","doi":"","title":"Private Regulation and Public Policy: Toward Effective Restriction of Internet Hate Propaganda","year":2004,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Political science; Accountability; Law; Public administration; Public relations; Law and economics; Sociology","score_opus":0.0066995262110539225,"score_gpt":0.22101314975359565,"score_spread":0.21431362354254171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560504338","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06949511,0.004639525,0.14956163,0.3264779,0.0011687353,0.00040418596,0.000089873116,0.0004334064,0.4477297],"genre_scores_gemma":[0.94155306,0.00188695,0.014149947,0.016440295,0.00068591075,0.00031818563,0.000032932807,0.00007071739,0.024862034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97656757,0.013246353,0.0006877129,0.0025486243,0.0046764724,0.0022733768],"domain_scores_gemma":[0.93050367,0.045788087,0.008036756,0.0070095295,0.006650532,0.0020114973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027309569,0.00066145597,0.0005894037,0.003024135,0.0054969825,0.017888252,0.0019263421,0.010280486,0.005606558],"category_scores_gemma":[0.044554926,0.00068999745,0.00075472164,0.0018053933,0.039115924,0.013137867,0.008133829,0.0099048745,0.00086549635],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001656182,0.00008826778,0.0012779586,0.00009824039,0.000012411224,0.0000746614,0.0025670037,0.0010300553,0.0004224386,0.96992403,0.0051609087,0.019327587],"study_design_scores_gemma":[0.000079039746,0.0000851934,0.0021625026,0.00071137876,0.000039330134,0.000105051025,0.003994218,0.005525747,0.002189559,0.8790068,0.106060006,0.000041070794],"about_ca_topic_score_codex":0.0056304066,"about_ca_topic_score_gemma":0.0050900658,"teacher_disagreement_score":0.027309569,"about_ca_system_score_codex":0.009379241,"about_ca_system_score_gemma":0.022160383,"threshold_uncertainty_score":0.14442849},"labels":[],"label_agreement":null},{"id":"W1562869308","doi":"","title":"Canadian Blasphemy Law in Context: Press, Legislative, and Public Reactions","year":2009,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Blasphemy; Repeal; Legislature; Political science; Context (archaeology); Law; Newspaper; House of Commons; Parliament; Politics; History","score_opus":0.014765472462252393,"score_gpt":0.22034122248271054,"score_spread":0.20557575002045814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562869308","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112443514,0.026225677,0.00044310265,0.49733388,0.0034637477,0.00009328032,0.000996982,0.00018846882,0.35881138],"genre_scores_gemma":[0.78097117,0.014538401,0.00043398162,0.089928254,0.0012580144,0.000057927024,0.0003812797,0.00017685733,0.11225414],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.99175876,0.00060485554,0.00010050887,0.0006818308,0.0036262425,0.0032278025],"domain_scores_gemma":[0.98876745,0.0026650291,0.0006894566,0.00023361493,0.0046383706,0.0030061489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003880355,0.0006655894,0.00061729393,0.0049446416,0.050122894,0.020497097,0.0028101213,0.009057964,0.017145142],"category_scores_gemma":[0.012773063,0.0008096675,0.00056787883,0.008900318,0.020106403,0.004167289,0.0046344623,0.011023407,0.0008192324],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008935964,0.00010687444,0.010083847,0.00042283005,0.00005527831,0.002200636,0.12609461,0.00038967087,0.0014439737,0.3388549,0.48037127,0.03988668],"study_design_scores_gemma":[0.000016627968,0.000019649739,0.019993775,0.00021228883,0.00003087334,0.0001447927,0.06429527,0.00016147312,0.00031537525,0.004180736,0.9104954,0.00013381895],"about_ca_topic_score_codex":0.9932227,"about_ca_topic_score_gemma":0.996293,"teacher_disagreement_score":0.23424841,"about_ca_system_score_codex":0.23424841,"about_ca_system_score_gemma":0.25041458,"threshold_uncertainty_score":0.8881633},"labels":[],"label_agreement":null},{"id":"W1574604405","doi":"10.26686/wgtn.17006713.v1","title":"The Limits of Free Speech: Democratic Legitimacy in Canada and New Zealand","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Democracy; Legitimacy; Democratic legitimacy; Free speech; Order (exchange); Law and economics; Political science; Citizen journalism; Deliberative democracy; Sociology; Law; Politics; Economics","score_opus":0.012689727810039351,"score_gpt":0.2215647601747734,"score_spread":0.20887503236473404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574604405","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7977036,0.008398169,0.001233363,0.05039271,0.00032874735,0.00015994097,0.00028293804,0.000030222971,0.14147036],"genre_scores_gemma":[0.9847371,0.0027864315,0.0005286898,0.0009835657,0.000029121788,0.0000259591,0.000053061303,0.0000209593,0.010835104],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99252456,0.0009974439,0.00024993788,0.00074406294,0.003036661,0.0024474708],"domain_scores_gemma":[0.9890382,0.0026735945,0.0013947806,0.00050881103,0.0035615675,0.0028229188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050206496,0.00038344928,0.00071071065,0.0028947708,0.019412644,0.009617134,0.0016164614,0.0017145746,0.0043740226],"category_scores_gemma":[0.015827019,0.0005344208,0.00041280538,0.004379171,0.01888242,0.0035845612,0.0048578763,0.0043126196,0.00022650757],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033298437,0.00018065308,0.084772326,0.00043809265,0.000081478305,0.0027645642,0.5244351,0.0007746594,0.0016166026,0.22151169,0.028328493,0.13476336],"study_design_scores_gemma":[0.000071453775,0.00007923798,0.30567303,0.00085607386,0.00009889495,0.00054625585,0.4521709,0.0017807399,0.00093833613,0.019049508,0.2184178,0.00031767882],"about_ca_topic_score_codex":0.9965065,"about_ca_topic_score_gemma":0.9984493,"teacher_disagreement_score":0.1408069,"about_ca_system_score_codex":0.1408069,"about_ca_system_score_gemma":0.1805623,"threshold_uncertainty_score":0.9965422},"labels":[],"label_agreement":null},{"id":"W1587480210","doi":"10.21991/c9g96p","title":"A REVIEW OF THE TAYLOR CASE: USING HUMAN RIGHTS LEGISLATION TO CURB RACIST SPEECH","year":2011,"lang":"en","type":"review","venue":"Constitutional Forum / Forum constitutionnel","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Legislation; Human rights; Political science; Law; Law and economics; Sociology","score_opus":0.06233473018195917,"score_gpt":0.3263775389393905,"score_spread":0.2640428087574313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587480210","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001452301,0.99201304,0.000119478274,0.002818838,0.00039491622,0.000013247122,0.00002460723,0.0000032216246,0.004467348],"genre_scores_gemma":[0.0024600197,0.99329835,0.00027098155,0.0025606565,0.00038130282,0.000023500763,0.00003153588,0.0000051144625,0.0009686788],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99456596,0.0018925985,0.0006863891,0.00060870295,0.001989676,0.00025663755],"domain_scores_gemma":[0.98120284,0.01359727,0.001455475,0.00030485843,0.0031772885,0.00026228308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007924431,0.0009698494,0.0021678652,0.0071328543,0.001332744,0.0028732247,0.0017759554,0.0060681077,0.004593785],"category_scores_gemma":[0.020834688,0.0008408653,0.0010526286,0.0056038606,0.0029928163,0.0036072293,0.0014536157,0.0038935149,0.00174899],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007167638,0.00012708742,0.0004640789,0.040657837,0.00015604285,0.00039703347,0.0008130082,0.00027988164,0.0004995334,0.034163725,0.18357246,0.73879755],"study_design_scores_gemma":[0.000015325237,0.00005907867,0.0015078437,0.032358594,0.00013717276,0.00044693085,0.0004039787,0.000044600198,0.00027153135,0.0025642095,0.96216446,0.000026207304],"about_ca_topic_score_codex":0.017560923,"about_ca_topic_score_gemma":0.029182736,"teacher_disagreement_score":0.017560923,"about_ca_system_score_codex":0.004739634,"about_ca_system_score_gemma":0.011141886,"threshold_uncertainty_score":0.04190892},"labels":[],"label_agreement":null},{"id":"W1588939777","doi":"10.26686/wgtn.17007385.v1","title":"Public nudity and the right to freedom of expression: Balancing competing interests","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Wellington City Council; Auckland Council","keywords":"Freedom of expression; Supreme court; Expression (computer science); Scope (computer science); Test (biology); Political science; Law; Human rights; Balancing test; Law and economics; Sociology; Computer science","score_opus":0.012832233074653407,"score_gpt":0.23201504713566143,"score_spread":0.21918281406100804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588939777","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0708212,0.0014775319,0.05111277,0.041177176,0.00018014447,0.00019524943,0.00005072377,0.000027838953,0.83495736],"genre_scores_gemma":[0.9796601,0.0003905425,0.004212639,0.002461255,0.00009540001,0.00018662763,0.000018545974,0.000018122784,0.012956639],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9653197,0.01899245,0.001083555,0.00304338,0.0074923644,0.0040685856],"domain_scores_gemma":[0.96392184,0.02643465,0.0022665344,0.00376271,0.0023731757,0.0012410058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027993478,0.00057177,0.0010545724,0.0014324997,0.008618077,0.016673623,0.0026185876,0.008489341,0.007988331],"category_scores_gemma":[0.046747133,0.00050838466,0.0007336847,0.0012178818,0.07848932,0.014706087,0.010420015,0.009667591,0.0006505173],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007957903,0.000008854058,0.00031299837,0.0000118253965,0.0000024513506,0.0000541848,0.0022845713,0.0001151897,0.000053734067,0.9938426,0.00029660072,0.0030089938],"study_design_scores_gemma":[0.000026681664,0.000038405367,0.001396967,0.00026288294,0.000015132196,0.00015796465,0.0069383057,0.0010268154,0.0005454221,0.9567967,0.03275064,0.000043937296],"about_ca_topic_score_codex":0.020132504,"about_ca_topic_score_gemma":0.024848133,"teacher_disagreement_score":0.027993478,"about_ca_system_score_codex":0.013606045,"about_ca_system_score_gemma":0.014305482,"threshold_uncertainty_score":0.14804548},"labels":[],"label_agreement":null},{"id":"W1602149093","doi":"","title":"Médias, technologies et droits humains : notions concurrentes. Cas canadiens et internationaux","year":2012,"lang":"fr","type":"article","venue":"R-libre (Université Téluq)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Humanities; Political science; Human rights; Freedom of expression; Sociology; Expression (computer science); Law; Art; Computer science","score_opus":0.035194216190896556,"score_gpt":0.26366234515586673,"score_spread":0.22846812896497018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602149093","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14652006,0.10577912,0.01942412,0.04339285,0.0021827829,0.00011647148,0.0002620516,0.00016118809,0.6821614],"genre_scores_gemma":[0.94148004,0.027260719,0.0029977548,0.0012304351,0.0008854145,0.00012433817,0.000060690523,0.000065710716,0.025894903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962729,0.0018204987,0.00021640275,0.00044555624,0.0008218223,0.0004227582],"domain_scores_gemma":[0.9866747,0.008890875,0.002059025,0.0006614312,0.0011343008,0.0005795511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036089693,0.00089078385,0.0004977598,0.0076254127,0.006713193,0.01803309,0.00086917105,0.0035323293,0.009145051],"category_scores_gemma":[0.011358389,0.0006245328,0.00046830418,0.006823435,0.031143585,0.0148698855,0.0048791156,0.006213255,0.0008994344],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024332403,0.000020139976,0.0030160572,0.00019081018,0.000009722391,0.00032080815,0.059471924,0.000105099694,0.0002484311,0.9174814,0.0013080848,0.017803192],"study_design_scores_gemma":[0.000022941203,0.00006969418,0.015013387,0.0015645213,0.000048529302,0.002938959,0.18875657,0.0005639436,0.0011119582,0.15370731,0.6360809,0.00012122837],"about_ca_topic_score_codex":0.02821898,"about_ca_topic_score_gemma":0.025553983,"teacher_disagreement_score":0.971781,"about_ca_system_score_codex":0.007746178,"about_ca_system_score_gemma":0.004076963,"threshold_uncertainty_score":0.05620271},"labels":[],"label_agreement":null},{"id":"W1605145486","doi":"10.60082/2563-8505.1271","title":"Hate Speech and the Reasonable Supreme Court of Canada","year":2013,"lang":"en","type":"article","venue":"Supreme Court law review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Constitutionality; Supreme court; Law; Hatred; Human rights; Political science; Legislation; Commission; Harm; Fundamental rights; Tribunal; Sociology; Politics","score_opus":0.011323929695200085,"score_gpt":0.215569236149523,"score_spread":0.20424530645432293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605145486","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12221685,0.017916039,0.008361956,0.26372075,0.0025500336,0.0005741426,0.0012933101,0.00021083311,0.5831561],"genre_scores_gemma":[0.8397895,0.0030581348,0.0028708004,0.08440157,0.00032916386,0.0002138004,0.00025137796,0.000057549634,0.069028184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9795196,0.0033610202,0.00096144906,0.0026729917,0.0077768904,0.0057080532],"domain_scores_gemma":[0.9683994,0.0150331585,0.0012711759,0.0011397486,0.011897493,0.0022590582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0154829975,0.00046473878,0.0008598591,0.0020544727,0.02671826,0.017371757,0.0032690258,0.013862363,0.0046744864],"category_scores_gemma":[0.047885608,0.0012216024,0.00092362356,0.002333184,0.013268599,0.0038037316,0.0034854559,0.016310005,0.0004437543],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042372292,0.00001861333,0.002153219,0.00009051567,0.000037917525,0.0006208197,0.004785798,0.00047213098,0.0005105396,0.94329816,0.040741887,0.007228004],"study_design_scores_gemma":[0.00022108619,0.00010221074,0.02090475,0.0014463321,0.0003413438,0.00033494437,0.01097798,0.0038009938,0.0027999626,0.25084946,0.7075268,0.00069416815],"about_ca_topic_score_codex":0.9641689,"about_ca_topic_score_gemma":0.9745054,"teacher_disagreement_score":0.09060945,"about_ca_system_score_codex":0.09060945,"about_ca_system_score_gemma":0.18117477,"threshold_uncertainty_score":0.65742064},"labels":[],"label_agreement":null},{"id":"W1726412375","doi":"10.47678/cjhe.v36i1.183526","title":"The Charge of Thought-Policing in Universities","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Higher Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Enforcement; Left-wing politics; False accusation; Politics; Expression (computer science); Law; Human rights; Political science; Criticism; Sociology; Law and economics","score_opus":0.005526231220567111,"score_gpt":0.21835524091310557,"score_spread":0.21282900969253846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1726412375","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25494203,0.010561328,0.018777937,0.25875175,0.0026297069,0.000093264054,0.00004055835,0.00041743318,0.4537861],"genre_scores_gemma":[0.9881689,0.0005247076,0.00070389395,0.002527685,0.00026556422,0.000014204864,0.0000037554237,0.00001973467,0.007771633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96696067,0.01749257,0.0011063591,0.0016064465,0.0059358175,0.00689815],"domain_scores_gemma":[0.9579867,0.018713433,0.008527305,0.0034754076,0.00470874,0.006588473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01418683,0.00041008415,0.00065958063,0.0019364551,0.03144133,0.025308682,0.0017378927,0.009192243,0.0038435003],"category_scores_gemma":[0.04117071,0.0006690319,0.00052857585,0.0022365905,0.07788925,0.008445203,0.009043663,0.008176997,0.00067887333],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006563723,0.000041575277,0.005889772,0.00006393644,0.000008025657,0.00034191538,0.09348654,0.00047989565,0.0002529878,0.85951257,0.01140956,0.028447527],"study_design_scores_gemma":[0.000066167515,0.00016654289,0.013841518,0.0007898861,0.000051533178,0.000754247,0.16259684,0.0028141784,0.0033713134,0.46060586,0.3547609,0.00018107808],"about_ca_topic_score_codex":0.09709314,"about_ca_topic_score_gemma":0.07617902,"teacher_disagreement_score":0.9639559,"about_ca_system_score_codex":0.03604411,"about_ca_system_score_gemma":0.03705753,"threshold_uncertainty_score":0.2615195},"labels":[],"label_agreement":null},{"id":"W1734963588","doi":"","title":"What is the Purpose of Freedom of Expression","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Charter; Dignity; Expression (computer science); Freedom of expression; Realm; Law; Supreme court; Freedom of thought; Democracy; Political science; Law and economics; Realization (probability); Government (linguistics); Relation (database); Human rights; Sociology; Philosophy; Politics; Computer science; Mathematics","score_opus":0.006001828542725032,"score_gpt":0.22329722848852063,"score_spread":0.2172953999457956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1734963588","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01459939,0.009919336,0.036675904,0.15600441,0.0022254512,0.00011128407,0.00021336172,0.00016363908,0.7800873],"genre_scores_gemma":[0.92000294,0.0072436114,0.010318411,0.018058851,0.001774728,0.00022521171,0.00015850036,0.0003177588,0.04189999],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9697156,0.015901262,0.0010965123,0.0022861955,0.007272731,0.003727795],"domain_scores_gemma":[0.972517,0.014113407,0.0017606672,0.0047429358,0.0050023347,0.0018637062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021771858,0.0005593717,0.0011017623,0.0021424885,0.0087173125,0.02454179,0.0017503101,0.005893725,0.0064988085],"category_scores_gemma":[0.030483888,0.00044761313,0.0009161819,0.0018239635,0.081041895,0.02642743,0.005354992,0.007234082,0.0026717111],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007670311,0.0000081883345,0.0003041165,0.00004650493,0.0000047032277,0.000030771556,0.004077864,0.000048986847,0.00008915754,0.98370725,0.0039346293,0.007740212],"study_design_scores_gemma":[0.00001441265,0.00001502892,0.0012709243,0.00070083863,0.000013004437,0.00016077055,0.007302736,0.00017372388,0.00045224692,0.77292544,0.21692769,0.00004316031],"about_ca_topic_score_codex":0.014158356,"about_ca_topic_score_gemma":0.010714709,"teacher_disagreement_score":0.02454179,"about_ca_system_score_codex":0.008132182,"about_ca_system_score_gemma":0.014041436,"threshold_uncertainty_score":0.11514199},"labels":[],"label_agreement":null},{"id":"W1816871147","doi":"","title":"Negotiating the Contours of Unlawful Hate Speech: Regulation Under Provincial Human Rights Law in Canada","year":2005,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Law; Legislature; Statute; Free speech; Political science; Human rights; Negotiation","score_opus":0.009458603697093015,"score_gpt":0.21068838383234528,"score_spread":0.20122978013525228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1816871147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.691163,0.003455523,0.008208604,0.038048763,0.00021479424,0.00020763924,0.00037366652,0.00013975301,0.25818813],"genre_scores_gemma":[0.9840469,0.0006250107,0.001050789,0.0009975515,0.000011815076,0.000018349248,0.00004008865,0.000015094788,0.013194367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9943743,0.0008327412,0.00013986994,0.00043389152,0.0021566143,0.0020626371],"domain_scores_gemma":[0.98827386,0.002550689,0.0009838253,0.0003389815,0.0053724158,0.0024802217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003686471,0.00021520903,0.0002759529,0.0019037294,0.020898636,0.008443235,0.0015685166,0.0014773366,0.0036072123],"category_scores_gemma":[0.011792037,0.0004439472,0.00032535146,0.0022583671,0.010731861,0.001546454,0.0032369122,0.0024622187,0.00019373634],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000243587,0.00009682431,0.10959892,0.00030872185,0.00008450009,0.0011717363,0.22241318,0.004590135,0.0055251713,0.49715808,0.036669835,0.12213937],"study_design_scores_gemma":[0.00007441792,0.00009292903,0.277296,0.00069268054,0.00017797822,0.00039016342,0.21534584,0.00949512,0.0048397495,0.04531897,0.44584957,0.00042657397],"about_ca_topic_score_codex":0.9963464,"about_ca_topic_score_gemma":0.9985588,"teacher_disagreement_score":0.19824696,"about_ca_system_score_codex":0.19824696,"about_ca_system_score_gemma":0.23858824,"threshold_uncertainty_score":0.9299199},"labels":[],"label_agreement":null},{"id":"W1823790170","doi":"10.1007/978-3-319-06483-3_25","title":"Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Twente; York University","keywords":"Computer science; Machine learning; Artificial intelligence; Classifier (UML)","score_opus":0.010636258748427573,"score_gpt":0.21431803929404786,"score_spread":0.20368178054562028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1823790170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38785228,0.002836133,0.5686354,0.0017548831,0.00030415977,0.00048470075,0.0017754049,0.0023052078,0.03405188],"genre_scores_gemma":[0.83233654,0.0005130515,0.14770785,0.00041080225,0.000153656,0.00013512911,0.001005992,0.00008582622,0.01765116],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991543,0.00021025489,0.000027032725,0.0002649608,0.00022464401,0.00011880806],"domain_scores_gemma":[0.9986333,0.00082843495,0.000087812274,0.00009768534,0.00024795215,0.00010465326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001165959,0.0009574075,0.00085084495,0.0029450115,0.00060570583,0.0018404557,0.0014356285,0.0019716253,0.004204154],"category_scores_gemma":[0.0025386733,0.00038420912,0.0007075685,0.0012424742,0.0004307909,0.002127685,0.0011137893,0.001018778,0.0013375373],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001069442,0.0015550306,0.08085983,0.00032149602,0.0006935181,0.000587333,0.0014912115,0.035874918,0.018823769,0.009009179,0.017822785,0.83189136],"study_design_scores_gemma":[0.00005467987,0.0006166637,0.05300445,0.00008591545,0.00034652848,0.0007904855,0.0017135584,0.902484,0.008771688,0.02418065,0.007839218,0.00011214648],"about_ca_topic_score_codex":0.0067998758,"about_ca_topic_score_gemma":0.011727772,"teacher_disagreement_score":0.0067998758,"about_ca_system_score_codex":0.00063452445,"about_ca_system_score_gemma":0.000575679,"threshold_uncertainty_score":0.014064312},"labels":[],"label_agreement":null},{"id":"W1832562446","doi":"10.47678/cjhe.v31i2.183392","title":"Stanley Fish's Case for Speech Regulation: A Critique","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Higher Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Impossibility; Argument (complex analysis); Free speech; Fish <Actinopterygii>; Indirect speech; Speech act; Law and economics; Linguistics; Psychology; Sociology; Political science; Law; Philosophy; Biology; Fishery","score_opus":0.01700345737689694,"score_gpt":0.2785156482506046,"score_spread":0.26151219087370764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1832562446","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036485414,0.0057772957,0.008476992,0.85691094,0.0018151088,0.000023348617,0.000045321172,0.000062252526,0.12324018],"genre_scores_gemma":[0.323139,0.0075952834,0.005060669,0.5969185,0.005961931,0.00033314968,0.00005169471,0.00026633628,0.060673416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9886857,0.004236356,0.00035422773,0.002047696,0.003715241,0.0009608122],"domain_scores_gemma":[0.97528255,0.019798122,0.0006790596,0.0009252303,0.0025633296,0.0007517035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014643144,0.0007614982,0.0013362514,0.0020487313,0.009756883,0.007053475,0.0039247153,0.025478939,0.007928443],"category_scores_gemma":[0.028392777,0.0006290738,0.001078971,0.0015731443,0.07198771,0.011449227,0.006542612,0.026777383,0.0017637626],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010161489,0.0000063953385,0.00011117514,0.000041405867,0.00000436497,0.00008399434,0.0040929955,0.00013460919,0.00004627081,0.94593334,0.045125343,0.0044098687],"study_design_scores_gemma":[0.000034415032,0.000016364585,0.00026599847,0.0002917438,0.000009263484,0.00013308255,0.0024409222,0.00026667363,0.00019420024,0.6933199,0.30299562,0.00003184607],"about_ca_topic_score_codex":0.047697823,"about_ca_topic_score_gemma":0.031107297,"teacher_disagreement_score":0.047697823,"about_ca_system_score_codex":0.010584127,"about_ca_system_score_gemma":0.010778601,"threshold_uncertainty_score":0.09484035},"labels":[],"label_agreement":null},{"id":"W1872706764","doi":"","title":"Beyond Irwin Toy: A New Approach to Freedom of Expression Under the Charter","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Supreme court; Jurisprudence; Law; Political science; Adjudication; Scrutiny; Charter; Law and economics; Sociology","score_opus":0.011952735129688752,"score_gpt":0.2274451726208317,"score_spread":0.21549243749114294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1872706764","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008766422,0.0059376173,0.12776361,0.0822773,0.0012180121,0.00021372283,0.0001294239,0.00014942029,0.7735445],"genre_scores_gemma":[0.733541,0.0045834635,0.061493397,0.023778457,0.0016663849,0.00060502294,0.00013541071,0.00031297456,0.17388384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97982496,0.008709001,0.0005035283,0.0025716715,0.0055823196,0.0028084312],"domain_scores_gemma":[0.9935095,0.0031639535,0.0003035358,0.001057019,0.0014372044,0.0005287391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015532262,0.0010801197,0.0012718557,0.003931695,0.015522449,0.025426758,0.0041618706,0.01060597,0.0055089816],"category_scores_gemma":[0.014431078,0.0007431184,0.001610352,0.0027657,0.075349025,0.01708266,0.009779922,0.018295923,0.00094904215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002811654,0.0000020901455,0.000021132482,0.00000419211,6.6699e-7,0.00001741063,0.00093933335,0.00007950869,0.00001902688,0.9966228,0.0010276324,0.0012633563],"study_design_scores_gemma":[0.000009765259,0.000011113408,0.000101339814,0.000084045714,0.000008005221,0.00003648557,0.0008749216,0.000727677,0.000103951395,0.928635,0.069382444,0.000025265826],"about_ca_topic_score_codex":0.17361994,"about_ca_topic_score_gemma":0.21501352,"teacher_disagreement_score":0.17361994,"about_ca_system_score_codex":0.03743203,"about_ca_system_score_gemma":0.043885585,"threshold_uncertainty_score":0.34521872},"labels":[],"label_agreement":null},{"id":"W1875271486","doi":"10.60082/2563-8505.1272","title":"Reconciling Rights: The Whatcott Case as Missed Opportunity","year":2013,"lang":"en","type":"article","venue":"Supreme Court law review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Supreme court; Law; Charter; Hatred; Jurisprudence; Human rights; Political science; Fundamental rights; Constitutional right; Commission; Expression (computer science); Sociology","score_opus":0.04051935539651571,"score_gpt":0.2677244446295087,"score_spread":0.22720508923299296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1875271486","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18517172,0.008549423,0.007863822,0.35670754,0.002615587,0.00025193047,0.00019474211,0.00008502478,0.43856013],"genre_scores_gemma":[0.8526963,0.0020547255,0.0013183379,0.10531097,0.00035215757,0.00007038472,0.000033039905,0.000038601087,0.03812537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98180455,0.0030273027,0.00032622204,0.0019172761,0.0057660113,0.0071587195],"domain_scores_gemma":[0.9919595,0.0040404797,0.00046808357,0.00038441463,0.0015643104,0.0015831892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00938768,0.00051966705,0.0008349292,0.00203552,0.033097617,0.014733071,0.0028975853,0.022007547,0.0033160485],"category_scores_gemma":[0.01873745,0.00081344607,0.00085144915,0.0021879328,0.032708704,0.006835852,0.006542726,0.021234302,0.00026674249],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032662658,0.000040805942,0.0025282688,0.000052424653,0.000022574588,0.006064464,0.026627768,0.00022671901,0.00044734977,0.910803,0.03997042,0.013183536],"study_design_scores_gemma":[0.00017963821,0.00011593869,0.010535856,0.0013240266,0.0001906512,0.003944358,0.08499679,0.0019687475,0.0023579693,0.25998625,0.6340243,0.00037542035],"about_ca_topic_score_codex":0.8757566,"about_ca_topic_score_gemma":0.9405855,"teacher_disagreement_score":0.8757566,"about_ca_system_score_codex":0.049246844,"about_ca_system_score_gemma":0.074100405,"threshold_uncertainty_score":0.35731256},"labels":[],"label_agreement":null},{"id":"W1977706228","doi":"10.1177/0734016808319174","title":"Book Review: Welch, M. (2006). Scapegoats of September 11th: Hate Crimes &amp; State Crimes in the War on Terror. New Brunswick, NJ: Rutgers University Press. 222 pp","year":2008,"lang":"en","type":"article","venue":"Criminal Justice Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criminology; State (computer science); Political science; Law; Sociology; Computer science","score_opus":0.038105910856513124,"score_gpt":0.2725925796038508,"score_spread":0.23448666874733767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977706228","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011027442,0.9427453,0.0003582964,0.015885213,0.020410975,0.00008432149,0.0013081725,0.00012890837,0.01896849],"genre_scores_gemma":[0.0013420031,0.905737,0.00062583067,0.011947338,0.010819739,0.00011381979,0.0015927079,0.000078812394,0.06774282],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916804,0.00008955936,0.000088648085,0.00011093688,0.0004566549,0.000086245855],"domain_scores_gemma":[0.99529403,0.001428505,0.0004164792,0.00009363457,0.0024329987,0.00033432269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010603322,0.0020467776,0.0027318306,0.0063981772,0.00083205255,0.0026616026,0.0028780743,0.0028465213,0.061998535],"category_scores_gemma":[0.0054563317,0.00085551053,0.00093016314,0.008902919,0.0009121699,0.002866409,0.0012798748,0.0040745935,0.07942479],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015121999,0.000006986733,0.000044816716,0.0010903499,0.00001029844,0.000021908303,0.000017625187,0.000022252463,0.000028977656,0.00016743006,0.94089484,0.057679404],"study_design_scores_gemma":[0.000015593143,0.000014134628,0.0005267225,0.002415588,0.000029127215,0.00023523095,0.000028250135,0.00001217942,0.00003612007,0.00026725157,0.9964079,0.000011807851],"about_ca_topic_score_codex":0.016873945,"about_ca_topic_score_gemma":0.037641473,"teacher_disagreement_score":0.061998535,"about_ca_system_score_codex":0.0022303541,"about_ca_system_score_gemma":0.0041318936,"threshold_uncertainty_score":0.20740575},"labels":[],"label_agreement":null},{"id":"W1990944375","doi":"10.7202/1025142ar","title":"“Sexualized Online Bullying” Through an Equality Lens: Missed Opportunity in AB v. Bragg?","year":2014,"lang":"en","type":"article","venue":"McGill Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"UNICEF","keywords":"Framing (construction); Interim; Supreme court; Political science; Law; Psychology; Engineering","score_opus":0.08735101159838843,"score_gpt":0.3089600770320802,"score_spread":0.2216090654336918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990944375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41483966,0.003654176,0.004622879,0.3707095,0.001100778,0.00012171808,0.0001003897,0.000036833557,0.20481408],"genre_scores_gemma":[0.92669624,0.0008333578,0.00050199206,0.059883844,0.00022250092,0.000046961955,0.000015547257,0.00001900959,0.011780569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98975194,0.0040050806,0.0002591989,0.0008597673,0.0018353442,0.0032886304],"domain_scores_gemma":[0.9948,0.0028542904,0.00043064562,0.00021954758,0.00066705234,0.0010284999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066512465,0.00032596616,0.0005073009,0.0010618829,0.028627977,0.0107265115,0.0021130654,0.012196849,0.0021712563],"category_scores_gemma":[0.012955037,0.0004877778,0.00045947975,0.00083239353,0.024239585,0.0081896605,0.0068538226,0.019309713,0.00018155202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038811184,0.000113572656,0.010786994,0.000055878405,0.000016464655,0.0043468,0.34706938,0.00007649934,0.0005157291,0.5954731,0.025100566,0.01640619],"study_design_scores_gemma":[0.00004983481,0.00012608407,0.015738877,0.0009279928,0.000103865554,0.003594263,0.61592925,0.00076850114,0.0024593796,0.12755173,0.23256105,0.00018923398],"about_ca_topic_score_codex":0.42800742,"about_ca_topic_score_gemma":0.5507614,"teacher_disagreement_score":0.5719926,"about_ca_system_score_codex":0.011528552,"about_ca_system_score_gemma":0.01444699,"threshold_uncertainty_score":0.85103226},"labels":[],"label_agreement":null},{"id":"W1996706811","doi":"10.7202/1013032ar","title":"“Dignitizing” Free Speech in Israel: The Impact of the Constitutional Revolution on Free Speech Protection","year":2012,"lang":"en","type":"article","venue":"McGill Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dignity; Supreme court; Free speech; Law; Political science; Bill of rights; Human rights","score_opus":0.027358065569301683,"score_gpt":0.2566332219396279,"score_spread":0.22927515637032622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996706811","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36675417,0.016266035,0.022576787,0.07466701,0.00083322835,0.000098266486,0.00019125217,0.0000828955,0.5185303],"genre_scores_gemma":[0.987112,0.0016972733,0.0012156068,0.0043217735,0.00038395124,0.000031858995,0.000047434918,0.000017262513,0.0051728804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9935707,0.0027188337,0.00021750099,0.0005782394,0.0020538422,0.0008609262],"domain_scores_gemma":[0.98280615,0.013656321,0.0010209302,0.00084868446,0.0012285151,0.0004394022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012248709,0.00026540103,0.00037518548,0.0021264802,0.003267479,0.008169958,0.001037653,0.00288466,0.004301293],"category_scores_gemma":[0.021391336,0.00020573869,0.00033987904,0.0011391606,0.027024845,0.0058208373,0.0035724775,0.004338687,0.0003478656],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038855665,0.000031619875,0.0015958505,0.000057822712,0.000010615297,0.00014375,0.005805138,0.000669462,0.0004594353,0.95160604,0.0018640239,0.037717372],"study_design_scores_gemma":[0.000073974836,0.00018934759,0.017022967,0.000950437,0.000051346386,0.00034497294,0.014224663,0.0034343407,0.003912226,0.7712426,0.18843456,0.00011855647],"about_ca_topic_score_codex":0.011593941,"about_ca_topic_score_gemma":0.01298482,"teacher_disagreement_score":0.012248709,"about_ca_system_score_codex":0.008656299,"about_ca_system_score_gemma":0.00591008,"threshold_uncertainty_score":0.06477815},"labels":[],"label_agreement":null},{"id":"W1997654733","doi":"10.5210/fm.v16i8.3498","title":"“Don’t feed the troll”: Shutting down debate about community expectations on Reddit.com","year":2011,"lang":"en","type":"article","venue":"First Monday","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Affordance; Order (exchange); Online community; Identity (music); Representation (politics); Internet privacy; Political science; Advertising; Computer science; Business; World Wide Web; Law; Politics","score_opus":0.02772003969741437,"score_gpt":0.22428629515153084,"score_spread":0.19656625545411646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997654733","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6780071,0.00046709238,0.024331652,0.09872438,0.0007234923,0.0002576431,0.0000890542,0.000415971,0.19698358],"genre_scores_gemma":[0.9770323,0.00009887527,0.0026316936,0.009426665,0.000119801894,0.000086052896,0.00002856393,0.0001036892,0.010472328],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9792809,0.014369896,0.0005270979,0.0013980785,0.0028363017,0.0015877349],"domain_scores_gemma":[0.9500376,0.035971094,0.0039512534,0.0035103918,0.004286226,0.00224345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02284873,0.00044607482,0.00035372277,0.0016452466,0.017361838,0.01136442,0.0013812161,0.007581542,0.0038677263],"category_scores_gemma":[0.06765241,0.0005426249,0.0002966775,0.0010309784,0.016492058,0.012486559,0.005697133,0.010637039,0.0009041146],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018941452,0.00017023514,0.015606891,0.00014555652,0.000019931022,0.0022840705,0.6252875,0.00021841028,0.0028982162,0.2695453,0.03726564,0.046368826],"study_design_scores_gemma":[0.00007825603,0.00024285758,0.017640406,0.00062889,0.000055369303,0.0017661804,0.5488093,0.005365812,0.010786066,0.055403005,0.35900703,0.0002168565],"about_ca_topic_score_codex":0.01608,"about_ca_topic_score_gemma":0.022815332,"teacher_disagreement_score":0.02284873,"about_ca_system_score_codex":0.005603257,"about_ca_system_score_gemma":0.002624124,"threshold_uncertainty_score":0.12083703},"labels":[],"label_agreement":null},{"id":"W2007254975","doi":"10.1177/1532708611423207","title":"Constructing the Student Culprit","year":2011,"lang":"en","type":"article","venue":"Culture Studies &#x2194 Critical Methodologies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia","keywords":"Culprit; Framing (construction); Deviance (statistics); Cheating; Psychology; Social psychology; Computer science; History","score_opus":0.1961798802841987,"score_gpt":0.4192586913859749,"score_spread":0.2230788111017762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007254975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5018729,0.0015053585,0.24730998,0.017579075,0.0005789296,0.00048818733,0.00026354525,0.00055269885,0.22984928],"genre_scores_gemma":[0.97093695,0.0003362334,0.017739138,0.00051265705,0.00005042633,0.00013227161,0.000047943355,0.00010219111,0.010142143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9925208,0.0044094003,0.00021768389,0.0012311287,0.0011325827,0.0004884728],"domain_scores_gemma":[0.9839019,0.009502803,0.0014970317,0.0016416685,0.002356597,0.0011000461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008341839,0.00079054135,0.00043365714,0.0047071213,0.005191333,0.0098654,0.0015783198,0.0018979893,0.007320647],"category_scores_gemma":[0.022493321,0.0005521912,0.00031130778,0.0013128357,0.019699177,0.008416925,0.007579486,0.0036689478,0.0011514237],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049578055,0.00014060215,0.023935208,0.00020990177,0.00002035244,0.0010722547,0.36757234,0.00044614144,0.0029980182,0.5066194,0.0041021355,0.092834085],"study_design_scores_gemma":[0.000027549067,0.00022884145,0.018858032,0.0010255404,0.000052129635,0.0013425462,0.4663567,0.006318723,0.013114729,0.25191745,0.24066047,0.000097354954],"about_ca_topic_score_codex":0.0024613908,"about_ca_topic_score_gemma":0.002647271,"teacher_disagreement_score":0.0098654,"about_ca_system_score_codex":0.004302645,"about_ca_system_score_gemma":0.0033138823,"threshold_uncertainty_score":0.044116378},"labels":[],"label_agreement":null},{"id":"W2010008752","doi":"10.1350/clwr.2012.41.2.0236","title":"Racial Vilification and Freedom of Speech in Australia and Elsewhere","year":2012,"lang":"en","type":"article","venue":"Common Law World Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Offensive; Law; Charter; Legislature; Context (archaeology); Political science; Politics; Government (linguistics); Democracy; Legislation; Freedom of information; Sociology; Law and economics; History; Linguistics; Economics","score_opus":0.03108574647807879,"score_gpt":0.30010409840855523,"score_spread":0.26901835193047646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010008752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6295615,0.021500846,0.0023456002,0.022087099,0.00036984475,0.000079444326,0.000058533344,0.000021228161,0.3239759],"genre_scores_gemma":[0.9835308,0.0029371763,0.0003399788,0.0023288783,0.00010858032,0.000025834717,0.0000133595,0.0000058012565,0.010709572],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944871,0.0025131456,0.00025214793,0.0005614949,0.001265522,0.0009205644],"domain_scores_gemma":[0.9914637,0.0039154286,0.0016909466,0.00045993875,0.0016134826,0.0008564533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004735106,0.00013254405,0.0004545847,0.0019165812,0.0048417253,0.0038179834,0.0005894265,0.0014696409,0.003245999],"category_scores_gemma":[0.014027171,0.00019135319,0.00022255593,0.0013043098,0.008354297,0.002676476,0.003884818,0.002455393,0.0002549459],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012613375,0.00019158226,0.04331172,0.00059556914,0.000098004,0.0014477775,0.1287874,0.00091444515,0.0021815887,0.60196036,0.008142732,0.21224268],"study_design_scores_gemma":[0.000040363586,0.00043224037,0.45187756,0.0017564632,0.00020469801,0.0008657005,0.042774204,0.0014499013,0.0024854576,0.10786025,0.39011365,0.00013946176],"about_ca_topic_score_codex":0.104242705,"about_ca_topic_score_gemma":0.16596937,"teacher_disagreement_score":0.104242705,"about_ca_system_score_codex":0.0047347085,"about_ca_system_score_gemma":0.0071925856,"threshold_uncertainty_score":0.20727193},"labels":[],"label_agreement":null},{"id":"W2024612190","doi":"10.1350/clwr.2010.39.1.0195","title":"Speech, Equality, and Citizenship in Canada","year":2010,"lang":"en","type":"article","venue":"Common Law World Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Hatred; Xenophobia; Human rights; Racism; Law; Incitement; Terrorism; Sociology; Sedition; Criticism; Political science; Criminology; Citizenship; Politics","score_opus":0.019617307612958202,"score_gpt":0.25944653486200536,"score_spread":0.23982922724904715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024612190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54395366,0.0056020073,0.0003270077,0.08831483,0.00092449307,0.00011397586,0.0018721101,0.000071258735,0.35882065],"genre_scores_gemma":[0.892472,0.001563189,0.00023641081,0.0040036933,0.000064017615,0.000026052488,0.00033032973,0.00003043986,0.10127374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970049,0.00021755185,0.000058990296,0.00022551804,0.000660028,0.0018330178],"domain_scores_gemma":[0.9957372,0.00032637906,0.00012883238,0.00007060409,0.0010850632,0.0026519229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012432052,0.0001850501,0.00054819207,0.0013861648,0.028642902,0.008378342,0.0016513513,0.0015943227,0.021516286],"category_scores_gemma":[0.0036053052,0.00018483055,0.0003512626,0.0030280552,0.0053117773,0.0019287677,0.0045932685,0.0030013851,0.0007118104],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021524784,0.0003948571,0.118458085,0.00023751715,0.000063409614,0.0017390223,0.12118196,0.0006859589,0.00045784336,0.44715026,0.1698212,0.13959463],"study_design_scores_gemma":[0.000052592153,0.00007914362,0.19246408,0.00038249628,0.000052899755,0.0002561042,0.18181913,0.0009939342,0.0004755646,0.014025254,0.60925186,0.00014699239],"about_ca_topic_score_codex":0.99719775,"about_ca_topic_score_gemma":0.9987697,"teacher_disagreement_score":0.14985372,"about_ca_system_score_codex":0.14985372,"about_ca_system_score_gemma":0.23640776,"threshold_uncertainty_score":0.9860492},"labels":[],"label_agreement":null},{"id":"W2025181783","doi":"10.3138/cjccj.2013.f03","title":"Mesure de la complexité de lecture des formulaires de déclaration et de renonciation aux droits pour adolescents québécois","year":2014,"lang":"fr","type":"article","venue":"Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.06842774135723736,"score_gpt":0.30517289711995454,"score_spread":0.23674515576271718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025181783","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99237716,0.00026222738,0.0012774268,0.00055634533,0.0000334191,0.00013432943,0.0002619262,0.000045925593,0.0050512785],"genre_scores_gemma":[0.9832435,0.00033065968,0.0037249376,0.00015595203,0.0000150088,0.00020275489,0.00036486486,0.000022999899,0.011939278],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947404,0.0019870498,0.00032870303,0.00043171755,0.0020769243,0.00043512552],"domain_scores_gemma":[0.9538154,0.022387214,0.004348669,0.0010926945,0.016647585,0.0017083882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008568002,0.0004151254,0.0004549302,0.0015229348,0.0015692838,0.0023943274,0.0008464229,0.00072244176,0.007508243],"category_scores_gemma":[0.039522268,0.00034907967,0.0003758248,0.0009953561,0.0011357972,0.00090948446,0.0013114542,0.0013458533,0.00060565677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001072097,0.0005259391,0.6134991,0.0009956216,0.00019634663,0.00078119355,0.12462622,0.0030917365,0.011680011,0.002481312,0.007097298,0.23395312],"study_design_scores_gemma":[0.00004082874,0.00056917267,0.9201919,0.0004309846,0.00015826961,0.00022098557,0.04703175,0.0041886573,0.0058068624,0.00042644312,0.020805582,0.00012857512],"about_ca_topic_score_codex":0.34923708,"about_ca_topic_score_gemma":0.5537084,"teacher_disagreement_score":0.6507629,"about_ca_system_score_codex":0.011730628,"about_ca_system_score_gemma":0.007597649,"threshold_uncertainty_score":0.6944086},"labels":[],"label_agreement":null},{"id":"W2038241675","doi":"10.1163/15718110020907936","title":"Canadians Discuss Freedom of Speech: Individual Rights Versus Group Protection","year":2000,"lang":"en","type":"article","venue":"International Journal on Minority and Group Rights","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Harmony (color); Free speech; Multiculturalism; Government (linguistics); Value (mathematics); Law; Political science; Sociology; Linguistics","score_opus":0.01461372575035904,"score_gpt":0.2319685843408332,"score_spread":0.21735485859047415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038241675","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8510828,0.0008092648,0.0006930553,0.009446401,0.00008562507,0.00006837735,0.0001636083,0.000014092066,0.13763672],"genre_scores_gemma":[0.99440825,0.00023500796,0.00011668439,0.0004405849,0.000013095796,0.0000053744016,0.000032326898,0.000003957163,0.004744775],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9954959,0.0010571555,0.00007384651,0.00026887562,0.0013513304,0.0017529066],"domain_scores_gemma":[0.99224234,0.003108194,0.001190782,0.00022530805,0.0017555319,0.001477786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00321768,0.00027295496,0.00029768105,0.0023292578,0.020380197,0.004592911,0.00088521175,0.0012930983,0.010909718],"category_scores_gemma":[0.009331235,0.0001877625,0.00021355784,0.0022022882,0.010950013,0.0015881818,0.0025236018,0.0015380665,0.00022547848],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009670596,0.000060310384,0.062250152,0.00016351513,0.000019107403,0.0007917367,0.8360845,0.00021597218,0.001063755,0.050164055,0.011601883,0.037488304],"study_design_scores_gemma":[0.000009354454,0.00001891718,0.042790636,0.000177796,0.00001355619,0.00012279939,0.87502545,0.00014071637,0.00026085516,0.002124931,0.0792729,0.00004209326],"about_ca_topic_score_codex":0.9340278,"about_ca_topic_score_gemma":0.96763545,"teacher_disagreement_score":0.06597221,"about_ca_system_score_codex":0.03461237,"about_ca_system_score_gemma":0.03582963,"threshold_uncertainty_score":0.25113153},"labels":[],"label_agreement":null},{"id":"W2046672557","doi":"10.2139/ssrn.2341559","title":"The Normativity of Using Prison to Control Hate Speech: The Hollowness of Waldron's Harm Theory","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Harm; Prison; Criminology; Control (management); Sociology; Psychology; Social psychology; Economics; Management","score_opus":0.007341313300764423,"score_gpt":0.22437714535415818,"score_spread":0.21703583205339375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046672557","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14730251,0.0016602929,0.24549109,0.061563153,0.0008940509,0.00011397099,0.00018004228,0.00017836591,0.5426165],"genre_scores_gemma":[0.9858592,0.0001306487,0.0059828456,0.0017852776,0.00014552678,0.00009356295,0.000014839995,0.000042855936,0.005945284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98724514,0.006955698,0.0006304899,0.0017867468,0.0023697405,0.0010121668],"domain_scores_gemma":[0.97781134,0.012676851,0.0018324321,0.004750751,0.0021842504,0.000744371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012351825,0.00041872982,0.0011422733,0.0017859362,0.005051203,0.0056030503,0.0017499969,0.0041420776,0.0050033648],"category_scores_gemma":[0.024085095,0.0005931988,0.0009851927,0.00086919306,0.050426103,0.013300507,0.0052835816,0.005071989,0.00048445596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014128393,0.000010237506,0.00026685614,0.000012078088,0.0000054609095,0.000016079553,0.0013892014,0.00010477439,0.000066769026,0.99581075,0.00041972456,0.0018840482],"study_design_scores_gemma":[0.000011357316,0.0000131774805,0.00039935662,0.000019288074,0.000006961775,0.00003551621,0.00056134094,0.0006517152,0.00013179649,0.9952148,0.0029401407,0.000014597122],"about_ca_topic_score_codex":0.0048117,"about_ca_topic_score_gemma":0.003233803,"teacher_disagreement_score":0.012351825,"about_ca_system_score_codex":0.0027993533,"about_ca_system_score_gemma":0.0025359953,"threshold_uncertainty_score":0.06532347},"labels":[],"label_agreement":null},{"id":"W2048330628","doi":"10.1109/asonam.2012.128","title":"Do-it-yourself Justice - Considerations of Social Media use in a Crisis Situation: The Case of the 2011 Vancouver Riots","year":2012,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Downtown; Economic Justice; Criminology; Social justice; Upload; Political science; Media studies; Sociology; Law; History; Computer science","score_opus":0.040651452627542245,"score_gpt":0.26918797080219564,"score_spread":0.2285365181746534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048330628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61964506,0.002059265,0.0026570382,0.24326842,0.00070386706,0.00021682864,0.00008049871,0.000045032604,0.13132401],"genre_scores_gemma":[0.9774236,0.00070182263,0.0006213827,0.012598225,0.00020292413,0.000057297348,0.000017839493,0.000034784396,0.008342128],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.975661,0.015334082,0.0004945302,0.001040469,0.0028798594,0.0045900582],"domain_scores_gemma":[0.9614013,0.024322521,0.003219532,0.0010400793,0.003955044,0.006061492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011508111,0.0006111705,0.00057019695,0.001948482,0.05826277,0.019071467,0.0031852622,0.016491052,0.0038927882],"category_scores_gemma":[0.037167095,0.0009650978,0.00047181416,0.0018877444,0.03184918,0.0068577672,0.008008671,0.020131467,0.00053984614],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007887348,0.00019420972,0.014129375,0.00013432092,0.000038557206,0.033179983,0.86118275,0.00036855898,0.0007717401,0.054425642,0.021847252,0.013648816],"study_design_scores_gemma":[0.000015230673,0.00004187758,0.006105537,0.00025666493,0.00001923339,0.0032066812,0.9277191,0.00044765588,0.00028028424,0.005966042,0.05587643,0.000065291984],"about_ca_topic_score_codex":0.33267945,"about_ca_topic_score_gemma":0.5857457,"teacher_disagreement_score":0.66732055,"about_ca_system_score_codex":0.020297516,"about_ca_system_score_gemma":0.013193115,"threshold_uncertainty_score":0.6614861},"labels":[],"label_agreement":null},{"id":"W2055718917","doi":"10.7870/cjcmh-2003-0013","title":"Questioning and Sexual Minority Adolescents: High School Experiences of Bullying, Sexual Harassment and Physical Abuse","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Community Mental Health","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Queen's University; York University","funders":"","keywords":"Harassment; Lesbian; Sexual orientation; Sexual minority; Psychology; Mental health; Clinical psychology; Peer victimization; Sexual abuse; Developmental psychology; Suicide prevention; Poison control; Social psychology; Psychiatry; Medicine","score_opus":0.01723939726278877,"score_gpt":0.2699184376298941,"score_spread":0.2526790403671053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055718917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994524,0.00006677715,0.000010855713,0.000015840045,9.569153e-7,0.000012450269,0.00008197281,5.778403e-7,0.00035816614],"genre_scores_gemma":[0.9993025,0.00017271237,0.000052077357,0.000030051404,0.0000019693339,0.000013196257,0.00019556528,7.3594174e-7,0.00023110543],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994797,0.000073327115,0.00003416818,0.000041573767,0.00016684837,0.00020439921],"domain_scores_gemma":[0.9988287,0.0001326137,0.00044376188,0.000040808904,0.0002684671,0.0002855973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005510477,0.00026076532,0.00033341828,0.0012718163,0.0020396682,0.00096007297,0.0004201643,0.0002994687,0.0011023396],"category_scores_gemma":[0.0016653714,0.00025905104,0.00026997857,0.0013912804,0.0006584282,0.00040592693,0.00077922276,0.0006061135,0.00012679228],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016085783,0.000050217164,0.9906448,0.00001313725,0.0000082559345,0.0000691939,0.0064774207,0.0000060414263,0.00020485155,0.000023249442,0.00007907151,0.0024074833],"study_design_scores_gemma":[0.0000011018434,0.000040237835,0.9891465,0.000009859718,0.0000080538475,0.00012852233,0.010236732,0.000015498148,0.000077395904,0.000009329066,0.0003244941,0.0000023406826],"about_ca_topic_score_codex":0.5095264,"about_ca_topic_score_gemma":0.72930384,"teacher_disagreement_score":0.5095264,"about_ca_system_score_codex":0.0017563307,"about_ca_system_score_gemma":0.0025574395,"threshold_uncertainty_score":0.98672426},"labels":[],"label_agreement":null},{"id":"W2059012954","doi":"10.1371/journal.pmed.1001615","title":"How Can Journals Respond to Threats of Libel Litigation?","year":2014,"lang":"en","type":"article","venue":"PLoS Medicine","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Political science; Law; Business; Litigation risk analysis; Public relations; Law and economics; Sociology; Accounting","score_opus":0.03159098858300292,"score_gpt":0.2673885659275045,"score_spread":0.23579757734450157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059012954","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012455105,0.003246087,0.0011652583,0.9732335,0.014806646,0.000026093889,0.000022758972,0.00009899237,0.006155164],"genre_scores_gemma":[0.08154581,0.00756382,0.00481795,0.8370703,0.058279097,0.00012617042,0.0000499369,0.0002365393,0.0103104105],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.90428054,0.0512444,0.0074640536,0.005110497,0.02438681,0.0075136106],"domain_scores_gemma":[0.45336995,0.38111076,0.046099767,0.016325625,0.076853365,0.026240494],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.09358405,0.0008877504,0.0010894601,0.0064411196,0.010203409,0.041499697,0.0039453995,0.0435094,0.012931825],"category_scores_gemma":[0.5036842,0.0009917142,0.0015656479,0.0031866643,0.018550409,0.027411552,0.0077014975,0.026564006,0.00949967],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002453149,0.0002430287,0.007937236,0.001157431,0.00029140772,0.0019315892,0.012659828,0.00044877335,0.00093701493,0.08246126,0.7810869,0.110600285],"study_design_scores_gemma":[0.00015898848,0.00019793569,0.002970484,0.0014727095,0.000145206,0.0013299625,0.01526662,0.0005622789,0.0014484068,0.14359833,0.8325783,0.00027075896],"about_ca_topic_score_codex":0.0022415943,"about_ca_topic_score_gemma":0.004071338,"teacher_disagreement_score":0.9564906,"about_ca_system_score_codex":0.0046820138,"about_ca_system_score_gemma":0.014223241,"threshold_uncertainty_score":0.49492568},"labels":[],"label_agreement":null},{"id":"W2076306831","doi":"10.3138/gsp.3.3.353","title":"Counteracting Hate Speech as a Way of Preventing Genocidal Violence","year":2008,"lang":"en","type":"article","venue":"Genocide Studies and Prevention","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Genocide; Incitement; Hatred; Criminology; Ethnic group; Racism; Political science; Psychology; Law","score_opus":0.027671983611161202,"score_gpt":0.28650030129555915,"score_spread":0.25882831768439796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076306831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53567034,0.022462226,0.056666806,0.05386674,0.0039015205,0.0005403256,0.000053072206,0.0005070437,0.32633188],"genre_scores_gemma":[0.9602185,0.006550125,0.012199101,0.004934931,0.0005340013,0.00015363075,0.000022569477,0.000040980292,0.015346294],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99850094,0.0008207707,0.000065354696,0.00010008115,0.00027599794,0.0002368511],"domain_scores_gemma":[0.99733895,0.0011733241,0.0007353252,0.0001988441,0.00028783863,0.00026568753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001850219,0.00045561072,0.00027402665,0.0006415219,0.0015683874,0.0020146184,0.0006076406,0.0016730122,0.0029660591],"category_scores_gemma":[0.006591102,0.0001447979,0.00027565678,0.00020048948,0.002789056,0.0012319942,0.0021291159,0.0023597975,0.0005651792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019796839,0.0011641508,0.015552565,0.0015076671,0.000121404155,0.0014745254,0.035355974,0.0016277177,0.02928625,0.23061341,0.031089809,0.65200865],"study_design_scores_gemma":[0.00018754724,0.004354003,0.072491325,0.007865343,0.00048393686,0.0062922332,0.049437527,0.0063011264,0.06852956,0.0878666,0.6959961,0.00019465425],"about_ca_topic_score_codex":0.0014564999,"about_ca_topic_score_gemma":0.0036234136,"teacher_disagreement_score":0.0029660591,"about_ca_system_score_codex":0.0005228003,"about_ca_system_score_gemma":0.0017295707,"threshold_uncertainty_score":0.009922385},"labels":[],"label_agreement":null},{"id":"W2085385322","doi":"10.5964/ejop.v9i2.574","title":"On the Freedom of Speech and Expression: Interview with Noam Chomsky","year":2013,"lang":"en","type":"article","venue":"Europe’s Journal of Psychology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Linguistics; Chomsky hierarchy; Expression (computer science); Psychology; Computer science; Philosophy; Programming language; Rule-based machine translation","score_opus":0.02108368666875411,"score_gpt":0.25522190791124866,"score_spread":0.23413822124249456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085385322","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040029924,0.055601094,0.003480873,0.85509235,0.006231539,0.00037770666,0.00015065253,0.00004782258,0.038988017],"genre_scores_gemma":[0.6210237,0.06666636,0.0041688047,0.26021785,0.0030756183,0.0012475144,0.00009409937,0.00022298345,0.043283083],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9895382,0.007400018,0.00029293302,0.00062984787,0.0013623969,0.0007766843],"domain_scores_gemma":[0.9791226,0.014681613,0.00083348673,0.00033565063,0.002681483,0.002345152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012751375,0.00074008486,0.0014478996,0.0016870148,0.017511863,0.006073497,0.001538221,0.005945368,0.003797081],"category_scores_gemma":[0.02883224,0.0011121072,0.00048775203,0.001473207,0.017212866,0.014011727,0.0067546633,0.024958087,0.0012179897],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010076781,0.00013453035,0.0014720809,0.00039569187,0.000012833199,0.0015353148,0.6751233,0.00016817433,0.00094039756,0.028874503,0.26726937,0.023973018],"study_design_scores_gemma":[0.000017002634,0.00005962765,0.0023830815,0.0012654581,0.000007630207,0.0016468505,0.5487116,0.00025689977,0.00030511824,0.009870048,0.4353617,0.00011496622],"about_ca_topic_score_codex":0.020242002,"about_ca_topic_score_gemma":0.017752621,"teacher_disagreement_score":0.020242002,"about_ca_system_score_codex":0.007317065,"about_ca_system_score_gemma":0.007715299,"threshold_uncertainty_score":0.06743652},"labels":[],"label_agreement":null},{"id":"W2090994653","doi":"10.5210/fm.v20i2.5450","title":"#Hashtagging hate: Using Twitter to track racism online","year":2015,"lang":"en","type":"article","venue":"First Monday","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Racism; Social media; Track (disk drive); Sociology; Data collection; Work (physics); Media studies; Computer science; World Wide Web; Engineering; Social science; Gender studies","score_opus":0.06502641979598314,"score_gpt":0.28696884182649757,"score_spread":0.22194242203051442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090994653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94548213,0.00034638128,0.009822006,0.0013260877,0.0003613328,0.00040024356,0.015844885,0.0010914135,0.025325434],"genre_scores_gemma":[0.9501349,0.00040661817,0.026872618,0.00037146703,0.00024422674,0.000410918,0.009217136,0.00014153725,0.012200629],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99939656,0.00016740315,0.000047345206,0.000107399894,0.00019469544,0.00008669303],"domain_scores_gemma":[0.99765587,0.0007684098,0.0004624601,0.00025101448,0.00063744007,0.00022475915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007249781,0.0003190831,0.00017689554,0.0026247967,0.0009743854,0.0013400428,0.00027561843,0.00053712813,0.002220526],"category_scores_gemma":[0.0030021071,0.00013149717,0.00015092072,0.0018548815,0.00030051405,0.0014895691,0.0009552153,0.00046905363,0.0017285963],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005681641,0.0003258304,0.6054225,0.0005599634,0.000104407074,0.0005200914,0.014104093,0.0012545319,0.02272676,0.0028744221,0.03789017,0.31364918],"study_design_scores_gemma":[0.00003372538,0.00048906414,0.7885144,0.0002287799,0.00010294242,0.0010039328,0.029908398,0.027086979,0.029172359,0.0027789008,0.12047778,0.00020282679],"about_ca_topic_score_codex":0.011253343,"about_ca_topic_score_gemma":0.03251544,"teacher_disagreement_score":0.011253343,"about_ca_system_score_codex":0.0005167779,"about_ca_system_score_gemma":0.00043003677,"threshold_uncertainty_score":0.022375703},"labels":[],"label_agreement":null},{"id":"W2121185233","doi":"10.1177/01914537030295002","title":"Poststructuralism, Behaviorism and the Problem of Hate Speech","year":2003,"lang":"en","type":"article","venue":"Philosophy & Social Criticism","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Behaviorism; Epistemology; Quine; Relativism; Philosophy; Watson; Problematization; Sociology; Computer science","score_opus":0.013587615433783054,"score_gpt":0.2417995353784794,"score_spread":0.22821191994469633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121185233","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2121435,0.006333552,0.2866933,0.095468305,0.0014579309,0.00013699011,0.00009212065,0.00032045296,0.39735374],"genre_scores_gemma":[0.98565906,0.0007228403,0.0061177462,0.0020117536,0.00036912964,0.00004682319,0.000018929142,0.000039409304,0.005014282],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931497,0.00364717,0.00027670275,0.0010206602,0.0014143117,0.0004914412],"domain_scores_gemma":[0.98579085,0.00847126,0.002295603,0.001613676,0.0013772444,0.00045132314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008007326,0.0005433371,0.0004752369,0.0019173932,0.0038716837,0.0047645085,0.0018140242,0.003149467,0.002961781],"category_scores_gemma":[0.015499377,0.0003148165,0.0005061487,0.0011131597,0.042629566,0.00692746,0.0037226935,0.0052125044,0.0003778781],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000960424,0.000011992965,0.0011360035,0.0000331483,0.0000059453564,0.000044014905,0.006564559,0.00012661316,0.00017576234,0.9842588,0.0006742361,0.0069594095],"study_design_scores_gemma":[0.000007646384,0.00004123359,0.0030465205,0.00009341535,0.000013543582,0.00026593692,0.006330964,0.0011063104,0.000847972,0.9587099,0.029508542,0.00002791862],"about_ca_topic_score_codex":0.0027475005,"about_ca_topic_score_gemma":0.0021027224,"teacher_disagreement_score":0.008007326,"about_ca_system_score_codex":0.003062942,"about_ca_system_score_gemma":0.0013522904,"threshold_uncertainty_score":0.042347312},"labels":[],"label_agreement":null},{"id":"W2128955980","doi":"10.1017/s0008423910000107","title":"The Limits of Tolerance in Diverse Societies: Hate Speech and Political Tolerance Norms Among Youth","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Political Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Politics; Civil liberties; Multiculturalism; Freedom of expression; Sociology; Political science; Humanities; Social psychology; Human rights; Psychology; Law; Philosophy","score_opus":0.013227006977536905,"score_gpt":0.23572576996090103,"score_spread":0.22249876298336413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128955980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987638,0.000042867523,0.00006748447,0.00005763941,0.0000029547155,0.0000023684543,0.000012501469,0.0000010099865,0.0010492433],"genre_scores_gemma":[0.9998361,0.000022851476,0.000023656163,0.0000071190557,0.000002990144,0.0000027513283,0.0000109249095,8.8436116e-7,0.00009270095],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980769,0.0006633313,0.0001729806,0.0002495143,0.0005114686,0.00032572175],"domain_scores_gemma":[0.99069667,0.0023571984,0.004137363,0.0004037836,0.0010960018,0.0013089346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030214605,0.00021803073,0.00030487712,0.0018262733,0.0013097529,0.0031370209,0.0004441544,0.0004700952,0.0016676236],"category_scores_gemma":[0.010078034,0.00021394629,0.00021584978,0.0010875221,0.0033329655,0.0012616407,0.0020937975,0.0010018054,0.00017083777],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075425385,0.0001377352,0.8887225,0.000023778734,0.000021913624,0.00015017836,0.09706037,0.00009124766,0.0007803163,0.002412983,0.00017831569,0.010345293],"study_design_scores_gemma":[0.0000051732286,0.00010037556,0.90298957,0.000050710823,0.000014002661,0.00023079432,0.09321494,0.00040821094,0.0005155548,0.0013033365,0.001146239,0.00002108958],"about_ca_topic_score_codex":0.007360988,"about_ca_topic_score_gemma":0.008180091,"teacher_disagreement_score":0.007360988,"about_ca_system_score_codex":0.0007565943,"about_ca_system_score_gemma":0.00051408855,"threshold_uncertainty_score":0.01597923},"labels":[],"label_agreement":null},{"id":"W2130288774","doi":"10.1177/1748048510363627","title":"Communication as a Human Right: A Blind Spot in Communication Research?","year":2010,"lang":"en","type":"article","venue":"International Communication Gazette","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Blind spot; Hot spot (computer programming); Communication; Psychology; Computer science; Artificial intelligence","score_opus":0.043310357016677056,"score_gpt":0.3742484586625273,"score_spread":0.33093810164585025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130288774","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02983564,0.15665743,0.020858789,0.7193775,0.0119696595,0.00009124386,0.00041094059,0.00015730833,0.060641523],"genre_scores_gemma":[0.7036793,0.09978788,0.009285736,0.15562537,0.02224324,0.0003665541,0.00014375302,0.00036654045,0.008501541],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9663123,0.022319475,0.0013810783,0.003667938,0.0042935475,0.0020257137],"domain_scores_gemma":[0.8730854,0.09541031,0.0054658116,0.009206855,0.009695005,0.007136503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04911806,0.0009923588,0.003714516,0.0067693894,0.013237863,0.020066058,0.003133477,0.013027404,0.019392561],"category_scores_gemma":[0.10013627,0.0011511402,0.00089395914,0.0058539803,0.0673617,0.057875976,0.01103013,0.01518942,0.002819509],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004049861,0.00027982294,0.014434552,0.0028643103,0.00022284976,0.0006408944,0.076002225,0.00032363186,0.00056687254,0.5423359,0.07633951,0.2855844],"study_design_scores_gemma":[0.00010318921,0.00025003066,0.0111644175,0.009248839,0.00019210139,0.0015388447,0.13288,0.0008947959,0.0006733802,0.6374011,0.20537432,0.00027888134],"about_ca_topic_score_codex":0.02693537,"about_ca_topic_score_gemma":0.02422749,"teacher_disagreement_score":0.04911806,"about_ca_system_score_codex":0.004761236,"about_ca_system_score_gemma":0.013405905,"threshold_uncertainty_score":0.2597642},"labels":[],"label_agreement":null},{"id":"W2138702777","doi":"10.1017/s1574019611100048","title":"‘Bad Tendencies’ in the ECtHR's ‘Hate Speech’ Jurisprudence","year":2011,"lang":"en","type":"article","venue":"European Constitutional Law Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Jurisprudence; Incitement; Law; Human rights; Supreme court; Charter; Political science; Sociology","score_opus":0.03921700626930504,"score_gpt":0.25012970074368324,"score_spread":0.2109126944743782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138702777","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09681531,0.0065763365,0.024572778,0.17045256,0.0011178768,0.00026497353,0.00033922706,0.0001272337,0.69973373],"genre_scores_gemma":[0.9491349,0.00073118106,0.005156243,0.024834206,0.00023871416,0.00010446776,0.000088861765,0.00006834236,0.019643117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.97297835,0.00623299,0.0013625389,0.0030712548,0.012264405,0.004090385],"domain_scores_gemma":[0.9728143,0.016909242,0.00080859644,0.0019983219,0.006388014,0.0010814243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02127604,0.0005243174,0.0009839226,0.0034178637,0.015264848,0.0141857555,0.0031534953,0.0114639215,0.0019245791],"category_scores_gemma":[0.03301098,0.0007076353,0.00071761553,0.0029862286,0.07147769,0.0057301275,0.0045341207,0.018548353,0.00022571119],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055629516,0.0000057610246,0.00030779373,0.00001278988,0.000004131418,0.000037476788,0.006842157,0.000080640486,0.000089634945,0.98598933,0.0036679334,0.0029567305],"study_design_scores_gemma":[0.000044994104,0.000029985174,0.011132878,0.0003693893,0.000082319726,0.00023256212,0.010730534,0.0011755809,0.0012757798,0.7286889,0.24603918,0.00019801184],"about_ca_topic_score_codex":0.630201,"about_ca_topic_score_gemma":0.7135419,"teacher_disagreement_score":0.630201,"about_ca_system_score_codex":0.031421516,"about_ca_system_score_gemma":0.044516884,"threshold_uncertainty_score":0.7439537},"labels":[],"label_agreement":null},{"id":"W2166345935","doi":"10.1145/2656870.2656876","title":"Using computer technology to address the problem of cyberbullying","year":2014,"lang":"en","type":"article","venue":"ACM SIGCAS Computers and Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Leverage (statistics); Computer science; Order (exchange); Computer technology; Computer security; Internet privacy; Business; Multimedia; Artificial intelligence","score_opus":0.018298998404451747,"score_gpt":0.2610942048042327,"score_spread":0.24279520639978092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166345935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3768357,0.010847081,0.4552647,0.01325549,0.0009561034,0.0007067801,0.0001692327,0.0028000225,0.13916497],"genre_scores_gemma":[0.8811185,0.0051373346,0.10029194,0.001305483,0.00027279052,0.0001382302,0.00011535343,0.000062852436,0.011557554],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870265,0.0006149132,0.00006558901,0.00015183612,0.0003106059,0.00015442132],"domain_scores_gemma":[0.9972398,0.0012899007,0.0003981399,0.0004812364,0.00044348036,0.00014752842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094840303,0.0005563744,0.00031725856,0.0017627807,0.0010627056,0.0025562271,0.000773675,0.001647147,0.0040989034],"category_scores_gemma":[0.0042318203,0.0001812846,0.00033493032,0.00088117697,0.0013128502,0.0031103343,0.0019127548,0.0008931486,0.0011261592],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014458122,0.00061253743,0.038591653,0.00085998815,0.00014999784,0.0011876317,0.0039928593,0.005079203,0.053279772,0.042597454,0.010007651,0.8434967],"study_design_scores_gemma":[0.00016434133,0.004192205,0.09024692,0.001997251,0.00079448795,0.013148914,0.02113547,0.11083133,0.15153468,0.107127704,0.4984277,0.00039904847],"about_ca_topic_score_codex":0.001135642,"about_ca_topic_score_gemma":0.0014659314,"teacher_disagreement_score":0.0040989034,"about_ca_system_score_codex":0.00033571353,"about_ca_system_score_gemma":0.0009503827,"threshold_uncertainty_score":0.013712168},"labels":[],"label_agreement":null},{"id":"W2172239277","doi":"10.1017/s1352325211000255","title":"THE STATUS OF HEARERS’ RIGHTS IN FREEDOM OF EXPRESSION","year":2012,"lang":"en","type":"article","venue":"Legal Theory","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Expression (computer science); Freedom of expression; Jurisprudence; Law; Philosophy; Political science; Computer science; Human rights","score_opus":0.008376557374638853,"score_gpt":0.2312050392591772,"score_spread":0.22282848188453835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172239277","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06216405,0.0041048564,0.0467148,0.07710146,0.0006666567,0.000058228878,0.0001074954,0.00008827518,0.8089942],"genre_scores_gemma":[0.96480423,0.0008645005,0.0031880604,0.0054775663,0.00067075755,0.000117175965,0.00004978734,0.00006669733,0.024761194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96669286,0.015919643,0.001652938,0.004946305,0.007291348,0.0034967544],"domain_scores_gemma":[0.918944,0.061761983,0.0037517764,0.008278879,0.0051588677,0.0021045278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027722694,0.00046834722,0.00082092854,0.0021312272,0.009101313,0.016430613,0.0022592442,0.010017452,0.009369012],"category_scores_gemma":[0.05953525,0.0006717393,0.0010107914,0.0012720132,0.0676807,0.021327032,0.009576868,0.012690166,0.0012932159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013498759,0.000008460143,0.00036427885,0.0000150242395,0.000004374638,0.00004056332,0.00540596,0.000042312415,0.0001325056,0.9896237,0.0008751844,0.0034740462],"study_design_scores_gemma":[0.000032987056,0.000032385204,0.0013035198,0.0001839351,0.000024116405,0.00015716905,0.004257351,0.0004932596,0.0008065768,0.9474203,0.045242175,0.000046219564],"about_ca_topic_score_codex":0.0044740257,"about_ca_topic_score_gemma":0.0025598612,"teacher_disagreement_score":0.027722694,"about_ca_system_score_codex":0.0036045806,"about_ca_system_score_gemma":0.0038883514,"threshold_uncertainty_score":0.14661342},"labels":[],"label_agreement":null},{"id":"W2175943076","doi":"","title":"Piracy by Approval: Social Norms, Deterrence, and Copyright Compliance in China Compared to the United States","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lambton College","funders":"","keywords":"Enforcement; Deterrence theory; Compliance (psychology); Deterrence (psychology); Duty; Law enforcement; Political science; China; Business; Law and economics; Social psychology; Advertising; Psychology; Law; Economics","score_opus":0.009310762633581576,"score_gpt":0.23710297078429907,"score_spread":0.2277922081507175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175943076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994831,0.000014121587,0.000013095741,0.000039125087,0.0000014406313,0.000001969472,0.000008178803,4.393941e-7,0.00043841032],"genre_scores_gemma":[0.9998318,0.000014739474,0.000007521835,0.000012233456,0.0000015831638,0.0000016380238,0.000014910402,4.4103464e-7,0.0001150955],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99929464,0.00017039309,0.000060526512,0.000106801475,0.0002170233,0.00015059166],"domain_scores_gemma":[0.9971904,0.0006303001,0.0011220766,0.00015096477,0.00038658845,0.00051964296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011294918,0.00020257031,0.00025993626,0.0011657164,0.0008549979,0.0010538313,0.00022557049,0.00028268527,0.00161829],"category_scores_gemma":[0.0036229796,0.00013516846,0.0002890024,0.0012441273,0.0012689674,0.0006142222,0.0007770468,0.00042304792,0.000111516885],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006865781,0.000108952976,0.9899275,0.000009748154,0.0000312685,0.000073424264,0.0046246913,0.00007756516,0.00034157195,0.00030891463,0.00007193328,0.0043557906],"study_design_scores_gemma":[0.0000033402125,0.00004517491,0.9973105,0.0000034619684,0.000008719754,0.000020747913,0.0020484861,0.0002852821,0.000077437406,0.00006059478,0.00013160177,0.000004684856],"about_ca_topic_score_codex":0.069726236,"about_ca_topic_score_gemma":0.11042215,"teacher_disagreement_score":0.069726236,"about_ca_system_score_codex":0.0012558647,"about_ca_system_score_gemma":0.0013735357,"threshold_uncertainty_score":0.13864076},"labels":[],"label_agreement":null},{"id":"W2179798232","doi":"10.1177/2056305115603997","title":"Histories of Hating","year":2015,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Situated; Perspective (graphical); Digital media; Order (exchange); Psychological intervention; Sociology; Media studies; Epistemology; Psychology; Political science; Law; Computer science","score_opus":0.03877005763817307,"score_gpt":0.24430405591224716,"score_spread":0.2055339982740741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179798232","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47655752,0.013500284,0.010364357,0.011470304,0.0013006314,0.00006556819,0.000192776,0.00010007961,0.4864485],"genre_scores_gemma":[0.9771235,0.0021852497,0.00052642246,0.0010361673,0.00022213373,0.000022995422,0.000043760876,0.000037299174,0.018802475],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99597,0.0019067897,0.00019880007,0.00059474766,0.0007661484,0.0005635245],"domain_scores_gemma":[0.9916596,0.004899599,0.0009347887,0.0010535547,0.00081881456,0.0006336191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004051422,0.00041592214,0.00029460917,0.002336157,0.01180434,0.0065504853,0.00093391736,0.0020296266,0.007091915],"category_scores_gemma":[0.010085344,0.00038564243,0.00025861472,0.001325939,0.019120408,0.008439261,0.0078973025,0.0037141277,0.00075214525],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000611078,0.000039462026,0.009571338,0.00022199286,0.00001953266,0.001340972,0.51830626,0.00009511513,0.0010656638,0.4105553,0.0059698108,0.05275356],"study_design_scores_gemma":[0.000006250111,0.00011427708,0.01201187,0.0009821756,0.000032953132,0.004321758,0.3089782,0.00030909755,0.0036235622,0.056298062,0.61323845,0.000083349514],"about_ca_topic_score_codex":0.004085673,"about_ca_topic_score_gemma":0.0043787076,"teacher_disagreement_score":0.01180434,"about_ca_system_score_codex":0.00318043,"about_ca_system_score_gemma":0.0010871104,"threshold_uncertainty_score":0.023724794},"labels":[],"label_agreement":null},{"id":"W2216551173","doi":"","title":"Reflections of an Online Internship: Evaluating, Identifying, and Applying Best Practices for Online Course Moderation","year":2011,"lang":"en","type":"article","venue":"E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Internship; Moderation; Massive open online course; Course (navigation); Psychology; Online course; Computer science; Medical education; Internet privacy; Social psychology; Mathematics education; Engineering; Medicine","score_opus":0.3595365285456168,"score_gpt":0.4318035734814901,"score_spread":0.07226704493587333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216551173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94522774,0.00035520396,0.008974029,0.02424933,0.0018655586,0.0009586401,0.0002771388,0.00048159962,0.017610896],"genre_scores_gemma":[0.96284974,0.00039620188,0.016490877,0.003642847,0.0004632277,0.000609602,0.00021606714,0.00025595748,0.015075527],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98411846,0.011343445,0.00057070743,0.00068712,0.0016859247,0.0015943461],"domain_scores_gemma":[0.89118993,0.06624548,0.0063853394,0.006228756,0.013934984,0.01601552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022450784,0.00082667073,0.00042428193,0.0009961226,0.0066546123,0.004068445,0.002065364,0.0033418492,0.004708598],"category_scores_gemma":[0.11618214,0.00055979576,0.0005636487,0.00050221296,0.0019429927,0.002905233,0.005898756,0.0064461753,0.0011822506],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011156119,0.0064505143,0.039722174,0.000850053,0.00010651778,0.002458299,0.6084791,0.0010048222,0.01480415,0.0028079932,0.055497065,0.26670378],"study_design_scores_gemma":[0.00015050593,0.00542904,0.05733281,0.0007446824,0.00016628442,0.00072542444,0.7540525,0.0020435443,0.011847279,0.0028934025,0.16432345,0.00029106872],"about_ca_topic_score_codex":0.0018102214,"about_ca_topic_score_gemma":0.007098021,"teacher_disagreement_score":0.022450784,"about_ca_system_score_codex":0.0026591632,"about_ca_system_score_gemma":0.0044659306,"threshold_uncertainty_score":0.11873257},"labels":[],"label_agreement":null},{"id":"W2219607269","doi":"10.26686/vuwlr.v39i2.5464","title":"Freedom of Expression, Commercial Expression and Tobacco in Canada","year":2008,"lang":"en","type":"article","venue":"Victoria University of Wellington Law Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Expression (computer science); Supreme court; Freedom of expression; Law; Flexibility (engineering); Value (mathematics); Democracy; Political science; Law and economics; Sociology; Human rights; Economics; Computer science; Management; Politics","score_opus":0.011249540627606525,"score_gpt":0.18831942828267745,"score_spread":0.17706988765507092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219607269","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15514933,0.010790893,0.0013674543,0.066061154,0.00041208413,0.0001881197,0.0005282195,0.000053193584,0.7654495],"genre_scores_gemma":[0.8580468,0.0051198574,0.00095243094,0.01779213,0.000111582995,0.000069575726,0.00017798327,0.000033534492,0.11769615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948112,0.0003096852,0.00015035085,0.00043133978,0.0021956463,0.0021017825],"domain_scores_gemma":[0.99502254,0.0018712649,0.00029782834,0.00022307037,0.0014874657,0.0010978557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023728453,0.0002643981,0.0004738798,0.0018410331,0.021783851,0.00953022,0.001971842,0.0057323254,0.0110535575],"category_scores_gemma":[0.0076504736,0.0005062869,0.0006344703,0.0026877823,0.00961553,0.0019283877,0.0039890087,0.0069978987,0.00038702777],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000636904,0.000088183166,0.014942517,0.00014080455,0.00003812544,0.0013376921,0.009636864,0.00081982854,0.0006021309,0.9043053,0.03537865,0.032646164],"study_design_scores_gemma":[0.00013976738,0.00009070644,0.09430723,0.0009243872,0.00020796797,0.0007503641,0.024335789,0.0044080447,0.001918199,0.11599245,0.7565694,0.0003556761],"about_ca_topic_score_codex":0.9927724,"about_ca_topic_score_gemma":0.99641645,"teacher_disagreement_score":0.087830774,"about_ca_system_score_codex":0.087830774,"about_ca_system_score_gemma":0.12000546,"threshold_uncertainty_score":0.63725984},"labels":[],"label_agreement":null},{"id":"W2219893227","doi":"10.1093/oxfordhb/9780199935383.013.101","title":"Hate Crimes","year":2015,"lang":"en","type":"book-chapter","venue":"Oxford University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislation; Punishment (psychology); Hate crime; Legislature; Criminology; Political science; Law; Criminal law; Order (exchange); Retributive justice; Inclusion (mineral); Sociology; Economic Justice; Psychology; Social psychology; Business","score_opus":0.037418127226927694,"score_gpt":0.2101214733419701,"score_spread":0.1727033461150424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219893227","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008983994,0.029901756,0.0035194175,0.0046330923,0.0017979969,0.00009385399,0.00028307442,0.00016682496,0.95061994],"genre_scores_gemma":[0.12152348,0.04970267,0.0028686572,0.005114676,0.0010515332,0.00008077361,0.00063088076,0.00009240281,0.818935],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908125,0.00015493548,0.000038584476,0.00014462261,0.00045628924,0.00012435224],"domain_scores_gemma":[0.99918276,0.00028408956,0.00011405953,0.000078899204,0.0002459458,0.000094109746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000562972,0.000534794,0.00036365926,0.0021094389,0.0014060189,0.0030286713,0.0007801656,0.0012601046,0.03396947],"category_scores_gemma":[0.00184744,0.0001998861,0.00027669,0.0009783813,0.0020667503,0.0019267208,0.0017070335,0.0016364394,0.010889963],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011477728,0.000053736905,0.0017274806,0.0003485499,0.000007653541,0.00019955519,0.0025957744,0.00023804526,0.00041901402,0.34681025,0.16800915,0.47957924],"study_design_scores_gemma":[0.000001865564,0.000021841699,0.003006981,0.0008175022,0.0000037569878,0.00063632784,0.0010693991,0.00015373499,0.00048558592,0.011180331,0.98261285,0.000009826009],"about_ca_topic_score_codex":0.0047097714,"about_ca_topic_score_gemma":0.0071067205,"teacher_disagreement_score":0.03396947,"about_ca_system_score_codex":0.0011168523,"about_ca_system_score_gemma":0.0012739252,"threshold_uncertainty_score":0.113639176},"labels":[],"label_agreement":null},{"id":"W2238729486","doi":"","title":"Should Hate Speech Be Allowed on the Internet? A Reply to Raphael Cohen-Almagor","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Free speech; Denial; Argument (complex analysis); The Holocaust; The Internet; Speech act; Law; Sociology; Law and economics; Media studies; Political science; Philosophy; Psychoanalysis; Psychology; Linguistics; Computer science","score_opus":0.0151857255301683,"score_gpt":0.2473013205240042,"score_spread":0.23211559499383588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2238729486","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038514828,0.0016978693,0.00017427024,0.99465865,0.0021995397,0.0000026533282,0.000020936164,0.0000098126575,0.0008510561],"genre_scores_gemma":[0.011302678,0.0020792794,0.0003682227,0.97769916,0.0064725606,0.00001930005,0.0000158699,0.000019869334,0.0020231307],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99222285,0.003413473,0.00032381187,0.001633916,0.0019807867,0.00042522134],"domain_scores_gemma":[0.91964155,0.06606824,0.0025598195,0.0014495425,0.007444352,0.002836527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012119336,0.000954202,0.0015361264,0.001440884,0.0065,0.00567349,0.0033595848,0.036678493,0.0044670864],"category_scores_gemma":[0.05264116,0.0006972569,0.00069959817,0.0017286119,0.0151490355,0.014779394,0.0027456407,0.06017666,0.0034880124],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046807545,0.00003876805,0.00094371225,0.000083346524,0.000018972854,0.0001794645,0.0028210168,0.00004571697,0.00013933191,0.023113867,0.9592131,0.01335582],"study_design_scores_gemma":[0.000087765664,0.000077602424,0.005506036,0.0009208054,0.00005173974,0.0013088082,0.016657157,0.0007838652,0.00071209855,0.06648923,0.9071486,0.00025631685],"about_ca_topic_score_codex":0.012888651,"about_ca_topic_score_gemma":0.017559377,"teacher_disagreement_score":0.036678493,"about_ca_system_score_codex":0.0032677753,"about_ca_system_score_gemma":0.0033107575,"threshold_uncertainty_score":0.06409395},"labels":[],"label_agreement":null},{"id":"W2252758653","doi":"","title":"Warman v. Lemire, Canadian Human Rights Tribunal Expert Report [Hate Speech Regulation in a Democratic State]","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Law; Democracy; Human rights; Tribunal; Free speech; Value (mathematics); Political science; State (computer science); Fundamental rights; Politics","score_opus":0.006975560591811402,"score_gpt":0.24807685808402882,"score_spread":0.24110129749221743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252758653","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023416603,0.03681177,0.0021900414,0.29926398,0.0045100776,0.0012200777,0.006813603,0.00053275103,0.62524104],"genre_scores_gemma":[0.1555971,0.021256842,0.0031638155,0.2190159,0.0008277639,0.0004560578,0.0019658634,0.0002583412,0.59745836],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99490947,0.00032821222,0.00018116857,0.00036873782,0.0028675392,0.0013448027],"domain_scores_gemma":[0.99435836,0.0017625436,0.0001715366,0.00014489614,0.0028518701,0.00071085367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038980965,0.0006491932,0.000357484,0.0022408767,0.019738674,0.0054870932,0.0033457011,0.013168957,0.014404632],"category_scores_gemma":[0.0092595145,0.000784238,0.00048231657,0.002395524,0.0032088463,0.0017880396,0.0013018791,0.0061597256,0.0015958825],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020352994,0.000028076125,0.0014578593,0.00013788407,0.000007597935,0.00044983128,0.0020905386,0.00020595218,0.0003524147,0.033208076,0.94925994,0.01278151],"study_design_scores_gemma":[0.00003221998,0.000016448885,0.013163723,0.00042788978,0.00005010954,0.00013461261,0.0033571427,0.0003351797,0.0009745368,0.004036361,0.97733206,0.00013986875],"about_ca_topic_score_codex":0.98419005,"about_ca_topic_score_gemma":0.9928732,"teacher_disagreement_score":0.038451873,"about_ca_system_score_codex":0.038451873,"about_ca_system_score_gemma":0.15180074,"threshold_uncertainty_score":0.2789892},"labels":[],"label_agreement":null},{"id":"W2255401858","doi":"","title":"Banning hate speech from public discourse in Canada and South Africa : a legal analysis of the roles of both countries' constitutional courts and human rights institutions","year":2012,"lang":"en","type":"article","venue":"Southern African Public Law","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Freedom of expression; Human rights; Democracy; Expression (computer science); Realm; Law; Thriving; Political science; Constitutional right; Freedom of the press; Fundamental rights; Constitutional court; Sociology; Politics; Social science; Supreme court","score_opus":0.01587222023007086,"score_gpt":0.22014443400692996,"score_spread":0.20427221377685909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255401858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79418343,0.005570623,0.0013120425,0.02445739,0.00008139803,0.00031232857,0.00029440605,0.000023951163,0.17376432],"genre_scores_gemma":[0.9877017,0.0025762776,0.00064607203,0.0010606312,0.000010677394,0.000054986296,0.000059230533,0.000009731841,0.007880627],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99556017,0.0005857551,0.00012372939,0.00019695495,0.0012224438,0.002310938],"domain_scores_gemma":[0.9898812,0.00440947,0.00089943234,0.00023648156,0.0033261343,0.0012471839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029132613,0.00043601613,0.00044658943,0.0050869705,0.020327765,0.009755888,0.0015164245,0.0021666794,0.0041134437],"category_scores_gemma":[0.014797748,0.00041336418,0.0004209472,0.006341448,0.0116828475,0.0029038233,0.0033138522,0.0034768193,0.00018760502],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019293207,0.00016358627,0.07622695,0.0006423147,0.00006953968,0.0034481846,0.27890924,0.0013097153,0.0018595603,0.56389236,0.0126297325,0.060655855],"study_design_scores_gemma":[0.000087569795,0.00006031092,0.17855912,0.0017216722,0.000330211,0.0007451159,0.6214416,0.0036788005,0.002963814,0.02630494,0.16390473,0.00020208344],"about_ca_topic_score_codex":0.9922069,"about_ca_topic_score_gemma":0.9964893,"teacher_disagreement_score":0.12813424,"about_ca_system_score_codex":0.12813424,"about_ca_system_score_gemma":0.21097493,"threshold_uncertainty_score":0.9296833},"labels":[],"label_agreement":null},{"id":"W2255441595","doi":"","title":"Ethical Considerations in Media Coverage of Hate Speech in Canada","year":2001,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Media coverage; Law; Sociology; Media studies","score_opus":0.01422143490793666,"score_gpt":0.2238625501760097,"score_spread":0.20964111526807305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255441595","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39514348,0.004132443,0.0016381143,0.46238688,0.0016618967,0.00044328743,0.0009600129,0.00006896945,0.133565],"genre_scores_gemma":[0.8830035,0.0016247848,0.00088319875,0.086645946,0.0006217384,0.00017008359,0.00017993384,0.000053746582,0.026817007],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9665841,0.008104477,0.001350139,0.0014963022,0.01261425,0.009850809],"domain_scores_gemma":[0.81209934,0.11133882,0.009871199,0.0029271224,0.044465728,0.019297792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019203465,0.0003488107,0.0005457654,0.0043977094,0.025683967,0.0117792245,0.0036133376,0.015514862,0.0061641177],"category_scores_gemma":[0.121730044,0.0010866527,0.0006342977,0.0038451073,0.008564199,0.002185905,0.0029206574,0.011285611,0.00040181298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009828756,0.00052922615,0.19267426,0.0006159398,0.0002742305,0.0072231907,0.15233399,0.0027780193,0.0035578746,0.23649333,0.32306322,0.07947382],"study_design_scores_gemma":[0.0002728704,0.00030072508,0.32476243,0.0020329778,0.00032915352,0.0023174009,0.16025476,0.0067189783,0.0051413467,0.014854545,0.4823711,0.00064368534],"about_ca_topic_score_codex":0.98424524,"about_ca_topic_score_gemma":0.9901293,"teacher_disagreement_score":0.089492284,"about_ca_system_score_codex":0.089492284,"about_ca_system_score_gemma":0.20280899,"threshold_uncertainty_score":0.649315},"labels":[],"label_agreement":null},{"id":"W2258367759","doi":"","title":"Regulating cases of 'extreme hate speech' in South Africa : a suggested framework for a legislated criminal sanction","year":2014,"lang":"en","type":"article","venue":"South African Journal of Criminal Justice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Incitement; Mandate; Harm; Law; Political science; Criminal law; Sexual orientation; Criminology; Sociology; Psychology; Social psychology","score_opus":0.04765374763878635,"score_gpt":0.2697585790390171,"score_spread":0.22210483140023077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258367759","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11042443,0.0064802775,0.2543175,0.30783054,0.004979653,0.0027482607,0.00018709285,0.00058300124,0.31244928],"genre_scores_gemma":[0.81487197,0.0018008419,0.11952451,0.03315158,0.0012202957,0.0023588622,0.00006603326,0.00008625891,0.026919689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98091733,0.0072069955,0.0017759938,0.0024025838,0.0040511834,0.0036458408],"domain_scores_gemma":[0.9795781,0.010907241,0.00216606,0.0015936956,0.00417652,0.0015783901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023720821,0.00071057316,0.00082860683,0.004049554,0.008927853,0.01239222,0.0043502273,0.017006548,0.004307479],"category_scores_gemma":[0.028339444,0.00079759094,0.0015104099,0.000832133,0.015999017,0.0071513155,0.008161964,0.012920704,0.0007280469],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034599325,0.00016020612,0.0027248927,0.00018327808,0.000024361036,0.0015087588,0.00422619,0.002091985,0.0021665585,0.9618508,0.006617676,0.018410677],"study_design_scores_gemma":[0.00038630812,0.00076997746,0.014999092,0.0050990847,0.0003165662,0.003546283,0.017534036,0.019032469,0.007255947,0.5889612,0.34130678,0.0007922375],"about_ca_topic_score_codex":0.015399119,"about_ca_topic_score_gemma":0.028565586,"teacher_disagreement_score":0.023720821,"about_ca_system_score_codex":0.0079162745,"about_ca_system_score_gemma":0.024388596,"threshold_uncertainty_score":0.12544918},"labels":[],"label_agreement":null},{"id":"W2270948127","doi":"","title":"Twenty Years Later Taylor Still Has It Right: How the Canadian Human Rights Act’s Hate Speech Provision Continues to Contribute to Equality","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Supreme court; Repeal; Hatred; Human rights; Political science; Law; Context (archaeology); Legislature; Democracy; Incitement; Harm; Scapegoating; Politics; Prejudice (legal term); Sociology","score_opus":0.012153164727101571,"score_gpt":0.2460651686549068,"score_spread":0.23391200392780523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270948127","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020002648,0.011752774,0.0051291897,0.6796048,0.005245399,0.000075215336,0.0003093865,0.000088449866,0.27779207],"genre_scores_gemma":[0.44411322,0.009712829,0.0043732626,0.29147008,0.0015456367,0.00006908452,0.00013145429,0.00017891462,0.24840543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.990299,0.0012470661,0.0001801389,0.0013084941,0.0047785644,0.0021867014],"domain_scores_gemma":[0.9871749,0.0048915646,0.0003636314,0.00043528908,0.006001936,0.0011326151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006450837,0.00055312517,0.0006077636,0.0016444802,0.028733613,0.018037612,0.0018053374,0.013611568,0.007081714],"category_scores_gemma":[0.018519849,0.00042438062,0.0006113336,0.0019565,0.028396524,0.008117464,0.0028802333,0.014525454,0.001140501],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003393849,0.000018690494,0.001705516,0.00006212537,0.000015137712,0.00023770116,0.019580897,0.00019374314,0.00037285534,0.7748681,0.16847354,0.03443786],"study_design_scores_gemma":[0.00001871823,0.000023175753,0.0040625827,0.00031062742,0.00004353007,0.0001509709,0.01683722,0.00055693544,0.0010012948,0.062729575,0.9140779,0.00018753366],"about_ca_topic_score_codex":0.9540241,"about_ca_topic_score_gemma":0.97150403,"teacher_disagreement_score":0.07346025,"about_ca_system_score_codex":0.07346025,"about_ca_system_score_gemma":0.08237407,"threshold_uncertainty_score":0.5329939},"labels":[],"label_agreement":null},{"id":"W2290337742","doi":"10.29173/alr1313","title":"E-Racing Racial Profiling","year":2004,"lang":"en","type":"article","venue":"Alberta Law Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Racial profiling; Profiling (computer programming); Charter; Reasonable suspicion; Legislation; Political science; Supreme court; Law; Criminology; Sociology; Computer science; Race (biology)","score_opus":0.011669131954357407,"score_gpt":0.25120834013083243,"score_spread":0.23953920817647503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290337742","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028663801,0.004322554,0.0030076394,0.07209955,0.0011596313,0.00022991015,0.00030393465,0.00023026868,0.88998264],"genre_scores_gemma":[0.31899938,0.0057122367,0.0029154753,0.03904631,0.00041506547,0.00007727793,0.00029552888,0.00007620969,0.63246256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99436176,0.0009221938,0.00018205769,0.00030277725,0.0025214995,0.0017096541],"domain_scores_gemma":[0.9878269,0.002514568,0.00046327856,0.0006472672,0.007116084,0.0014318596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004983303,0.00020915233,0.00023741333,0.0024863593,0.011219924,0.0053238105,0.0013588042,0.0032177747,0.017499192],"category_scores_gemma":[0.014255141,0.00027280409,0.0002536531,0.0023234752,0.003606227,0.0019634827,0.0023304038,0.0018038806,0.002148015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029199255,0.00007931513,0.016356077,0.00015087107,0.000009496513,0.0005441549,0.005109608,0.0003002903,0.00070022093,0.2036919,0.56385124,0.20917775],"study_design_scores_gemma":[0.000009279167,0.000021737495,0.021534422,0.00012360618,0.000015324636,0.00020325759,0.005492919,0.0003433829,0.0006134343,0.004992303,0.9666166,0.000033810164],"about_ca_topic_score_codex":0.8144239,"about_ca_topic_score_gemma":0.9063671,"teacher_disagreement_score":0.8144239,"about_ca_system_score_codex":0.012990265,"about_ca_system_score_gemma":0.044747416,"threshold_uncertainty_score":0.37333792},"labels":[],"label_agreement":null},{"id":"W2291324282","doi":"10.4314/pelj.v18i6.06","title":"Cyberbullying: Should Schools choose between Safety and Privacy?","year":2015,"lang":"en","type":"article","venue":"Potchefstroom Electronic Law Journal/Potchefstroomse Elektroniese Regsblad","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cyberspace; Anonymity; Monster; Law; Face (sociological concept); Political science; Expression (computer science); State (computer science); The Right to Privacy; Internet privacy; Right to privacy; Freedom of information; Sociology; Human rights; Public relations; The Internet; Computer science; Social science","score_opus":0.027093879895546673,"score_gpt":0.2606954477839584,"score_spread":0.23360156788841174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291324282","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07535561,0.009047439,0.0061752275,0.83669007,0.0012996668,0.000097918826,0.000058712398,0.000049253635,0.07122609],"genre_scores_gemma":[0.9408202,0.0057764715,0.0019501542,0.044196244,0.00043539685,0.00008020031,0.000027263424,0.000032684737,0.0066814222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9885964,0.0063311383,0.00035637876,0.0011135744,0.0013065578,0.002295973],"domain_scores_gemma":[0.9857372,0.005634963,0.0019938555,0.0005901407,0.0020358565,0.004007979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009380065,0.0005788397,0.0009019253,0.0022069928,0.011270844,0.018240353,0.0017458934,0.009761353,0.010389481],"category_scores_gemma":[0.0173225,0.0005843956,0.00066716725,0.0014822996,0.039187133,0.02194678,0.009519666,0.013252046,0.0012369144],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060379716,0.00032418763,0.013708178,0.0005817827,0.000027770231,0.0007556262,0.10070727,0.00037431333,0.0001650105,0.8173304,0.01994527,0.04601982],"study_design_scores_gemma":[0.00006398404,0.00014992956,0.010259708,0.0038068295,0.00006354117,0.00060630654,0.35871682,0.000670133,0.00053467316,0.4462408,0.1787886,0.00009871726],"about_ca_topic_score_codex":0.0149479145,"about_ca_topic_score_gemma":0.02184162,"teacher_disagreement_score":0.018240353,"about_ca_system_score_codex":0.010378975,"about_ca_system_score_gemma":0.021607459,"threshold_uncertainty_score":0.075305045},"labels":[],"label_agreement":null},{"id":"W2294324668","doi":"10.4230/darts.1.1.7","title":"The Love/Hate Relationship with the C Preprocessor: An Interview Study (Artifact)","year":2015,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Artifact (error); Grounded theory; Subject (documents); Preprocessor; Computer science; Process (computing); Psychology; Data science; Information retrieval; Epistemology; Social psychology; Artificial intelligence; World Wide Web; Sociology; Qualitative research; Social science; Philosophy","score_opus":0.047419963778204614,"score_gpt":0.2804762879035224,"score_spread":0.2330563241253178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294324668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9518338,0.00034808557,0.02178251,0.0026801296,0.00019536298,0.0040539205,0.0016398825,0.00016460395,0.017301768],"genre_scores_gemma":[0.93272024,0.0006588093,0.026424812,0.002567004,0.00013127169,0.0071154223,0.00101236,0.00018413743,0.029185865],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9954016,0.0030522833,0.00026432434,0.0004248077,0.0005043008,0.00035258205],"domain_scores_gemma":[0.9793849,0.014729673,0.0007852218,0.0014073636,0.0021732878,0.0015194917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076066325,0.0005827247,0.00045534977,0.0016203267,0.005854904,0.0025117514,0.00091643503,0.002333791,0.0073745255],"category_scores_gemma":[0.026234215,0.0007566008,0.00033802792,0.001497538,0.0029271117,0.0020456577,0.002244937,0.0033437205,0.0026147596],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021355327,0.0011566317,0.01040519,0.0003673245,0.000006470447,0.0018775419,0.93679065,0.00015633833,0.0062866844,0.0033769098,0.012611203,0.026751515],"study_design_scores_gemma":[0.00007689877,0.0016994574,0.029486664,0.00058958784,0.000021310807,0.0024361357,0.80061436,0.0010160115,0.008936293,0.0017532617,0.15321998,0.00015001955],"about_ca_topic_score_codex":0.0056579923,"about_ca_topic_score_gemma":0.008108355,"teacher_disagreement_score":0.0076066325,"about_ca_system_score_codex":0.0026865706,"about_ca_system_score_gemma":0.002944681,"threshold_uncertainty_score":0.040228188},"labels":[],"label_agreement":null},{"id":"W2310854232","doi":"10.1108/er-04-2015-0072","title":"Social media dilemmas in the employment context","year":2016,"lang":"en","type":"article","venue":"Employee Relations","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Social media; Context (archaeology); Public relations; Originality; Labour law; Business; Damages; Employment discrimination; Business ethics; Sociology; Political science; Law","score_opus":0.030344475532973193,"score_gpt":0.2558641484634062,"score_spread":0.225519672930433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310854232","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17068672,0.018117445,0.009461309,0.36880928,0.002229938,0.0002282755,0.00006554726,0.000066425244,0.4303351],"genre_scores_gemma":[0.9517682,0.0046758708,0.0019683195,0.02262246,0.0017064146,0.0002075289,0.00002688687,0.000045323697,0.016978966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.96709454,0.021758271,0.0010302813,0.0018547699,0.004994776,0.0032672947],"domain_scores_gemma":[0.92240447,0.06502235,0.0047778194,0.001892138,0.0028887715,0.0030145026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024304973,0.00054573495,0.00054056157,0.0037588335,0.026705917,0.024874069,0.0033213797,0.016753672,0.010573172],"category_scores_gemma":[0.04559379,0.0006814055,0.0006485104,0.002361445,0.050186794,0.021436261,0.01610505,0.008711327,0.0011046391],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042553882,0.00011707827,0.004480433,0.0002866419,0.000018641313,0.010338375,0.099418946,0.00041082563,0.0003203006,0.83350486,0.016940491,0.03412087],"study_design_scores_gemma":[0.000032844455,0.00007122047,0.00287816,0.0019349348,0.000022523967,0.0061508054,0.24645042,0.00095425255,0.00059504865,0.41744608,0.32337385,0.00008992402],"about_ca_topic_score_codex":0.005813143,"about_ca_topic_score_gemma":0.00647023,"teacher_disagreement_score":0.026705917,"about_ca_system_score_codex":0.0080059245,"about_ca_system_score_gemma":0.006998531,"threshold_uncertainty_score":0.12853849},"labels":[],"label_agreement":null},{"id":"W2317742967","doi":"10.5860/choice.38-6468","title":"The constitutional protection of freedom of expression","year":2001,"lang":"en","type":"article","venue":"Choice Reviews Online","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Freedom of expression; Expression (computer science); Political science; Law; Computer science; Human rights","score_opus":0.04193905577905953,"score_gpt":0.2923387763539352,"score_spread":0.25039972057487564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317742967","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041003167,0.008879715,0.019547554,0.031485148,0.0010564852,0.00009129964,0.0001478966,0.00012767175,0.9345639],"genre_scores_gemma":[0.55621165,0.010490804,0.01480765,0.036810037,0.0024589195,0.0007421715,0.00035651945,0.0003628283,0.37775946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9871955,0.0036702214,0.00061859377,0.0017664985,0.0045603393,0.0021888802],"domain_scores_gemma":[0.9946062,0.0030559818,0.0002567963,0.0009340719,0.0009164728,0.00023044726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006879131,0.00065971236,0.00083588844,0.00088460464,0.0075825574,0.010221773,0.0019340807,0.006446523,0.007948906],"category_scores_gemma":[0.011434913,0.0006233408,0.0008701535,0.0009981095,0.032643218,0.006828611,0.004408468,0.008959903,0.0020777637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031907546,0.0000025800168,0.000031170326,0.000012623084,0.0000018233486,0.000018458175,0.0011091464,0.000054437183,0.000055619443,0.9915798,0.0041195047,0.0030117622],"study_design_scores_gemma":[0.000012792516,0.000021310707,0.00039413947,0.00022190262,0.000012714238,0.00009486237,0.0010880096,0.00023644805,0.00038563414,0.44391388,0.5535839,0.00003431738],"about_ca_topic_score_codex":0.04678576,"about_ca_topic_score_gemma":0.036027305,"teacher_disagreement_score":0.04678576,"about_ca_system_score_codex":0.007123499,"about_ca_system_score_gemma":0.010880897,"threshold_uncertainty_score":0.09302688},"labels":[],"label_agreement":null},{"id":"W2326204440","doi":"10.1177/1461444816639976","title":"Tor, what is it good for? Political repression and the use of online anonymity-granting technologies","year":2016,"lang":"en","type":"article","venue":"New Media & Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for International Governance Innovation","funders":"","keywords":"Anonymity; Politics; The Internet; Government (linguistics); Internet privacy; Civil society; Political science; Sociology; Law and economics; Public relations; Law; Computer science; World Wide Web","score_opus":0.04634509651507258,"score_gpt":0.2733686260530438,"score_spread":0.22702352953797122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326204440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9580779,0.0007107235,0.0014073976,0.0053498144,0.00004790176,0.000012983148,0.00017131392,0.000017036935,0.034204982],"genre_scores_gemma":[0.99897075,0.0001877625,0.00007648366,0.00018900285,0.000022561637,0.0000030485814,0.00003379665,0.000005049024,0.00051164365],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99806935,0.00095234107,0.00009504207,0.00014884467,0.00034122134,0.00039314336],"domain_scores_gemma":[0.9853495,0.0056516924,0.0059476187,0.00091179204,0.0009683209,0.0011710628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019513727,0.000121186604,0.00025957837,0.000942506,0.0015287822,0.004703374,0.00034851523,0.00063528266,0.004328612],"category_scores_gemma":[0.019285707,0.00014473301,0.00016972717,0.0011812701,0.004783344,0.0039454782,0.0013702974,0.0017182283,0.0006307855],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005636013,0.00022160604,0.78891015,0.00027707967,0.00018376499,0.00048219677,0.05941921,0.0008245544,0.0017849935,0.041295048,0.0072656837,0.09877214],"study_design_scores_gemma":[0.000023190829,0.00019096398,0.78748477,0.0003861992,0.00011696328,0.00066357927,0.14935024,0.0022295578,0.0016857875,0.014592961,0.043180034,0.00009581078],"about_ca_topic_score_codex":0.004884812,"about_ca_topic_score_gemma":0.005660845,"teacher_disagreement_score":0.004884812,"about_ca_system_score_codex":0.0008666088,"about_ca_system_score_gemma":0.00052572915,"threshold_uncertainty_score":0.01448065},"labels":[],"label_agreement":null},{"id":"W2341118646","doi":"10.14288/1.0066566","title":"Hate crime law &amp; social contention : a comparison of nongovernmental knowledge practices in Canada &amp; the United States","year":2008,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hate crime; Political science; Criminology; Law; Sociology","score_opus":0.03291386803876073,"score_gpt":0.22797620783272143,"score_spread":0.1950623397939607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341118646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9600368,0.001121324,0.00009944991,0.002766695,0.000028651628,0.00008541577,0.0004462797,0.000015789783,0.035399623],"genre_scores_gemma":[0.9945194,0.0008435563,0.0001195722,0.0004733705,0.0000053127765,0.000027473057,0.00023973022,0.000008396375,0.0037631127],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99555296,0.00039837701,0.00012264769,0.00026221544,0.0016334415,0.0020303128],"domain_scores_gemma":[0.9885888,0.0009656378,0.001134367,0.00021821572,0.004938175,0.004154686],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016913499,0.0002700759,0.00047803813,0.005868058,0.016770938,0.00629625,0.0021373143,0.00095220894,0.0042267824],"category_scores_gemma":[0.006394583,0.00032779112,0.0005356189,0.008244589,0.0050728433,0.0014755228,0.004541091,0.0015102237,0.00020829956],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021871307,0.00029153732,0.5465651,0.00024446848,0.00010263871,0.00095199625,0.3385577,0.00026982964,0.0007299766,0.019413602,0.010465659,0.08218869],"study_design_scores_gemma":[0.00001533982,0.000045440807,0.67937523,0.0002551608,0.00003503439,0.000113436756,0.2963479,0.00029858656,0.00015201201,0.00035841085,0.02295217,0.000051289262],"about_ca_topic_score_codex":0.99638957,"about_ca_topic_score_gemma":0.9984195,"teacher_disagreement_score":0.98322904,"about_ca_system_score_codex":0.105625816,"about_ca_system_score_gemma":0.14522253,"threshold_uncertainty_score":0.7663725},"labels":[],"label_agreement":null},{"id":"W2342703583","doi":"","title":"Politics and Ideology beyond the Law of Free Speech","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"La Cité Collégiale","funders":"","keywords":"Dictatorship; Democracy; Ideology; Distrust; Political science; Nationalism; Politics; Rhetoric; Law; Authoritarianism; Free market; Democratization; Constitutionality; Political economy; Law and economics; Sociology","score_opus":0.004271670832582557,"score_gpt":0.20001992189693338,"score_spread":0.19574825106435081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342703583","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22777669,0.0020754104,0.022966223,0.018516112,0.0004238388,0.000054942182,0.00010143921,0.0000605104,0.72802484],"genre_scores_gemma":[0.98757255,0.00038815755,0.0010078859,0.000815793,0.00017927123,0.000023397179,0.000021478078,0.00001660671,0.009974864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972065,0.0011514232,0.000075348566,0.0003728915,0.00076639914,0.00042738992],"domain_scores_gemma":[0.99393165,0.003798363,0.00076046196,0.0006683041,0.00043419292,0.00040703604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029368529,0.00017807152,0.00025319355,0.0016605154,0.0028871929,0.008297038,0.00034393356,0.0012374965,0.0068212445],"category_scores_gemma":[0.0076874876,0.00017121708,0.00017414503,0.0013846726,0.01811399,0.004517386,0.0018741728,0.0028158352,0.0007270496],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028285223,0.000024025421,0.0057304166,0.000039029106,0.0000064768587,0.00010589953,0.02159363,0.00011335182,0.0006985405,0.9487119,0.0020811355,0.02086741],"study_design_scores_gemma":[0.000031908403,0.000100607016,0.03697118,0.00045506368,0.000024495092,0.00040352545,0.029030021,0.0021040798,0.002339963,0.71374965,0.21474259,0.000046919224],"about_ca_topic_score_codex":0.0021572085,"about_ca_topic_score_gemma":0.0022963535,"teacher_disagreement_score":0.008297038,"about_ca_system_score_codex":0.001933592,"about_ca_system_score_gemma":0.0018154305,"threshold_uncertainty_score":0.02281934},"labels":[],"label_agreement":null},{"id":"W2397216883","doi":"","title":"University Discipline in the Age of Social Media","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Misconduct; Appeal; Harassment; Social media; Charter; Discipline; Autonomy; Political science; Sociology; Law; Public relations","score_opus":0.007469784433806974,"score_gpt":0.20620881436069802,"score_spread":0.19873902992689105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397216883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3996562,0.011385774,0.0045927837,0.20148005,0.0028154529,0.000043359596,0.00005733743,0.0001776939,0.3797914],"genre_scores_gemma":[0.96746236,0.0017892207,0.00043040226,0.014196221,0.0012358598,0.000021540276,0.000012527863,0.000032600714,0.014819206],"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98789376,0.0058341394,0.00030029414,0.0006850418,0.0026079705,0.002678852],"domain_scores_gemma":[0.9675478,0.013990405,0.005462642,0.0015752044,0.0024884203,0.008935574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008724955,0.00025225454,0.00038732047,0.0025411788,0.024400376,0.02227809,0.0015559774,0.007558808,0.005187842],"category_scores_gemma":[0.028461441,0.0004292222,0.0003010873,0.0023082364,0.028793719,0.013474081,0.014659005,0.008392667,0.0009768519],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007637148,0.00010281905,0.02778523,0.00008531822,0.000015833375,0.001962105,0.45813173,0.00017764268,0.00045486997,0.37340388,0.044971764,0.09283256],"study_design_scores_gemma":[0.00001167167,0.00012464632,0.02620171,0.0004448093,0.000014867954,0.0015169933,0.41198286,0.00029090876,0.0005270365,0.06905315,0.48973918,0.000092177215],"about_ca_topic_score_codex":0.020775551,"about_ca_topic_score_gemma":0.05147061,"teacher_disagreement_score":0.024400376,"about_ca_system_score_codex":0.010185635,"about_ca_system_score_gemma":0.011071049,"threshold_uncertainty_score":0.07390225},"labels":[],"label_agreement":null},{"id":"W2484642270","doi":"10.2495/dne-v11-n3-406-415","title":"Cyber hate speech on twitter: Analyzing disruptive events from social media to build a violent communication and hate speech taxonomy","year":2016,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad","keywords":"Social media; Taxonomy (biology); Computer science; Psychology; Computer security; Internet privacy; Communication; World Wide Web","score_opus":0.017680215513076823,"score_gpt":0.2619026129296643,"score_spread":0.2442223974165875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484642270","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97549,0.0003787965,0.012483399,0.0005187897,0.0000636361,0.0007368789,0.0040588393,0.00013088099,0.0061388244],"genre_scores_gemma":[0.96077013,0.0004608438,0.0298716,0.000090666115,0.00008821718,0.0007092859,0.005611909,0.000029540943,0.0023678008],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989718,0.00025971272,0.00018327576,0.00011589787,0.000312722,0.00015657417],"domain_scores_gemma":[0.99625164,0.0018478736,0.00073573715,0.0001690729,0.0007790953,0.00021664922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013858224,0.0004244399,0.0003854355,0.008511159,0.0009664144,0.0018052069,0.00044618585,0.00067119754,0.0010448059],"category_scores_gemma":[0.004250924,0.00018093093,0.00054212706,0.0047045816,0.00060177996,0.0018322808,0.001434161,0.00059225253,0.0006787983],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056734827,0.0005680369,0.7474133,0.0015782294,0.00017039514,0.001738183,0.038345303,0.0030073894,0.027120415,0.0044281264,0.0054524783,0.16961083],"study_design_scores_gemma":[0.000017920078,0.00025130322,0.873772,0.00031343687,0.00011047956,0.00082891673,0.05981032,0.03639305,0.007848389,0.0024748298,0.018074827,0.000104462764],"about_ca_topic_score_codex":0.0069431737,"about_ca_topic_score_gemma":0.008882099,"teacher_disagreement_score":0.008511159,"about_ca_system_score_codex":0.0009198994,"about_ca_system_score_gemma":0.0007857123,"threshold_uncertainty_score":0.013805509},"labels":[],"label_agreement":null},{"id":"W2489259107","doi":"10.4018/978-1-59140-789-8.ch058","title":"Digital Morality and Ethics","year":2007,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Morality; Information ethics; Ethics of technology; Engineering ethics; Computer ethics; Political science; Government (linguistics); Democracy; Compliance (psychology); Sociology; Environmental ethics; Meta-ethics; Psychology; Social psychology; Engineering; Law; Politics; Philosophy","score_opus":0.04195936875614188,"score_gpt":0.27739521126786115,"score_spread":0.23543584251171928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489259107","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003021017,0.01970506,0.00833665,0.021652466,0.0007905287,0.00004323991,0.0000573344,0.000045583205,0.94634813],"genre_scores_gemma":[0.45277348,0.045970947,0.014349893,0.017715199,0.0030255413,0.0003325208,0.00022099356,0.00016995685,0.46544147],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980597,0.0009910393,0.00006791637,0.00022497839,0.0005003154,0.0001560197],"domain_scores_gemma":[0.99897987,0.00056201266,0.000073444375,0.00014893785,0.00013845011,0.00009722342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015211529,0.00050126074,0.0002851175,0.0009508581,0.0026484344,0.008240208,0.0006132895,0.003438401,0.0099313725],"category_scores_gemma":[0.0025671571,0.00019749718,0.00022708667,0.00087512,0.01804142,0.006214721,0.0027541483,0.0044234386,0.0024807623],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011544196,0.000005024374,0.000033310407,0.000027252425,9.758882e-7,0.000014498631,0.00066080724,0.00009113566,0.000029533207,0.9843635,0.0073416727,0.007430967],"study_design_scores_gemma":[0.0000028473985,0.00000601442,0.00013791854,0.00021684717,0.000001561234,0.00008432763,0.0006210708,0.0001760031,0.00007796115,0.5557655,0.44290456,0.0000053453214],"about_ca_topic_score_codex":0.0016722262,"about_ca_topic_score_gemma":0.0013075585,"teacher_disagreement_score":0.0099313725,"about_ca_system_score_codex":0.0039710756,"about_ca_system_score_gemma":0.0025259482,"threshold_uncertainty_score":0.033223808},"labels":[],"label_agreement":null},{"id":"W2491742414","doi":"","title":"The Canadian Armed Forces “YouTube War”: A Cross-Border Military-Social Media Complex","year":2015,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Media studies; Political science; Computer science; Sociology; World Wide Web","score_opus":0.23554941057237938,"score_gpt":0.5461186129988205,"score_spread":0.3105692024264411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491742414","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28521213,0.014868191,0.007306343,0.107760094,0.0013525679,0.00043387202,0.003190335,0.00031681574,0.5795597],"genre_scores_gemma":[0.9423902,0.0076486147,0.0032085413,0.0070287543,0.0003457151,0.00018139146,0.00095864554,0.00015465597,0.03808354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9962703,0.0007289115,0.00009501781,0.00039334688,0.0015687085,0.0009436918],"domain_scores_gemma":[0.9949759,0.0015978833,0.000381839,0.0002330994,0.0020441439,0.0007671617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003137492,0.000748896,0.00035001623,0.0060430607,0.025513269,0.020947061,0.0015988316,0.0019295352,0.010199942],"category_scores_gemma":[0.006767671,0.00049507775,0.000359797,0.010617836,0.015938183,0.008092953,0.0065701543,0.0029225743,0.00067997765],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087593915,0.000052722186,0.02904751,0.00053006,0.00004974229,0.0023510347,0.26354608,0.0007944393,0.001752464,0.4978981,0.10280905,0.1010813],"study_design_scores_gemma":[0.000007712393,0.000021204462,0.034304596,0.00064547494,0.000037565038,0.0004417668,0.33742586,0.0008602304,0.00058758416,0.008548627,0.61701953,0.00009980449],"about_ca_topic_score_codex":0.9654866,"about_ca_topic_score_gemma":0.9775249,"teacher_disagreement_score":0.07795325,"about_ca_system_score_codex":0.07795325,"about_ca_system_score_gemma":0.07441594,"threshold_uncertainty_score":0.56559306},"labels":[],"label_agreement":null},{"id":"W2500273366","doi":"10.1017/cbo9780511740084.012","title":"Against FocusP: arguments from Zulu","year":2012,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":230,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Zulu; Philosophy; History; Linguistics","score_opus":0.017143474110843476,"score_gpt":0.18190809636362706,"score_spread":0.1647646222527836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500273366","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03872615,0.005046278,0.03633274,0.02626826,0.00075876625,0.000068863425,0.00024088775,0.00044062125,0.89211744],"genre_scores_gemma":[0.97164786,0.0009876397,0.0038443722,0.002821894,0.00032825008,0.00013143157,0.00012996356,0.00025865866,0.019849893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969406,0.0015958411,0.00008998913,0.0005027709,0.00043689698,0.00043392924],"domain_scores_gemma":[0.99749607,0.0018165048,0.00013850139,0.00028774422,0.00019569809,0.0000654001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034352492,0.0012889777,0.000958864,0.002036892,0.006030417,0.0057729315,0.0014267504,0.0067134914,0.015229673],"category_scores_gemma":[0.011347887,0.00081295555,0.0006068878,0.001465281,0.010335364,0.015513396,0.0064172056,0.0058605415,0.0022022463],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026595944,0.0000043232817,0.000074676645,0.00005678917,0.0000040158657,0.00012359921,0.0032761998,0.000046521127,0.00016806509,0.98927677,0.0028792666,0.0040631555],"study_design_scores_gemma":[0.00004281709,0.000020971045,0.00061253976,0.0003599219,0.000028571447,0.00039071555,0.0050836257,0.0013930575,0.001410534,0.86774,0.122878276,0.000038995597],"about_ca_topic_score_codex":0.003991789,"about_ca_topic_score_gemma":0.0033940147,"teacher_disagreement_score":0.015229673,"about_ca_system_score_codex":0.0036349692,"about_ca_system_score_gemma":0.0007575423,"threshold_uncertainty_score":0.05094832},"labels":[],"label_agreement":null},{"id":"W2504171395","doi":"10.1093/acprof:oso/9780199548781.003.0012","title":"Incitement and the Regulation of Hate Speech in Canada: A Philosophical Analysis","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Free speech; Incitement; Legislation; First amendment; Supreme court; Democracy; Law; Mill; Political science; Marketplace of ideas; Operationalization; Sociology; Law and economics; Epistemology; History; Philosophy; Politics","score_opus":0.010746809607856508,"score_gpt":0.19565692664440598,"score_spread":0.18491011703654947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504171395","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045615405,0.013052777,0.011693641,0.05039324,0.00028972345,0.00006753012,0.00016313628,0.000036959435,0.8786875],"genre_scores_gemma":[0.9173432,0.007076854,0.0028853507,0.0050550653,0.00014003374,0.00004536861,0.000045147626,0.000031399682,0.06737754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977508,0.00038998487,0.000033497996,0.0001938369,0.0010599251,0.0005719047],"domain_scores_gemma":[0.9978192,0.0008458339,0.000105078565,0.00007095442,0.0009540657,0.00020492903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019554798,0.00039806715,0.00037489441,0.0024051436,0.014657373,0.008489749,0.0015843547,0.0034318047,0.005348971],"category_scores_gemma":[0.002570457,0.0002883927,0.00034333358,0.0022384843,0.026286712,0.0021978929,0.0017002878,0.003982041,0.0003070204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001883358,0.0000036428428,0.00018761605,0.000015118587,0.000001705228,0.000054250697,0.005480577,0.0002754841,0.000060683946,0.98843354,0.0030312967,0.0024542748],"study_design_scores_gemma":[0.000015089517,0.000016299391,0.0067795003,0.0006908696,0.000038552243,0.00021084362,0.030719154,0.006443688,0.00068075047,0.5311349,0.42314723,0.00012317355],"about_ca_topic_score_codex":0.97310966,"about_ca_topic_score_gemma":0.9747595,"teacher_disagreement_score":0.12718405,"about_ca_system_score_codex":0.12718405,"about_ca_system_score_gemma":0.08468893,"threshold_uncertainty_score":0.92278916},"labels":[],"label_agreement":null},{"id":"W2520136630","doi":"10.15375/zbb-2015-0306","title":"Risiko-Warnhinweise in Kurzinformationen für Finanzdienstleistungen. Eine empirische Analyse","year":2015,"lang":"de","type":"article","venue":"Zeitschrift für Bankrecht und Bankwirtschaft","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Political science; Gynecology; Humanities; Art; Medicine","score_opus":0.03267351193875151,"score_gpt":0.318469550810912,"score_spread":0.2857960388721605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520136630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87102866,0.05690214,0.01251096,0.008366803,0.0009078221,0.0021524574,0.011562127,0.00011019277,0.03645883],"genre_scores_gemma":[0.97143966,0.009426273,0.003803704,0.0016646908,0.00029069278,0.0025842146,0.0020824478,0.000061429695,0.008646901],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9804303,0.011993686,0.0018116497,0.0021301487,0.0029587727,0.0006754485],"domain_scores_gemma":[0.7856846,0.18052489,0.021884814,0.0036239105,0.006722213,0.0015596134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020994773,0.0008168607,0.0013105808,0.0026715065,0.0013527032,0.004787545,0.0012017688,0.0019243924,0.03500164],"category_scores_gemma":[0.124523304,0.00071588473,0.0036209887,0.00337509,0.0018477513,0.0042305165,0.00285999,0.0039423443,0.002548818],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016785186,0.0044347323,0.48964867,0.038697653,0.011412157,0.0016691977,0.047646277,0.008487636,0.0021313315,0.056055248,0.014413577,0.30861825],"study_design_scores_gemma":[0.0010554392,0.008652657,0.78059965,0.015111402,0.01665101,0.0010612484,0.030164804,0.0064367745,0.004412819,0.0494144,0.0859866,0.00045321617],"about_ca_topic_score_codex":0.005330213,"about_ca_topic_score_gemma":0.0039207838,"teacher_disagreement_score":0.03500164,"about_ca_system_score_codex":0.00330343,"about_ca_system_score_gemma":0.0025640188,"threshold_uncertainty_score":0.11709213},"labels":[],"label_agreement":null},{"id":"W252891807","doi":"","title":"The Attack on Human Rights Commissions and the Decline of Public Discourse","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Contempt; Human rights; Commission; Hatred; Phone; Political science; Race (biology); Law; The Internet; Criminology; Sociology; Politics; Gender studies","score_opus":0.019917942096267554,"score_gpt":0.30220842392417807,"score_spread":0.2822904818279105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W252891807","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019476026,0.006012234,0.001050781,0.8678822,0.0032628775,0.000039243758,0.00015507122,0.000108577275,0.10201307],"genre_scores_gemma":[0.56571496,0.0045264075,0.0009617523,0.33486533,0.0039845663,0.000109997985,0.00018767941,0.00020371318,0.08944569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9683194,0.0065610204,0.0005765189,0.0027578652,0.012569918,0.009215351],"domain_scores_gemma":[0.9251048,0.040014673,0.0061962516,0.003420499,0.013354172,0.011909562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038431015,0.0007928038,0.0011586596,0.005163771,0.037432056,0.027611613,0.003730827,0.028946714,0.014325863],"category_scores_gemma":[0.08214556,0.0012441445,0.0008577511,0.005455496,0.054527596,0.014691446,0.010272665,0.03579548,0.001558076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038815644,0.000040831233,0.0015114094,0.00005851979,0.000010822037,0.00023256321,0.03330665,0.000115816605,0.0002066904,0.6811528,0.2624568,0.020868279],"study_design_scores_gemma":[0.00003530321,0.000016333355,0.003341524,0.00025819032,0.000008757677,0.00008481019,0.01776083,0.0003001697,0.0002905496,0.038177405,0.9396376,0.00008855736],"about_ca_topic_score_codex":0.71782386,"about_ca_topic_score_gemma":0.62254524,"teacher_disagreement_score":0.71782386,"about_ca_system_score_codex":0.08648906,"about_ca_system_score_gemma":0.08909809,"threshold_uncertainty_score":0.627525},"labels":[],"label_agreement":null},{"id":"W2565870272","doi":"","title":"The Metalinguistic Dilemma of Freedom of Expression: Its Limits","year":2016,"lang":"en","type":"article","venue":"Journal of political studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Human rights; Law; Declaration; Parliament; Fundamental rights; Bill of rights; Right to property; Sociology; Political science; Politics","score_opus":0.04411621606295145,"score_gpt":0.30566497693425426,"score_spread":0.2615487608713028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565870272","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015026598,0.029687455,0.030220827,0.14972751,0.0017157142,0.000046253426,0.00016840888,0.00015121693,0.773256],"genre_scores_gemma":[0.8900117,0.010012759,0.011375277,0.025658723,0.0043036384,0.0003042706,0.00015465838,0.0003782165,0.05780073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9866412,0.0068365415,0.00048916956,0.0019444649,0.0031076106,0.0009810195],"domain_scores_gemma":[0.9834885,0.012230719,0.00058385317,0.0018032701,0.0013729419,0.00052076986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010869919,0.0007771542,0.0012851371,0.0016512477,0.007751795,0.014408537,0.0029995735,0.007897763,0.009827536],"category_scores_gemma":[0.017007282,0.0006265622,0.0006818863,0.0014027816,0.07869555,0.024376364,0.009210035,0.011300416,0.0022529317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005783341,0.0000026373375,0.000019089603,0.000018564335,0.000001139519,0.000021031594,0.0016091348,0.000037400125,0.000023131333,0.9948015,0.0019187251,0.0015418596],"study_design_scores_gemma":[0.000005671539,0.000003709309,0.00004492853,0.000081045226,0.0000017142439,0.00007358541,0.00044389052,0.00012509305,0.00005138305,0.96402895,0.035131697,0.000008411179],"about_ca_topic_score_codex":0.0045319153,"about_ca_topic_score_gemma":0.002233173,"teacher_disagreement_score":0.014408537,"about_ca_system_score_codex":0.008488634,"about_ca_system_score_gemma":0.0037471822,"threshold_uncertainty_score":0.06158966},"labels":[],"label_agreement":null},{"id":"W2571582334","doi":"","title":"The Media Education Policy and the Background in Canada : As a Clue Which Makes Contradiction of Youth Protect Regulation Clear(III. Research Papers)","year":2001,"lang":"en","type":"article","venue":"日本教育政策学会年報","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Contradiction; Political science; Media studies; Public relations; Sociology; Epistemology; Philosophy","score_opus":0.021971311583783192,"score_gpt":0.2705372307910433,"score_spread":0.24856591920726012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571582334","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18621354,0.028635563,0.0022429256,0.47823307,0.002591321,0.00011893839,0.0024265454,0.00011646697,0.29942158],"genre_scores_gemma":[0.8754621,0.011784696,0.00095283415,0.038939618,0.00049667404,0.000050603576,0.00027233845,0.000067542154,0.07197355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99607736,0.00037950036,0.00006866567,0.00024079485,0.0011996201,0.002034098],"domain_scores_gemma":[0.99253535,0.0018725746,0.00050403696,0.00016486194,0.002700002,0.0022231417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026635868,0.00028287803,0.00045219646,0.0019897798,0.01660536,0.014821062,0.0016871738,0.004099997,0.010472051],"category_scores_gemma":[0.008932659,0.00031883043,0.000391911,0.0037303502,0.009880214,0.003051893,0.0025592016,0.0056597902,0.00042448705],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012374387,0.000109267545,0.04340443,0.00050305366,0.000057832058,0.00059319276,0.03375649,0.00072793534,0.0006508713,0.7053782,0.15615107,0.05854398],"study_design_scores_gemma":[0.000053636493,0.00006479675,0.122875445,0.0018672235,0.00015863123,0.00017273254,0.10428069,0.00090239797,0.0022355574,0.03697357,0.7302332,0.00018202674],"about_ca_topic_score_codex":0.9956204,"about_ca_topic_score_gemma":0.9977545,"teacher_disagreement_score":0.876779,"about_ca_system_score_codex":0.12322097,"about_ca_system_score_gemma":0.27255177,"threshold_uncertainty_score":0.8940349},"labels":[],"label_agreement":null},{"id":"W2579619295","doi":"10.1068/d2206a","title":"Getting Warmer? Some Reactions to <i>Fahrenheit 9/11</i>","year":2004,"lang":"en","type":"article","venue":"Environment and Planning D Society and Space","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Astrobiology; Physics","score_opus":0.009854985947422273,"score_gpt":0.2068033954996031,"score_spread":0.19694840955218085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579619295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60079247,0.005002112,0.00087001635,0.25182167,0.009874277,0.000075552256,0.00033024835,0.00009419863,0.13113941],"genre_scores_gemma":[0.92930424,0.0017932436,0.00025368415,0.03154543,0.000896099,0.000058954112,0.00009467495,0.00010535617,0.035948373],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99700016,0.0014728333,0.00010676253,0.00017559159,0.00052761525,0.0007170278],"domain_scores_gemma":[0.9943041,0.0019671577,0.00084964995,0.0003068759,0.0011820901,0.0013900407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004184,0.00034990514,0.00024590135,0.0008605004,0.011160536,0.005603438,0.000820265,0.004323337,0.009125335],"category_scores_gemma":[0.014910838,0.0003964393,0.0004828769,0.0007838218,0.005579137,0.003224286,0.002801084,0.0073913937,0.0014346644],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004069622,0.0002094688,0.049718287,0.0001899296,0.000065737644,0.002636029,0.7145902,0.00011123807,0.001775542,0.022604281,0.16241114,0.045281246],"study_design_scores_gemma":[0.000013995931,0.00014879699,0.057140373,0.00024152457,0.000018814877,0.00082448917,0.7131139,0.000070457325,0.00050465204,0.001547109,0.22629899,0.00007689244],"about_ca_topic_score_codex":0.03152456,"about_ca_topic_score_gemma":0.052410986,"teacher_disagreement_score":0.03152456,"about_ca_system_score_codex":0.0029628682,"about_ca_system_score_gemma":0.0011022278,"threshold_uncertainty_score":0.06268215},"labels":[],"label_agreement":null},{"id":"W2582601590","doi":"10.1017/s0008423916001219","title":"Finding the Harm in Hate Speech: An Argument against Censorship","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Political Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Censorship; Harm; Harm principle; Argument (complex analysis); Dignity; Fallacy; Offensive; Mill; Law and economics; Law; Political science; Sociology; Epistemology; Philosophy; Economics; History","score_opus":0.04222381877679681,"score_gpt":0.293918831351188,"score_spread":0.2516950125743912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582601590","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22649398,0.003633264,0.17528899,0.25379354,0.001512755,0.00017512738,0.00016741925,0.000119318975,0.3388156],"genre_scores_gemma":[0.9837446,0.0002962924,0.0034410239,0.007187612,0.00036086474,0.000047463374,0.0000124553435,0.000012865531,0.0048967996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9800061,0.010907346,0.00066146016,0.0019169594,0.0053211744,0.0011870185],"domain_scores_gemma":[0.8225523,0.14877917,0.0090948595,0.010257844,0.0071988916,0.0021169498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0235213,0.00038974744,0.00089775753,0.003288683,0.0058767684,0.009257419,0.0021749993,0.011625747,0.004006817],"category_scores_gemma":[0.11559081,0.00041586527,0.0005702741,0.0015350743,0.04459139,0.011764198,0.005772595,0.011045694,0.0005200005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051381096,0.00003621737,0.0030718269,0.00005659553,0.000017570892,0.00028859,0.004338083,0.0004216166,0.00020079273,0.9781342,0.0037850027,0.009598082],"study_design_scores_gemma":[0.000025926192,0.00003371997,0.0017184282,0.00026607403,0.0000296456,0.0003487703,0.002858325,0.0027038758,0.0011010806,0.9733778,0.017491598,0.000044663524],"about_ca_topic_score_codex":0.0025704938,"about_ca_topic_score_gemma":0.0020661918,"teacher_disagreement_score":0.9974295,"about_ca_system_score_codex":0.0033917814,"about_ca_system_score_gemma":0.001831769,"threshold_uncertainty_score":0.12439406},"labels":[],"label_agreement":null},{"id":"W2587939649","doi":"","title":"\"Crazy B****\": Discriminatory Language, Radio Censorship, Regulation, and Enforcement Policies in Canada","year":2016,"lang":"en","type":"dissertation","venue":"YorkSpace (York University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Censorship; Enforcement; Political science; Law enforcement; Computer security; Business; Computer science; Law","score_opus":0.005525343225973517,"score_gpt":0.18331692246026726,"score_spread":0.17779157923429376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587939649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.954444,0.0023843716,0.00047053036,0.0047777495,0.0000578688,0.00007127854,0.005079267,0.00003060423,0.032684386],"genre_scores_gemma":[0.99145776,0.0013373685,0.0002741426,0.00028935578,0.000012235869,0.000015807065,0.0014086099,0.000014058962,0.0051906393],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9974195,0.00018422736,0.00008850763,0.00024220916,0.00120961,0.000856096],"domain_scores_gemma":[0.99143666,0.0010393113,0.0012933058,0.0002969337,0.0042699915,0.001663719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015926728,0.00019792284,0.00036796578,0.0029196008,0.006323868,0.003998109,0.0009995356,0.00039002637,0.00315396],"category_scores_gemma":[0.010164111,0.0002025647,0.00032176092,0.0070523066,0.002873599,0.0008450965,0.0015423049,0.001099119,0.00020872203],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016988559,0.000063396845,0.9005356,0.00017976448,0.000097504526,0.000220301,0.01803982,0.001102197,0.0004851822,0.009214288,0.016381174,0.053510893],"study_design_scores_gemma":[0.000007243381,0.000020558751,0.95813787,0.0001610418,0.00003234438,0.000037190144,0.023565635,0.0007047882,0.00021939521,0.0002975721,0.01678087,0.000035569185],"about_ca_topic_score_codex":0.99828124,"about_ca_topic_score_gemma":0.9990978,"teacher_disagreement_score":0.068075836,"about_ca_system_score_codex":0.068075836,"about_ca_system_score_gemma":0.098587744,"threshold_uncertainty_score":0.49392706},"labels":[],"label_agreement":null},{"id":"W2596163002","doi":"","title":"Turning Worms: Some Thoughts on Liabilities for Spreading Computer Infections","year":2004,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Forensic engineering; Computer security; Geology; Computer science; Mining engineering; Internet privacy; Engineering","score_opus":0.012660274857466123,"score_gpt":0.23922287522312705,"score_spread":0.22656260036566092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596163002","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020702682,0.022962997,0.03284004,0.5005136,0.0027691352,0.00013596237,0.00014117747,0.00016649436,0.4197679],"genre_scores_gemma":[0.8013317,0.022672318,0.007744523,0.08443964,0.007772255,0.00034663986,0.000090826405,0.000124516,0.07547757],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961582,0.0017863656,0.00012390767,0.00038707268,0.0008852572,0.0006592204],"domain_scores_gemma":[0.9901992,0.0071435655,0.000683749,0.0004908392,0.0010785556,0.0004041436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065629156,0.0006217533,0.00058194774,0.0034893882,0.00834597,0.010855332,0.0031713303,0.020228062,0.00926444],"category_scores_gemma":[0.02456647,0.0004499187,0.0012972893,0.0014473482,0.03479986,0.020988114,0.003428579,0.012563168,0.0011938717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000440963,0.000009905728,0.00025041902,0.000020462545,0.0000039075207,0.00025654217,0.0012113336,0.0003593743,0.000033514927,0.9857466,0.009246306,0.0028572702],"study_design_scores_gemma":[0.0000070217566,0.000018766303,0.0002864175,0.00017680992,0.000008478988,0.00026184818,0.001862932,0.0010736324,0.000091868555,0.9434047,0.052788008,0.00001959041],"about_ca_topic_score_codex":0.014252199,"about_ca_topic_score_gemma":0.008107174,"teacher_disagreement_score":0.020228062,"about_ca_system_score_codex":0.006586707,"about_ca_system_score_gemma":0.0024903598,"threshold_uncertainty_score":0.04779011},"labels":[],"label_agreement":null},{"id":"W2597006171","doi":"10.29173/cais945","title":"Readability of Informed Consent Forms: Analysis and Recommendations for Development of Consent Forms for Use with Communities with Limited or Low Literacy","year":2016,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Readability; Informed consent; Psychology; Literacy; Humanities; Computer science; Medicine; Art; Pedagogy; Alternative medicine","score_opus":0.0675056919160336,"score_gpt":0.29246875681525497,"score_spread":0.22496306489922135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597006171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082794614,0.0033249166,0.49117678,0.037332132,0.0023378923,0.30112723,0.01176946,0.007412488,0.06272452],"genre_scores_gemma":[0.0989669,0.0026923786,0.6811894,0.0036337338,0.0004661908,0.19633378,0.005443884,0.0014315044,0.0098421285],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.5011044,0.37281832,0.08251848,0.004365455,0.03535675,0.0038366443],"domain_scores_gemma":[0.2438306,0.5137668,0.043793697,0.06869329,0.12658982,0.0033257813],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.43899104,0.0013881816,0.0014445735,0.011000323,0.00429399,0.0069220196,0.003559154,0.0034254554,0.020476889],"category_scores_gemma":[0.67990285,0.0019856044,0.0023327598,0.007312124,0.0044972245,0.0082948925,0.0044573764,0.005490725,0.011949906],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014667674,0.001161224,0.022382423,0.00854693,0.00012728623,0.0010956452,0.08015725,0.0010942413,0.010872926,0.019725293,0.1362627,0.7171073],"study_design_scores_gemma":[0.0014316324,0.0028955399,0.07050945,0.03143734,0.00028228178,0.002631354,0.06998666,0.011468285,0.037807673,0.036176365,0.7343053,0.0010681648],"about_ca_topic_score_codex":0.0042985817,"about_ca_topic_score_gemma":0.0062285457,"teacher_disagreement_score":0.56100893,"about_ca_system_score_codex":0.0053376635,"about_ca_system_score_gemma":0.024749486,"threshold_uncertainty_score":0.69182396},"labels":[],"label_agreement":null},{"id":"W2602596225","doi":"10.1108/jfp-02-2017-0003","title":"Examining the portrayal of homophobic and non-homophobic aggression in print media through an integrated grounded behavioural linguistic inquiry (IGBLI) approach","year":2017,"lang":"en","type":"article","venue":"Journal of Forensic Practice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Newspaper; Originality; Aggression; Content analysis; Psychology; Representation (politics); Grounded theory; Social media; Social psychology; Sociology; Media studies; Social science; Qualitative research; Computer science; Political science; Creativity","score_opus":0.06930138823125936,"score_gpt":0.31399164635645965,"score_spread":0.2446902581252003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602596225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9728756,0.00036420132,0.0052531236,0.0009729562,0.000040594372,0.00047398743,0.0001854218,0.000023978238,0.019810125],"genre_scores_gemma":[0.9850995,0.0006041415,0.00990383,0.0004549398,0.000022983091,0.00064670655,0.00008843519,0.000014595252,0.003164797],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99467826,0.0038952841,0.0002481132,0.00022184903,0.0007540644,0.00020258444],"domain_scores_gemma":[0.98551184,0.010103522,0.002514825,0.0005000874,0.0011402852,0.00022941775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005969178,0.00033623783,0.00027976758,0.004291516,0.002368499,0.003234098,0.00073839794,0.0008141797,0.002656111],"category_scores_gemma":[0.015162851,0.0002885352,0.0002477201,0.0019830945,0.004383604,0.0022941302,0.0032559063,0.0010058613,0.00022538647],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110723144,0.00026819482,0.044348445,0.0009090019,0.00003537487,0.00067624764,0.8761363,0.00015613037,0.004420299,0.00847649,0.00084674294,0.06361615],"study_design_scores_gemma":[0.000009456392,0.0001862536,0.061329816,0.0006755428,0.000035363148,0.00037754737,0.9251148,0.00041667515,0.0015957922,0.002825796,0.007401016,0.0000320657],"about_ca_topic_score_codex":0.009139713,"about_ca_topic_score_gemma":0.017706633,"teacher_disagreement_score":0.009139713,"about_ca_system_score_codex":0.0028554804,"about_ca_system_score_gemma":0.0017889217,"threshold_uncertainty_score":0.031568408},"labels":[],"label_agreement":null},{"id":"W2675729201","doi":"","title":"Law as an Ally or Enemy in the War on Cyberbullying: Exploring the Contested Terrain of Privacy and Other Legal Concepts in the Age of Technology and Social Media","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Adversary; Political science; Law; Government (linguistics); The Right to Privacy; Sociology; Law and economics; Computer security; Human rights; Computer science","score_opus":0.040433504423501854,"score_gpt":0.28367808145624696,"score_spread":0.2432445770327451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2675729201","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3701821,0.009734502,0.011508712,0.20576982,0.0007009605,0.00012694733,0.00008178877,0.000033365326,0.40186176],"genre_scores_gemma":[0.98454106,0.0029361597,0.0009406173,0.0051004733,0.00016770668,0.000045894692,0.000013277708,0.000020166884,0.0062346137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99050725,0.005877743,0.0001555133,0.0004178624,0.0012545058,0.0017872087],"domain_scores_gemma":[0.9783604,0.018330729,0.0009660379,0.00052074867,0.00086790946,0.0009541284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010713036,0.0004210321,0.0006993852,0.0031580164,0.022692215,0.02417446,0.0022968168,0.009706153,0.0036495968],"category_scores_gemma":[0.018572917,0.00051750155,0.0004562709,0.0025478739,0.08681255,0.022202507,0.010499148,0.010067707,0.000265936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000122040365,0.00004412296,0.0028697862,0.00003739157,0.0000045003744,0.00060921133,0.13513191,0.00019137938,0.00010667868,0.8508819,0.0020463436,0.008064596],"study_design_scores_gemma":[0.0000106198095,0.000044511784,0.0059030773,0.00069280143,0.000018928753,0.00053895515,0.62267166,0.0008452877,0.00043062007,0.29098517,0.07780848,0.0000499604],"about_ca_topic_score_codex":0.13665943,"about_ca_topic_score_gemma":0.20436895,"teacher_disagreement_score":0.13665943,"about_ca_system_score_codex":0.017288702,"about_ca_system_score_gemma":0.020158172,"threshold_uncertainty_score":0.27172798},"labels":[],"label_agreement":null},{"id":"W2730454633","doi":"10.1177/1948550617714586","title":"Honest People Tend to Use Less—Not More—Profanity","year":2017,"lang":"en","type":"article","venue":"Social Psychological and Personality Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Calgary","funders":"","keywords":"Impression management; Psychology; Honesty; Impression formation; Cheating; Social psychology; Dishonesty; Trait; Personality; Impression; Scale (ratio); Relation (database); Big Five personality traits; Perception; Social perception","score_opus":0.12001021279385923,"score_gpt":0.37316299833979727,"score_spread":0.253152785545938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730454633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99452025,0.000117573436,0.0005556266,0.00024894736,0.000019160107,0.000012979555,0.000036734833,0.000009626454,0.0044791573],"genre_scores_gemma":[0.9985544,0.00008475662,0.00032391874,0.00020835329,0.000030217476,0.000008628882,0.000041849235,0.000005219884,0.00074250065],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986981,0.00041041983,0.00014546525,0.00018991235,0.00044119536,0.000114849485],"domain_scores_gemma":[0.9638728,0.013285195,0.01732874,0.0024883305,0.001888625,0.0011363799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017419014,0.00021574732,0.0002998121,0.0008467666,0.0006643191,0.0013830358,0.0002007347,0.0005544616,0.0033150055],"category_scores_gemma":[0.01643378,0.00019935849,0.00021694331,0.0004179153,0.0011718671,0.00087085227,0.0007696468,0.00082863006,0.0005138186],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003088643,0.00020700664,0.93559706,0.00015718225,0.00019850457,0.00024222286,0.014029839,0.00010910565,0.011324675,0.0011210798,0.0007002771,0.03600417],"study_design_scores_gemma":[0.000008597802,0.00017883789,0.9911696,0.00003215224,0.000045517652,0.00062573195,0.004578167,0.00021723707,0.0012192348,0.0007282067,0.0011711895,0.000025482927],"about_ca_topic_score_codex":0.00083912484,"about_ca_topic_score_gemma":0.0011248677,"teacher_disagreement_score":0.0033150055,"about_ca_system_score_codex":0.00019372674,"about_ca_system_score_gemma":0.00013701637,"threshold_uncertainty_score":0.011089802},"labels":[],"label_agreement":null},{"id":"W2740895812","doi":"10.18653/v1/w17-3002","title":"Constructive Language in News Comments","year":2017,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Constructive; Argumentation theory; Computer science; Relation (database); Natural language processing; Artificial intelligence; Linguistics; Programming language; Process (computing); Data mining; Philosophy","score_opus":0.011898486649955993,"score_gpt":0.2695451822500218,"score_spread":0.25764669560006576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740895812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80044574,0.0042332793,0.12182633,0.002817109,0.00078048295,0.0006619263,0.0050939913,0.0025418103,0.06159944],"genre_scores_gemma":[0.95818734,0.00072815793,0.028832335,0.00034307866,0.0004310726,0.00021891703,0.0036654416,0.00023724812,0.007356393],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9946683,0.002197241,0.0004632523,0.0006428013,0.0016873024,0.00034110705],"domain_scores_gemma":[0.9503628,0.027558647,0.008379285,0.0019202788,0.010745773,0.001033228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003096567,0.0009234639,0.00036026412,0.006165371,0.0016051342,0.0029227661,0.0006797146,0.0010933111,0.0036016346],"category_scores_gemma":[0.0312969,0.00033882548,0.00048579698,0.003439004,0.0016308632,0.0035636197,0.0018353787,0.0013290555,0.0015736117],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016915468,0.00047497213,0.2548924,0.0039980095,0.00022927884,0.005321382,0.0629494,0.007299657,0.05758048,0.051443573,0.030441834,0.5236774],"study_design_scores_gemma":[0.000121020326,0.000628182,0.3402258,0.0023899798,0.00033090636,0.011567867,0.0499486,0.117084965,0.06690895,0.049622335,0.36062425,0.00054705876],"about_ca_topic_score_codex":0.003517848,"about_ca_topic_score_gemma":0.00467383,"teacher_disagreement_score":0.006165371,"about_ca_system_score_codex":0.0011155582,"about_ca_system_score_gemma":0.00089048425,"threshold_uncertainty_score":0.016376376},"labels":[],"label_agreement":null},{"id":"W2755606293","doi":"10.1080/0020174x.2017.1371865","title":"The illocutionary force of laws","year":2017,"lang":"en","type":"article","venue":"Inquiry","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Directive; Statute; Utterance; Argument (complex analysis); Law; Set (abstract data type); Political science; Property (philosophy); Law and economics; Sociology; Linguistics; Computer science; Epistemology; Philosophy","score_opus":0.02875631011875999,"score_gpt":0.2871574325810447,"score_spread":0.2584011224622847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755606293","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37810236,0.0019189015,0.18098937,0.0076212683,0.0003166598,0.00038963623,0.00038098564,0.00024723672,0.43003356],"genre_scores_gemma":[0.9828289,0.00031996588,0.007780924,0.00036941606,0.00008011637,0.000095601215,0.0000765041,0.000042829375,0.008405742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98860884,0.0034523262,0.0008188412,0.0012371753,0.0049029933,0.000979847],"domain_scores_gemma":[0.9636498,0.025775835,0.0032638095,0.0034763517,0.00336937,0.00046483605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005975006,0.00044229836,0.0003736663,0.0033281045,0.0021090375,0.004209985,0.0011664751,0.0019083369,0.0053429897],"category_scores_gemma":[0.031126512,0.0004010096,0.00067805866,0.0010799334,0.013754134,0.004256656,0.0030186037,0.004074733,0.0006972582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003991103,0.000040194755,0.0053410474,0.000060156533,0.000014559145,0.0002753719,0.0063400716,0.0006240516,0.0009367423,0.9709418,0.0006661897,0.014719944],"study_design_scores_gemma":[0.000054706365,0.00017301476,0.030718582,0.0005557595,0.00012104957,0.0013823326,0.010523876,0.016694091,0.008266811,0.8223545,0.109051876,0.0001033401],"about_ca_topic_score_codex":0.00523558,"about_ca_topic_score_gemma":0.0023988397,"teacher_disagreement_score":0.005975006,"about_ca_system_score_codex":0.0020413897,"about_ca_system_score_gemma":0.002540172,"threshold_uncertainty_score":0.031599224},"labels":[],"label_agreement":null},{"id":"W2757384725","doi":"10.18653/v1/w17-4218","title":"Using New York Times Picks to Identify Constructive Comments","year":2017,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Constructive; Computer science; Classifier (UML); Artificial intelligence; Natural language processing; Thread (computing); Information retrieval; Programming language","score_opus":0.0738470419160949,"score_gpt":0.3451609796345253,"score_spread":0.2713139377184304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757384725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9487036,0.0013142264,0.023419684,0.0007325728,0.00069823867,0.0004906797,0.006453537,0.0036832667,0.014504245],"genre_scores_gemma":[0.9292056,0.00052386604,0.038938187,0.00020368192,0.0004125411,0.00047392843,0.018244397,0.00037811074,0.011619816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99522936,0.0011382877,0.00034543686,0.0008778429,0.0019523485,0.000456666],"domain_scores_gemma":[0.9664651,0.019110428,0.0028181274,0.0014710587,0.008802811,0.0013324717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041828062,0.0013329122,0.00080211804,0.009016431,0.0016815411,0.0019342693,0.0006984,0.0014330891,0.0029168813],"category_scores_gemma":[0.02280204,0.00037721262,0.0005957201,0.002221827,0.00074695516,0.002533578,0.0020089995,0.0014461515,0.0044312337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024462396,0.0011002735,0.365951,0.0026359325,0.00032090064,0.0024175255,0.014110382,0.0067582172,0.07920476,0.0019818472,0.071864806,0.45120814],"study_design_scores_gemma":[0.0002480732,0.00089993404,0.45106357,0.0008630732,0.00034982763,0.003068721,0.0138129825,0.30140245,0.114009455,0.002555416,0.111212224,0.00051421527],"about_ca_topic_score_codex":0.010001334,"about_ca_topic_score_gemma":0.020409875,"teacher_disagreement_score":0.010001334,"about_ca_system_score_codex":0.0007596786,"about_ca_system_score_gemma":0.0010236888,"threshold_uncertainty_score":0.022121012},"labels":[],"label_agreement":null},{"id":"W2763989620","doi":"10.1163/22112596-02201007","title":"Understanding Human Rights on the Internet: An Exercise of Translation?","year":2017,"lang":"en","type":"article","venue":"Tilburg foreign law review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Internet governance; The Internet; Human rights; Political science; Accountability; Corporate governance; Civil society; Legal aspects of computing; Metaphor; Public relations; Sociology; Law; Law and economics; Business; Politics; Computer science; World Wide Web","score_opus":0.1621784929933845,"score_gpt":0.3170302117675512,"score_spread":0.1548517187741667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763989620","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021127176,0.040043186,0.089221895,0.5110272,0.0033459242,0.00024019524,0.00010204574,0.00015045068,0.33474192],"genre_scores_gemma":[0.85722214,0.027968006,0.020661548,0.059134908,0.0058171838,0.00056404795,0.00014396678,0.0002854751,0.02820268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9408353,0.04579012,0.002108656,0.0030957058,0.006283963,0.0018861563],"domain_scores_gemma":[0.8251534,0.15144514,0.005033182,0.010282419,0.007181805,0.0009040399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05716341,0.0009993132,0.0017846914,0.005818688,0.008209783,0.019366663,0.002703955,0.012391059,0.007828542],"category_scores_gemma":[0.13910703,0.0005892602,0.0006894072,0.005879204,0.08048066,0.04976147,0.009660563,0.012320574,0.0016046147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011609428,0.000019288333,0.00031719625,0.00018152704,0.000008517447,0.00011960311,0.023647035,0.00010814178,0.00006980859,0.94654477,0.004791163,0.024181336],"study_design_scores_gemma":[0.000011174209,0.00002419222,0.0004759038,0.0010422886,0.000011117787,0.00018483563,0.018318618,0.0004460492,0.00025214764,0.9027814,0.076426364,0.000025828289],"about_ca_topic_score_codex":0.006641723,"about_ca_topic_score_gemma":0.0034822312,"teacher_disagreement_score":0.05716341,"about_ca_system_score_codex":0.0071975044,"about_ca_system_score_gemma":0.010179142,"threshold_uncertainty_score":0.30231255},"labels":[],"label_agreement":null},{"id":"W2766367305","doi":"10.2495/dne-v13-n1-60-70","title":"Classification of tweets with a mixed method based on pragmatic content and meta-information","year":2018,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Content (measure theory); Computer science; Information retrieval; Artificial intelligence; Natural language processing; Mathematics","score_opus":0.02384813191798324,"score_gpt":0.26967382649364524,"score_spread":0.245825694575662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766367305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46166316,0.0031610974,0.4886819,0.001295162,0.0013499187,0.0043310914,0.018064795,0.005625868,0.015827078],"genre_scores_gemma":[0.72168624,0.0005503039,0.25581855,0.00012966599,0.0006353864,0.0028016788,0.012113316,0.0003250074,0.0059398767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957147,0.0010568486,0.0008531768,0.0008129192,0.001244331,0.00031792262],"domain_scores_gemma":[0.99220884,0.0035762715,0.0006518246,0.0005966578,0.002628264,0.00033799864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042794375,0.0017766969,0.0014704388,0.014945851,0.001434301,0.0031518838,0.00093517016,0.0014549071,0.0042891935],"category_scores_gemma":[0.011348016,0.00041471364,0.0020754703,0.00607303,0.000754561,0.0028700312,0.0019874915,0.0010036252,0.0032680708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0061028413,0.0010567193,0.1101723,0.002369821,0.0012250959,0.00083851186,0.0036252278,0.0034856761,0.069147795,0.0055281846,0.011969409,0.7844785],"study_design_scores_gemma":[0.000403709,0.003037356,0.19826989,0.00084947585,0.0024814622,0.0027720213,0.009691845,0.6406481,0.06516471,0.025120841,0.050908715,0.00065189553],"about_ca_topic_score_codex":0.001657388,"about_ca_topic_score_gemma":0.0025799728,"teacher_disagreement_score":0.014945851,"about_ca_system_score_codex":0.0007365268,"about_ca_system_score_gemma":0.0013228732,"threshold_uncertainty_score":0.022632062},"labels":[],"label_agreement":null},{"id":"W2768646450","doi":"10.1017/9781108242189.020","title":"Religion and Hate Speech in Canada","year":2017,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Content (measure theory); Internet privacy; Computer science; Media studies; Sociology; Political science; Mathematics","score_opus":0.012161503968077983,"score_gpt":0.1752913338484273,"score_spread":0.1631298298803493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768646450","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37831196,0.14317079,0.00037045157,0.058437858,0.0018411266,0.000084503794,0.023201348,0.00010567871,0.3944763],"genre_scores_gemma":[0.726679,0.09029791,0.0003580921,0.0028888702,0.0002586919,0.00002870289,0.0046622264,0.000081565755,0.17474505],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991653,0.000048775913,0.000019744575,0.000049755676,0.00041735516,0.0002991012],"domain_scores_gemma":[0.9977247,0.00033473174,0.00017738051,0.000044228855,0.0010215549,0.000697419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005954329,0.0002361151,0.00037403894,0.0023320431,0.0059501696,0.0032903422,0.00085876393,0.0008581215,0.023822904],"category_scores_gemma":[0.0030553655,0.00023438195,0.00031402035,0.0073062284,0.0015301848,0.00080017967,0.0012286783,0.0015468377,0.0010059519],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013450625,0.00012077687,0.12860882,0.0009360478,0.000101174184,0.00097623805,0.044668652,0.00065106305,0.0003337562,0.054996558,0.49153712,0.27693528],"study_design_scores_gemma":[0.000014388376,0.00002792315,0.5085153,0.0017518721,0.000068271365,0.00039397506,0.05394019,0.00046115293,0.0002696842,0.0023616476,0.43210033,0.00009524899],"about_ca_topic_score_codex":0.99714464,"about_ca_topic_score_gemma":0.9989767,"teacher_disagreement_score":0.06450543,"about_ca_system_score_codex":0.06450543,"about_ca_system_score_gemma":0.099491924,"threshold_uncertainty_score":0.4680218},"labels":[],"label_agreement":null},{"id":"W2770237858","doi":"10.24926/26390742.1259","title":"Viewpoint Discrimination, Hate Speech Laws, and the Double-Sided Nature of Freedom of Speech","year":2015,"lang":"en","type":"article","venue":"Constitutional commentary","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Free speech; First amendment; Law; Political science; Supreme court","score_opus":0.021973279780244197,"score_gpt":0.25754783292264793,"score_spread":0.23557455314240375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770237858","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09119349,0.002258139,0.026090557,0.13721672,0.0014346831,0.000097585704,0.00027273304,0.000105741754,0.7413304],"genre_scores_gemma":[0.9481461,0.0006827022,0.0030715798,0.018540867,0.0002793533,0.000047762784,0.000049263428,0.000038985745,0.029143283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9898487,0.0015328976,0.00023164247,0.0014667449,0.0048012314,0.0021188217],"domain_scores_gemma":[0.98705006,0.007390435,0.00096343254,0.0011762476,0.0026359363,0.00078384124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008163693,0.0005654958,0.00048113093,0.0015888173,0.012806472,0.0103558,0.0023615898,0.00807436,0.005560058],"category_scores_gemma":[0.02021702,0.0003632859,0.00052264263,0.0015354413,0.06169906,0.0059637935,0.0033490593,0.012115249,0.00035651986],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009324428,0.0000062342674,0.0006357301,0.000021975324,0.0000032668108,0.000093588555,0.007924974,0.00007946577,0.00022361326,0.9789291,0.005371357,0.0067014727],"study_design_scores_gemma":[0.0000448657,0.000044816607,0.009436395,0.0004973776,0.00008147344,0.00042514308,0.026524909,0.0014641874,0.0033947057,0.68419087,0.27373406,0.00016128679],"about_ca_topic_score_codex":0.6940185,"about_ca_topic_score_gemma":0.82538605,"teacher_disagreement_score":0.6940185,"about_ca_system_score_codex":0.024330096,"about_ca_system_score_gemma":0.037630294,"threshold_uncertainty_score":0.61556697},"labels":[],"label_agreement":null},{"id":"W2772447100","doi":"","title":"A New Method to Address Cyberbullying in the United States: The Application of a Notice-and-Takedown Model as a Restriction on Cyberbullying Speech","year":2017,"lang":"en","type":"article","venue":"Federal communications law journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scrutiny; Notice; Legislature; Political science; Law; Digital Millennium Copyright Act; Argument (complex analysis); Government (linguistics); State (computer science); Law and economics; Sociology; Medicine; Computer science; Linguistics; Intellectual property","score_opus":0.05313333046070591,"score_gpt":0.35365477881683943,"score_spread":0.30052144835613354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772447100","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02088357,0.00275769,0.4648945,0.018153314,0.003369575,0.0022751389,0.0003007177,0.0024061075,0.48495954],"genre_scores_gemma":[0.3514772,0.0038215786,0.41706654,0.013309863,0.0017472184,0.0041040042,0.00029117125,0.0004973938,0.2076851],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944653,0.002113217,0.00026885048,0.00077276333,0.0020935507,0.00028630337],"domain_scores_gemma":[0.9939407,0.0031953978,0.0003413736,0.0012053302,0.0010331586,0.00028400973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005662947,0.0006741973,0.00039494067,0.0025590213,0.002814443,0.0057558487,0.0020716467,0.0039554806,0.026004637],"category_scores_gemma":[0.01119178,0.0004003383,0.0009019107,0.0010868883,0.0048548575,0.0058479756,0.005157308,0.0041025314,0.0041251066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007330414,0.00034939678,0.0010648315,0.0002506462,0.000020402817,0.00020319757,0.001890086,0.0018778595,0.0041628852,0.7675223,0.026549272,0.1960358],"study_design_scores_gemma":[0.00015022041,0.00047113886,0.0020898778,0.0007644481,0.0000894325,0.0004781869,0.0014959325,0.020708831,0.00691363,0.15006654,0.8166316,0.00014008145],"about_ca_topic_score_codex":0.008571122,"about_ca_topic_score_gemma":0.012693124,"teacher_disagreement_score":0.026004637,"about_ca_system_score_codex":0.003427183,"about_ca_system_score_gemma":0.008938153,"threshold_uncertainty_score":0.08699417},"labels":[],"label_agreement":null},{"id":"W2773093872","doi":"10.2308/isys-51978","title":"The Use of Crowdsourcing and Social Media in Accounting Research","year":2017,"lang":"en","type":"article","venue":"Journal of Information Systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Crowdsourcing; Social media; Data science; Computer science; Task (project management); Work (physics); Crowdsourcing software development; World Wide Web; Knowledge management; Engineering","score_opus":0.08784815958822945,"score_gpt":0.3116870677272965,"score_spread":0.22383890813906704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773093872","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8178958,0.0077548847,0.020169655,0.019912336,0.00074543967,0.0005732538,0.00076814333,0.0002946072,0.13188589],"genre_scores_gemma":[0.9924684,0.0007990621,0.0038804987,0.00064794003,0.00025505264,0.0001542019,0.0001012012,0.0000341664,0.0016594446],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.91056615,0.070483424,0.0027389787,0.0027852997,0.011400603,0.002025543],"domain_scores_gemma":[0.51927394,0.3977029,0.038800504,0.022204092,0.016220674,0.0057978597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050236586,0.00061994407,0.00069093215,0.012477326,0.0067321057,0.011222093,0.0016672132,0.0020316443,0.005094549],"category_scores_gemma":[0.16133358,0.0004868356,0.00070999545,0.011211253,0.0058712107,0.008062187,0.0065136557,0.001969059,0.00091349764],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007539916,0.0007774205,0.363813,0.0029534271,0.0006234346,0.0016643587,0.14304325,0.0039017282,0.002387122,0.06599093,0.017098773,0.39699262],"study_design_scores_gemma":[0.0001523808,0.00053360616,0.38006285,0.0061863572,0.00048147526,0.0016229857,0.23198335,0.024018543,0.0072932923,0.102049544,0.24488847,0.0007271498],"about_ca_topic_score_codex":0.009547447,"about_ca_topic_score_gemma":0.009817254,"teacher_disagreement_score":0.050236586,"about_ca_system_score_codex":0.0045090006,"about_ca_system_score_gemma":0.004300607,"threshold_uncertainty_score":0.2656796},"labels":[],"label_agreement":null},{"id":"W2774435229","doi":"10.7202/1070696ar","title":"Free Speech on Campus (Sigal Ben-Porath)","year":2020,"lang":"en","type":"article","venue":"Philosophical Inquiry in Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology","score_opus":0.04752168853435418,"score_gpt":0.2952153040977625,"score_spread":0.2476936155634083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774435229","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02927724,0.009504376,0.0018656056,0.052225098,0.047354367,0.000328903,0.00731796,0.0021879012,0.84993845],"genre_scores_gemma":[0.017471,0.000873955,0.00052555196,0.0010312394,0.0017336246,0.000040507322,0.00084006443,0.00027037004,0.9772136],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958307,0.00007432372,0.000009708688,0.000096932796,0.00012801184,0.000108093045],"domain_scores_gemma":[0.99877554,0.00012898196,0.000040263374,0.00007587117,0.00023137512,0.00074804714],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010429278,0.0006462646,0.00034350983,0.00054328685,0.0024529428,0.0024594364,0.00046012495,0.0014534545,0.3571901],"category_scores_gemma":[0.0015445565,0.00023439982,0.00024194157,0.0002379096,0.00047096983,0.0009609629,0.00281819,0.0017175686,0.12032185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021556707,0.00009112264,0.00053108984,0.000054430497,0.000004200881,0.00022915548,0.00027144252,0.000049326783,0.0009419386,0.0010982754,0.8975888,0.09892474],"study_design_scores_gemma":[0.000013397687,0.000060056926,0.0033890286,0.000046621455,0.0000020293216,0.00009323477,0.0005638336,0.0000719493,0.0005590412,0.00029678567,0.99489576,0.000008202168],"about_ca_topic_score_codex":0.0050967657,"about_ca_topic_score_gemma":0.018465094,"teacher_disagreement_score":0.3571901,"about_ca_system_score_codex":0.00054183963,"about_ca_system_score_gemma":0.0010950117,"threshold_uncertainty_score":0.9168903},"labels":[],"label_agreement":null},{"id":"W2774450286","doi":"10.4000/ethiquepublique.3080","title":"Le débat public à la suite de l’attentat contre Charlie Hebdo – Y a-t-il une éthique de la liberté d’expression ?","year":2017,"lang":"fr","type":"article","venue":"Éthique Publique","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy; Freedom of expression; Political science; Human rights","score_opus":0.01324898910944277,"score_gpt":0.25891900178699,"score_spread":0.24567001267754723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774450286","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038698487,0.016808521,0.011435024,0.4627053,0.0038770349,0.00008509186,0.000105227045,0.00008887783,0.46619654],"genre_scores_gemma":[0.66741675,0.006667398,0.002563541,0.062374514,0.0008109363,0.00015256235,0.00005163321,0.00013107498,0.25983155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99293315,0.0034625926,0.000114024275,0.00081694435,0.0016751047,0.0009982571],"domain_scores_gemma":[0.9930977,0.0034048967,0.00055082,0.0005429474,0.0015998323,0.00080372766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007375734,0.00044344098,0.00041312075,0.00093405385,0.010934372,0.009976432,0.0011758931,0.0059654643,0.013298961],"category_scores_gemma":[0.010906369,0.00030194453,0.0003970995,0.000866769,0.02619188,0.0059381076,0.004403197,0.010363735,0.001785094],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034382712,0.000019281584,0.0005814668,0.00009447706,0.000008587958,0.000091481445,0.0130806025,0.00010162274,0.00025578134,0.93246824,0.03721739,0.016046733],"study_design_scores_gemma":[0.000036248668,0.000046662793,0.0029512767,0.00085632695,0.000027695974,0.00014475427,0.016122278,0.00033716531,0.0011863937,0.14600116,0.8322146,0.00007562188],"about_ca_topic_score_codex":0.14349243,"about_ca_topic_score_gemma":0.20812401,"teacher_disagreement_score":0.14349243,"about_ca_system_score_codex":0.028292844,"about_ca_system_score_gemma":0.028637065,"threshold_uncertainty_score":0.28531444},"labels":[],"label_agreement":null},{"id":"W2784246330","doi":"10.3138/utlj.2017-0073","title":"Gender identity, gender pronouns, and freedom of expression: Bill C-16 and the traction of specious legal claims","year":2018,"lang":"en","type":"article","venue":"University of Toronto Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Opposition (politics); Law; Political science; Politics; Legislature; Human rights; Legislation; Jurisdiction; Sociology; Binary opposition; Freedom of expression; Epistemology; Philosophy","score_opus":0.012049845758129975,"score_gpt":0.20870440582891384,"score_spread":0.19665456007078386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784246330","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3347487,0.011499217,0.005377981,0.11942831,0.00091170776,0.000039345734,0.00011462155,0.00003251887,0.5278476],"genre_scores_gemma":[0.98947805,0.0011067078,0.00038115805,0.0020130835,0.00012868685,0.000009473985,0.000018730836,0.000015348802,0.00684882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944165,0.0024901172,0.00015885665,0.0005279893,0.0015651758,0.00084138213],"domain_scores_gemma":[0.98962003,0.007065004,0.0011496987,0.0005707423,0.0010934853,0.00050099986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006398253,0.00035820284,0.0004167405,0.0021853298,0.01611434,0.012570741,0.0010607485,0.004238084,0.0033642696],"category_scores_gemma":[0.014342409,0.0003678456,0.00021029274,0.0025731323,0.08176017,0.0069465605,0.004577181,0.0050873156,0.0003220884],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024955476,0.000015781636,0.0032120217,0.000039705505,0.0000029458386,0.00023581437,0.20604545,0.00005307643,0.0001259906,0.77171063,0.004937694,0.013595861],"study_design_scores_gemma":[0.000029856401,0.00004072038,0.023987448,0.00079799176,0.000037342972,0.00059591635,0.43796146,0.00077407394,0.001211898,0.26093283,0.2735374,0.00009300991],"about_ca_topic_score_codex":0.27923816,"about_ca_topic_score_gemma":0.33273563,"teacher_disagreement_score":0.72076184,"about_ca_system_score_codex":0.021820795,"about_ca_system_score_gemma":0.01158826,"threshold_uncertainty_score":0.5552256},"labels":[],"label_agreement":null},{"id":"W2793814837","doi":"","title":"Hate propaganda, section 2(b) and section 1 of the Charter: a Canadian constitutional dilemma","year":2016,"lang":"en","type":"article","venue":"Books | Encyclopedia of Canadian Laws","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Section (typography); Charter; Dilemma; Political science; Law; Constitutional law; Philosophy; Advertising; Business","score_opus":0.0071418298246610865,"score_gpt":0.17375751653451357,"score_spread":0.1666156867098525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793814837","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01623398,0.009011032,0.0022009907,0.28161982,0.0035506168,0.00021857208,0.0016136111,0.00017361526,0.68537784],"genre_scores_gemma":[0.24897134,0.006997084,0.0050805756,0.13703449,0.0009798438,0.00022070856,0.0006703071,0.00022480219,0.5998209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99088156,0.0006247018,0.00020220419,0.0007925898,0.0045369603,0.0029620165],"domain_scores_gemma":[0.9918263,0.0022472967,0.00025565008,0.0002833915,0.004138389,0.0012490578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047407635,0.0007948803,0.00053722854,0.0031728772,0.032792583,0.015090371,0.0038598236,0.016695645,0.01776168],"category_scores_gemma":[0.018406665,0.0010971695,0.00073255604,0.0040133437,0.013115579,0.0038820491,0.0021682046,0.012527536,0.0014173014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013412429,0.000028199893,0.00078055594,0.000060577917,0.0000062544723,0.00012048919,0.0045753964,0.0001337441,0.0002310524,0.62225276,0.35697424,0.014823351],"study_design_scores_gemma":[0.000027413038,0.00001376067,0.007563005,0.0002757804,0.000029346633,0.00009419428,0.005253988,0.0002544182,0.00038918093,0.029590927,0.956375,0.00013303604],"about_ca_topic_score_codex":0.99517304,"about_ca_topic_score_gemma":0.99744475,"teacher_disagreement_score":0.10274553,"about_ca_system_score_codex":0.10274553,"about_ca_system_score_gemma":0.329015,"threshold_uncertainty_score":0.7454745},"labels":[],"label_agreement":null},{"id":"W2795187047","doi":"10.1111/blar.12744","title":"Media and Punitive Populism in<scp>A</scp>rgentina and<scp>C</scp>hile","year":2018,"lang":"en","type":"article","venue":"Bulletin of Latin American Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"Punitive damages; Populism; Politics; Autonomy; Political science; Latin Americans; Public opinion; Political economy; Criminology; Sociology; Law","score_opus":0.03783214025867559,"score_gpt":0.3031593630055405,"score_spread":0.26532722274686493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795187047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89500463,0.0018457718,0.00045558353,0.007947951,0.00013768398,0.00003290927,0.00008299254,0.00001325732,0.09447932],"genre_scores_gemma":[0.99614584,0.00069425313,0.00008504801,0.0003723629,0.00006148059,0.000018054552,0.000026490354,0.000013953952,0.002582438],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99831957,0.0008595958,0.000056989054,0.00012824348,0.00024171693,0.00039379904],"domain_scores_gemma":[0.99231124,0.004078916,0.0022120073,0.0003056778,0.0006181519,0.00047409433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026860354,0.0003194322,0.00024585234,0.002909748,0.005015595,0.007947338,0.00040478873,0.0009782863,0.004715589],"category_scores_gemma":[0.009158227,0.00026930065,0.00017076511,0.0029189305,0.007986955,0.0039516743,0.003206483,0.0017087929,0.0002679065],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034072163,0.00061073346,0.20115821,0.00052189204,0.00008675249,0.0023660187,0.6024491,0.00012744374,0.0031805017,0.1234178,0.006951964,0.05878885],"study_design_scores_gemma":[0.00003707698,0.0000792367,0.2638873,0.0008124667,0.00005698526,0.000598425,0.61481,0.00025582215,0.0018686894,0.00843466,0.10910289,0.000056572375],"about_ca_topic_score_codex":0.013311482,"about_ca_topic_score_gemma":0.026812647,"teacher_disagreement_score":0.013311482,"about_ca_system_score_codex":0.0029004496,"about_ca_system_score_gemma":0.0019756998,"threshold_uncertainty_score":0.026467979},"labels":[],"label_agreement":null},{"id":"W2795925994","doi":"10.2139/ssrn.3147336","title":"Can Cyber Harassment Laws Encourage Online Speech?","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Harassment; Internet privacy; Political science; Law; Computer security; Business; Computer science","score_opus":0.010623976058554595,"score_gpt":0.2528124265220819,"score_spread":0.24218845046352733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795925994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8051828,0.0010776513,0.0015304452,0.04299442,0.0005958091,0.000055908586,0.00029489596,0.000042560278,0.14822556],"genre_scores_gemma":[0.99343807,0.0003518404,0.00012245883,0.0025746361,0.00015900699,0.000012541996,0.00003307377,0.00000531518,0.0033030873],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978168,0.00089491054,0.000071918024,0.00018786712,0.00050118257,0.0005273695],"domain_scores_gemma":[0.96380067,0.021260561,0.008513349,0.0013867355,0.002955862,0.0020828804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025574665,0.00016978772,0.00022313792,0.0009806214,0.0015204678,0.0038773646,0.0005143726,0.0030510777,0.013787415],"category_scores_gemma":[0.03402034,0.00024109392,0.00025322035,0.00058040145,0.0018673268,0.003116537,0.000963055,0.0025556874,0.0019635481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004625133,0.0024336178,0.76796275,0.00024707834,0.00013550608,0.001000423,0.009990797,0.0005153819,0.0025050691,0.0638451,0.02606909,0.12483258],"study_design_scores_gemma":[0.00004175201,0.00040697271,0.90285826,0.00040564095,0.00013185029,0.0007496526,0.031546153,0.0022189338,0.0026210826,0.024162589,0.034819063,0.00003795471],"about_ca_topic_score_codex":0.0064881495,"about_ca_topic_score_gemma":0.014316242,"teacher_disagreement_score":0.013787415,"about_ca_system_score_codex":0.0008281616,"about_ca_system_score_gemma":0.0016199312,"threshold_uncertainty_score":0.046123505},"labels":[],"label_agreement":null},{"id":"W2797551927","doi":"","title":"Striking the Right Balance: Hate Speech Laws in Japan, the United States, and Canada","year":2018,"lang":"en","type":"article","venue":"Hastings constitutional law quarterly","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Law; Balance (ability); Doctrine; Legislation; Political science; Constitutional right; Marketplace of ideas; Supreme court; Law and economics; Sociology; Politics; First amendment; Psychology","score_opus":0.0069795281344962115,"score_gpt":0.2018759241549634,"score_spread":0.19489639602046718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797551927","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4000607,0.35378218,0.00068395527,0.026402894,0.00060157647,0.00009066808,0.0008749231,0.000028170392,0.21747492],"genre_scores_gemma":[0.8273269,0.1612355,0.0006035982,0.00396539,0.00007048548,0.000026175438,0.00032460512,0.000018951057,0.0064284937],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997593,0.00021099673,0.00011481325,0.00015804869,0.0011373236,0.0007858447],"domain_scores_gemma":[0.99469924,0.00079825567,0.0005005729,0.00008013827,0.0034002748,0.00052150124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002341619,0.00028181958,0.00049917854,0.0059893103,0.008404811,0.005624195,0.0009869565,0.0014684127,0.0015883272],"category_scores_gemma":[0.005712956,0.00024189841,0.0005032552,0.010874939,0.005222776,0.0025579042,0.0013714574,0.002112983,0.00006294458],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034401056,0.00019379407,0.11506502,0.005426499,0.00033938914,0.003931597,0.15042101,0.0027475413,0.0019020974,0.41756517,0.039626665,0.26243722],"study_design_scores_gemma":[0.000042076612,0.00009856752,0.35171875,0.0058911154,0.00056416396,0.00051101495,0.18257236,0.0009439601,0.0009960978,0.009267242,0.44716707,0.00022766629],"about_ca_topic_score_codex":0.9946694,"about_ca_topic_score_gemma":0.9971227,"teacher_disagreement_score":0.09806719,"about_ca_system_score_codex":0.09806719,"about_ca_system_score_gemma":0.13525163,"threshold_uncertainty_score":0.71153057},"labels":[],"label_agreement":null},{"id":"W2802105481","doi":"10.18653/v1/s18-2005","title":"Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":395,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Task (project management); Benchmark (surveying); Race (biology); Sentiment analysis; Artificial intelligence; Affect (linguistics); Gender bias; Natural language processing; Machine learning; SemEval; Psychology; Social psychology; Sociology","score_opus":0.06497271295094052,"score_gpt":0.29041027779294604,"score_spread":0.2254375648420055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802105481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801595,0.00069793075,0.002853278,0.00040803108,0.00018598772,0.0001339065,0.0071313037,0.0004620111,0.007968077],"genre_scores_gemma":[0.96102804,0.00027578283,0.0062444815,0.000259029,0.00009599561,0.0001881253,0.028202925,0.00015638018,0.0035493306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996986,0.0009833695,0.00032038736,0.00053117936,0.0009184782,0.0002605829],"domain_scores_gemma":[0.9932388,0.0029648007,0.0004198598,0.00084142666,0.0022931823,0.00024185503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031044127,0.00045974922,0.00030099062,0.0014732588,0.0011662496,0.00082572363,0.00049031887,0.0005855319,0.0013002433],"category_scores_gemma":[0.0082988,0.0002482088,0.00036451136,0.0011206554,0.0006687326,0.0009364818,0.0011749823,0.00058571465,0.00096542976],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024754566,0.0011289996,0.41256142,0.001901127,0.000524634,0.00088707404,0.009107465,0.006435086,0.084705755,0.0047936933,0.12059878,0.35488054],"study_design_scores_gemma":[0.00027311483,0.001061708,0.7783202,0.0001917142,0.00023444465,0.0008284273,0.0036405344,0.05190961,0.056716785,0.002464807,0.104203366,0.00015529146],"about_ca_topic_score_codex":0.006126354,"about_ca_topic_score_gemma":0.013147261,"teacher_disagreement_score":0.006126354,"about_ca_system_score_codex":0.00088890025,"about_ca_system_score_gemma":0.00066955783,"threshold_uncertainty_score":0.01641792},"labels":[],"label_agreement":null},{"id":"W2803034608","doi":"10.1007/s10612-018-9394-y","title":"A Climate for Hate? An Exploration of the Right-Wing Extremist Landscape in Canada","year":2018,"lang":"en","type":"article","venue":"Critical Criminology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University; Ontario Tech University","funders":"","keywords":"Politics; Right wing; Political science; Criminology; Law enforcement; Sociology; Law","score_opus":0.07165223020226696,"score_gpt":0.2923776909721806,"score_spread":0.22072546076991362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803034608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93379915,0.0018241359,0.00042035565,0.017181294,0.00010315886,0.00008739771,0.00046571347,0.000015769403,0.046103075],"genre_scores_gemma":[0.9919727,0.00079297996,0.00016673838,0.0008493757,0.000012293784,0.000011044485,0.00008311765,0.000012873655,0.006098853],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99820757,0.00019891387,0.000020500145,0.00010518726,0.0003504684,0.001117466],"domain_scores_gemma":[0.99657506,0.0005288568,0.00028368126,0.00006713197,0.0011051247,0.0014401892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009398602,0.00026742442,0.0004715116,0.0028807933,0.026557349,0.008386467,0.0021308325,0.0018235669,0.0068595116],"category_scores_gemma":[0.0030583837,0.0004527418,0.00036842647,0.0053746277,0.008168011,0.0016638555,0.0038897402,0.0032141628,0.00026587618],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029530213,0.00029968226,0.3162593,0.00044370748,0.0001208299,0.0048557534,0.4534745,0.0017554805,0.0012885198,0.08460279,0.050891005,0.08571313],"study_design_scores_gemma":[0.000010191956,0.000032496217,0.21438222,0.00029622728,0.00003238632,0.00023277981,0.72050107,0.0010301312,0.00018418157,0.0026450197,0.060586426,0.00006686123],"about_ca_topic_score_codex":0.9967313,"about_ca_topic_score_gemma":0.99937385,"teacher_disagreement_score":0.11340723,"about_ca_system_score_codex":0.11340723,"about_ca_system_score_gemma":0.16070382,"threshold_uncertainty_score":0.8228309},"labels":[],"label_agreement":null},{"id":"W2804332317","doi":"10.5539/hes.v8n2p81","title":"A Pragmatic Study of Relational Identity in Bystander Intervention","year":2018,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bystander effect; Intervention (counseling); Identity (music); Psychology; Pragmatics; Identity negotiation; Empathy; Phenomenon; Social psychology; Negotiation; Epistemology; Sociology; Linguistics; Social science","score_opus":0.047773033018849886,"score_gpt":0.3755999060456239,"score_spread":0.327826873026774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804332317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7011103,0.0022340056,0.12556575,0.013043876,0.0004894603,0.0025845948,0.00008946135,0.00014658281,0.15473598],"genre_scores_gemma":[0.98890615,0.0002918291,0.0066411383,0.00045389353,0.000056520807,0.0007287672,0.0000130830895,0.000031345095,0.0028772466],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9535847,0.040825825,0.0005325943,0.001781312,0.0021241258,0.0011514701],"domain_scores_gemma":[0.9573512,0.03477717,0.002795112,0.0024537712,0.0014102437,0.0012124884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020986205,0.00051519665,0.0008914597,0.001967678,0.008597809,0.0045147855,0.0014283912,0.0028822687,0.006024144],"category_scores_gemma":[0.04113389,0.0004453506,0.00045465495,0.0010125138,0.026782908,0.007337813,0.009467098,0.003024493,0.00041933559],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013848118,0.00038989043,0.0021943222,0.00059576344,0.0000248071,0.0015195213,0.7839133,0.00020477865,0.0050987904,0.18707953,0.000842232,0.017998612],"study_design_scores_gemma":[0.0002012008,0.0010543284,0.009516878,0.0011328032,0.000061026338,0.0019087782,0.78486526,0.0019303611,0.003606544,0.09601247,0.09962429,0.00008607438],"about_ca_topic_score_codex":0.001404538,"about_ca_topic_score_gemma":0.0018437719,"teacher_disagreement_score":0.020986205,"about_ca_system_score_codex":0.0031184382,"about_ca_system_score_gemma":0.0032313045,"threshold_uncertainty_score":0.11098695},"labels":[],"label_agreement":null},{"id":"W2806155868","doi":"10.5204/ijcjsd.v7i2.521","title":"Special Edition: Discourses of Hate - Guest Editors' Introduction","year":2018,"lang":"en","type":"article","venue":"International Journal for Crime Justice and Social Democracy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Hostility; Narrative; Politics; Immigration; Political science; Democracy; Hate crime; Media studies; Download; Public discourse; Sociology; Criminology; Law; Psychology; Social psychology; Literature; Art","score_opus":0.014485711379304149,"score_gpt":0.3061960691063416,"score_spread":0.29171035772703746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806155868","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010053317,0.04280817,0.0014588399,0.12392863,0.5669883,0.0001224997,0.0015323194,0.0005042575,0.2526037],"genre_scores_gemma":[0.13156348,0.0439637,0.0011911965,0.027945362,0.5032752,0.00028719913,0.0014871111,0.00080599694,0.28948087],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842525,0.00041556952,0.00009034327,0.00023169488,0.00067446835,0.00016270023],"domain_scores_gemma":[0.9904759,0.006388308,0.0005744071,0.00039661178,0.0014371905,0.0007275111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017064274,0.0012438708,0.00076839473,0.0028591675,0.00367025,0.008790253,0.0010476072,0.0024114961,0.030595],"category_scores_gemma":[0.01172203,0.00034727494,0.00032188703,0.002613463,0.0031677869,0.005280703,0.003735223,0.0040632277,0.0056315307],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014312706,0.000017047098,0.00028868255,0.00020433345,0.000002999468,0.00010066199,0.0050016413,0.000019005212,0.00011615554,0.0052920743,0.96859276,0.020350376],"study_design_scores_gemma":[0.000003575021,0.00002163899,0.0016635115,0.0005555581,0.0000049780638,0.00018494208,0.004147444,0.00005961967,0.0001237473,0.0012535417,0.9919689,0.000012409481],"about_ca_topic_score_codex":0.0019255192,"about_ca_topic_score_gemma":0.003493755,"teacher_disagreement_score":0.030595,"about_ca_system_score_codex":0.0015535406,"about_ca_system_score_gemma":0.0011753221,"threshold_uncertainty_score":0.10235047},"labels":[],"label_agreement":null},{"id":"W2810320099","doi":"10.1111/capa.12263","title":"Digital third parties: Understanding the technological challenge to Canada's third party advertising regime","year":2018,"lang":"en","type":"article","venue":"Canadian Public Administration","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Advertising; Third party; The Internet; Obstacle; Business; Social media; Political science; Internet privacy; Law; Computer science","score_opus":0.03203629545183775,"score_gpt":0.2313499278907134,"score_spread":0.19931363243887565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810320099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71845454,0.0017127192,0.0014384951,0.026340662,0.00008176417,0.0001061166,0.0010507007,0.000025577343,0.25078934],"genre_scores_gemma":[0.99063057,0.00063446217,0.00029760247,0.0008097486,0.000013264388,0.00001489158,0.00014633407,0.000008623206,0.0074445303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99622005,0.00029511136,0.00008351001,0.00025402225,0.0017443008,0.0014030783],"domain_scores_gemma":[0.99013036,0.0026431826,0.0014179235,0.0005496801,0.0037362792,0.001522532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024399164,0.00016418421,0.0003216585,0.0036300162,0.012220709,0.01053228,0.0013104465,0.0015268777,0.007225686],"category_scores_gemma":[0.0114669325,0.0002464381,0.0003643267,0.005513929,0.0069321296,0.002466313,0.0030370136,0.002404622,0.0002891631],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017619896,0.00016506338,0.29227614,0.0002442208,0.000058911315,0.0010696204,0.09726801,0.0016479677,0.0012071971,0.5038924,0.021402031,0.0805921],"study_design_scores_gemma":[0.000034801833,0.00005811405,0.49172446,0.0006410308,0.00012050252,0.00022063822,0.19430211,0.0052665253,0.0011632745,0.024510795,0.28180778,0.00014996338],"about_ca_topic_score_codex":0.99413764,"about_ca_topic_score_gemma":0.9960588,"teacher_disagreement_score":0.121456645,"about_ca_system_score_codex":0.121456645,"about_ca_system_score_gemma":0.12401672,"threshold_uncertainty_score":0.88123375},"labels":[],"label_agreement":null},{"id":"W2811400418","doi":"10.4000/revdh.4109","title":"Constitutional Treatment of Hate Speech and Freedom of Expression: a Canada – U.S. perspective","year":2018,"lang":"en","type":"article","venue":"Revue des droits de l’homme","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Harm; Multiculturalism; Law; Political science; Constitutional law; Supreme court; Freedom of expression; Sociology; Human rights","score_opus":0.016604695107571947,"score_gpt":0.23807274662653136,"score_spread":0.2214680515189594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811400418","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04217386,0.025805952,0.00810872,0.13109814,0.0012312507,0.0001097186,0.0005154531,0.000084515304,0.79087234],"genre_scores_gemma":[0.91387475,0.017137723,0.0046765795,0.023290109,0.0004917774,0.000080575956,0.0002387808,0.00006320638,0.04014646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.989077,0.0015381785,0.00029605217,0.0010729136,0.00453395,0.0034818808],"domain_scores_gemma":[0.99040586,0.003272946,0.00047949102,0.0004387706,0.0040988307,0.0013040749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006166621,0.00065977743,0.000774482,0.0058020586,0.028342512,0.013128597,0.0029848956,0.006101051,0.007030119],"category_scores_gemma":[0.010949003,0.00044453648,0.000708449,0.0055160318,0.03583372,0.0035581868,0.0042717215,0.00869082,0.00035930617],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008560488,0.000010164329,0.00074261276,0.00003668664,0.0000056784575,0.00012811126,0.0036494357,0.0002155207,0.000118945245,0.97885853,0.007031305,0.00919448],"study_design_scores_gemma":[0.000046345456,0.00005829505,0.020732112,0.0013449624,0.000119440985,0.00048107503,0.020381058,0.0020380395,0.0011186781,0.2495511,0.70381945,0.00030938428],"about_ca_topic_score_codex":0.98867995,"about_ca_topic_score_gemma":0.99114686,"teacher_disagreement_score":0.16917852,"about_ca_system_score_codex":0.16917852,"about_ca_system_score_gemma":0.20020193,"threshold_uncertainty_score":0.96363515},"labels":[],"label_agreement":null},{"id":"W283666408","doi":"","title":"Provocation and Multiculturalism","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Provocation test; Doctrine; Multiculturalism; Law; Cultural diversity; Political science; Sociology; Medicine; Alternative medicine","score_opus":0.003359273753181322,"score_gpt":0.20576408891731393,"score_spread":0.2024048151641326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W283666408","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32216406,0.006618096,0.036779292,0.027480766,0.00075564167,0.00012677543,0.000029592846,0.000067449655,0.60597837],"genre_scores_gemma":[0.994541,0.00039353123,0.0010403082,0.00122628,0.00014932417,0.000022485057,0.0000046905875,0.000010845775,0.002611573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.986937,0.008055996,0.00041024538,0.0012730795,0.0020351282,0.0012886091],"domain_scores_gemma":[0.9871579,0.0071030497,0.0017808,0.0016791502,0.0014207935,0.0008582118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009553248,0.00035185248,0.0004689512,0.002388695,0.008780624,0.006543767,0.0015059887,0.0024692956,0.0038275733],"category_scores_gemma":[0.016311022,0.0002859342,0.0005791887,0.0010761233,0.048357617,0.0074459333,0.011306907,0.0046431287,0.0002792333],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018552993,0.000030113244,0.0033942293,0.000061634346,0.000009485788,0.00033741948,0.043556362,0.00011176388,0.00021249954,0.936005,0.00062989345,0.015633032],"study_design_scores_gemma":[0.000035157307,0.000115645686,0.008966754,0.0005143133,0.000031651518,0.0021848455,0.07386467,0.0007958895,0.0014457759,0.80182713,0.11015272,0.00006537846],"about_ca_topic_score_codex":0.0027410057,"about_ca_topic_score_gemma":0.0027644457,"teacher_disagreement_score":0.009553248,"about_ca_system_score_codex":0.0028274285,"about_ca_system_score_gemma":0.0028191751,"threshold_uncertainty_score":0.050523043},"labels":[],"label_agreement":null},{"id":"W2884904579","doi":"10.1163/15691330-12341489","title":"Public Opinion and Morality Policy: Lessons from Canada and the United States","year":2019,"lang":"en","type":"article","venue":"Comparative Sociology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public opinion; Political science; Public administration; Public policy; Law; Politics","score_opus":0.07376144229755967,"score_gpt":0.3132940220202529,"score_spread":0.23953257972269326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884904579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6943981,0.010368666,0.0007875966,0.094961695,0.00048368567,0.00016116162,0.0017041283,0.000037690592,0.19709736],"genre_scores_gemma":[0.98446345,0.0035828496,0.00036477408,0.004367798,0.000057462457,0.000022144985,0.00030614526,0.000020227913,0.006815196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942145,0.0012937433,0.00010002151,0.00031311397,0.001574318,0.0025043876],"domain_scores_gemma":[0.97622514,0.007663366,0.0009961835,0.00045926202,0.011546705,0.003109289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006306623,0.00037153717,0.00096016575,0.0033044163,0.012003957,0.007855399,0.0020519032,0.0021871044,0.00520898],"category_scores_gemma":[0.020388678,0.0002649505,0.00054313825,0.008477893,0.0068783257,0.0023444234,0.0026410988,0.0039111865,0.00026955933],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010443792,0.0009836378,0.351796,0.0006942885,0.00040935588,0.0025793794,0.112593435,0.0054434193,0.00062380877,0.24812555,0.11306214,0.16264464],"study_design_scores_gemma":[0.00027886624,0.00019357023,0.40189195,0.0020058246,0.00042404383,0.00020136317,0.37519494,0.0045511704,0.0008229164,0.02995379,0.18414918,0.00033245832],"about_ca_topic_score_codex":0.9983663,"about_ca_topic_score_gemma":0.99907875,"teacher_disagreement_score":0.14807263,"about_ca_system_score_codex":0.14807263,"about_ca_system_score_gemma":0.17277473,"threshold_uncertainty_score":0.988115},"labels":[],"label_agreement":null},{"id":"W2891468492","doi":"10.47678/cjhe.v48i2.188215","title":"Book review of \"University Commons Divided: Exploring Dissent and Debate on Campus\"","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Higher Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dissent; Commons; Sociology; Political science; Media studies; Law","score_opus":0.020476566051042276,"score_gpt":0.24315429566115998,"score_spread":0.2226777296101177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891468492","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084428495,0.1547491,0.0007652828,0.6953989,0.07409616,0.000044278346,0.000277989,0.00006939049,0.073754564],"genre_scores_gemma":[0.024210723,0.16035658,0.00090582337,0.4254599,0.10108097,0.00031336513,0.0006120361,0.00044300983,0.28661764],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972023,0.0009839329,0.00008154402,0.00024043217,0.0013020815,0.00018980322],"domain_scores_gemma":[0.9773252,0.017000435,0.0005589101,0.00032442476,0.004036371,0.00075470994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034181937,0.00060909503,0.0009946267,0.002993872,0.005067525,0.0075760614,0.002024569,0.0063068713,0.01891086],"category_scores_gemma":[0.017940197,0.00040223228,0.00044830833,0.004759347,0.006631962,0.007067164,0.00261225,0.00997751,0.005951055],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032346597,0.000003994005,0.000024746314,0.00009533719,0.0000016284695,0.000008264413,0.00042801737,0.000020059852,0.000011519986,0.0077049127,0.98376393,0.0079343105],"study_design_scores_gemma":[0.000004152182,0.0000041789217,0.00025384274,0.0005245826,0.000002719377,0.000025163888,0.00094802247,0.00002930881,0.000016701155,0.0035201043,0.99466395,0.000007204392],"about_ca_topic_score_codex":0.03365609,"about_ca_topic_score_gemma":0.09691207,"teacher_disagreement_score":0.03365609,"about_ca_system_score_codex":0.009303035,"about_ca_system_score_gemma":0.010588666,"threshold_uncertainty_score":0.067498565},"labels":[],"label_agreement":null},{"id":"W2899082763","doi":"10.18653/v1/w18-5119","title":"Decipherment for Adversarial Offensive Language Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Decipherment; Offensive; Computer science; Adversarial system; Filter (signal processing); Plaintext; Artificial intelligence; Task (project management); Ciphertext; Computer security; Encryption; Natural language processing; Computer vision; Engineering; Linguistics","score_opus":0.0073919685686821765,"score_gpt":0.24470517585450735,"score_spread":0.23731320728582517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899082763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25354245,0.0032040337,0.71491367,0.0011260514,0.00048204,0.0004893554,0.0026084918,0.014503709,0.009130238],"genre_scores_gemma":[0.8373015,0.00069865305,0.15181135,0.0003243079,0.00019415497,0.00016819692,0.003196875,0.00022925991,0.006075693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792945,0.00050488184,0.00017132815,0.00059940544,0.0005914534,0.00020346149],"domain_scores_gemma":[0.99353266,0.0029256917,0.000977856,0.0017925726,0.0006068783,0.00016433264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019582775,0.0011309928,0.0011115067,0.0023735058,0.00072245725,0.0013420997,0.0011708696,0.0014252437,0.001844582],"category_scores_gemma":[0.009196526,0.00027742196,0.0007019145,0.0007634508,0.0009770595,0.002389743,0.0014623844,0.0017927719,0.0022888114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007949757,0.00052751496,0.015030744,0.00046975957,0.00018822728,0.0008726123,0.00048750095,0.07826005,0.040294867,0.0105850445,0.020615878,0.83187276],"study_design_scores_gemma":[0.000026297243,0.00027007307,0.0050063254,0.00006705848,0.000060725746,0.0012694828,0.0001704119,0.90039784,0.06756347,0.014311098,0.010802949,0.00005423476],"about_ca_topic_score_codex":0.0013468971,"about_ca_topic_score_gemma":0.0016588815,"teacher_disagreement_score":0.0023735058,"about_ca_system_score_codex":0.00068369065,"about_ca_system_score_gemma":0.0007193235,"threshold_uncertainty_score":0.010356486},"labels":[],"label_agreement":null},{"id":"W2905486807","doi":"10.4000/books.aaccademia.4772","title":"Comparing Different Supervised Approaches to Hate Speech Detection","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Task (project management); Computer science; Artificial neural network; Domain (mathematical analysis); Voice activity detection; Artificial intelligence; Speech recognition; Machine learning; Natural language processing; Speech processing; Engineering; Mathematics","score_opus":0.10207902198841332,"score_gpt":0.20651671551683387,"score_spread":0.10443769352842056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905486807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.574508,0.01812904,0.3293506,0.0016314649,0.0023272617,0.0012583118,0.007663375,0.028114565,0.03701742],"genre_scores_gemma":[0.81052524,0.0023275302,0.13920753,0.0005691539,0.00068771746,0.00051655626,0.025902327,0.0013938922,0.01887003],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99249846,0.003404962,0.0004248414,0.0019877169,0.0011821855,0.0005018221],"domain_scores_gemma":[0.9844148,0.009313414,0.00043914327,0.0021347767,0.003244169,0.0004537214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006748725,0.0028837908,0.0016422864,0.0032624076,0.0012860763,0.0020574871,0.0024967848,0.002297914,0.0032574108],"category_scores_gemma":[0.0142482,0.0005900293,0.0014110598,0.0017062145,0.0007769265,0.0033379982,0.0025889801,0.0021373944,0.005046386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002491933,0.002186002,0.022465173,0.0016487187,0.0016833135,0.00024076464,0.0007481126,0.06586712,0.0067159226,0.0018405648,0.035547,0.8585654],"study_design_scores_gemma":[0.00023320894,0.001099956,0.014671122,0.00018783753,0.0004850258,0.0004006028,0.0010860887,0.9453628,0.020022454,0.0060368516,0.010275358,0.0001386801],"about_ca_topic_score_codex":0.008830061,"about_ca_topic_score_gemma":0.016415277,"teacher_disagreement_score":0.008830061,"about_ca_system_score_codex":0.0012808058,"about_ca_system_score_gemma":0.001567876,"threshold_uncertainty_score":0.035691082},"labels":[],"label_agreement":null},{"id":"W2906822531","doi":"10.4000/books.aaccademia.4784","title":"Hate Speech Detection using Attention-based LSTM","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Computer science; Task (project management); Voice activity detection; Speech recognition; Artificial neural network; Layer (electronics); Natural language processing; Value (mathematics); Artificial intelligence; Speech processing; Machine learning; Engineering","score_opus":0.028009497793476095,"score_gpt":0.21654081991639834,"score_spread":0.18853132212292226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906822531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08182223,0.00448609,0.861582,0.0009122993,0.0008902697,0.00017574609,0.002078748,0.021149501,0.026903061],"genre_scores_gemma":[0.65565896,0.0017821032,0.29842442,0.00075859233,0.0003529983,0.00016652561,0.004083599,0.0006452294,0.03812762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974126,0.000047431822,0.000011397933,0.00009953317,0.00005906268,0.000041174913],"domain_scores_gemma":[0.9996817,0.00014129495,0.000025175012,0.000038959482,0.00009774911,0.000015219536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003993189,0.0010357117,0.00042774418,0.00071288546,0.00031823217,0.00079281256,0.00080250675,0.00081105746,0.0050139106],"category_scores_gemma":[0.0010602585,0.00026564355,0.0005309332,0.0006537128,0.00024292807,0.0010615016,0.000686611,0.0008919205,0.0033708196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002125442,0.00014184079,0.0013316548,0.00028867333,0.00010018844,0.00028213146,0.00017199735,0.022444915,0.08951719,0.0021164513,0.016356573,0.86703575],"study_design_scores_gemma":[0.000018354149,0.00014940335,0.0034347211,0.00007130623,0.00012618345,0.00037568633,0.00007611008,0.89617544,0.07651596,0.005489818,0.01751801,0.000049052993],"about_ca_topic_score_codex":0.0033189654,"about_ca_topic_score_gemma":0.006456108,"teacher_disagreement_score":0.0050139106,"about_ca_system_score_codex":0.00060032407,"about_ca_system_score_gemma":0.00036264694,"threshold_uncertainty_score":0.016773224},"labels":[],"label_agreement":null},{"id":"W2907651791","doi":"10.1080/21582041.2018.1563305","title":"Offensive communications: exploring the challenges involved in policing social media","year":2019,"lang":"en","type":"article","venue":"Contemporary Social Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast; Leverhulme Trust","keywords":"Offensive; Commit; Criminal justice; Public relations; Social media; Political science; Criminology; Pace; Sociology; Law; Engineering","score_opus":0.14495216467289712,"score_gpt":0.28681503652087376,"score_spread":0.14186287184797663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907651791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8143766,0.003926408,0.011176989,0.051977117,0.00035076804,0.00059324154,0.000104143335,0.00005163219,0.11744315],"genre_scores_gemma":[0.9888837,0.0027303414,0.0032829405,0.001570588,0.00009402019,0.00016695171,0.00003409306,0.000033179786,0.003204227],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.97157794,0.022096813,0.0006109677,0.00086572906,0.002210296,0.002638248],"domain_scores_gemma":[0.95829993,0.03202344,0.0040037814,0.0009448288,0.0032120456,0.0015159214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0171476,0.00077070325,0.00063371036,0.0052437074,0.017107746,0.02521045,0.0027710542,0.0050079604,0.0024972053],"category_scores_gemma":[0.04469292,0.00084303704,0.00041171224,0.004638267,0.018110622,0.023480019,0.008515259,0.0037760967,0.0006348771],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005084716,0.00013306281,0.025005758,0.00049292325,0.00002513955,0.0016533457,0.8798516,0.00044492338,0.00034727552,0.043571793,0.0035282793,0.044895142],"study_design_scores_gemma":[0.000002153384,0.000024683173,0.002195476,0.00025602817,0.0000045634815,0.00015008349,0.98226595,0.0003875807,0.000083252584,0.0045051933,0.010115291,0.0000097525435],"about_ca_topic_score_codex":0.021662118,"about_ca_topic_score_gemma":0.040705018,"teacher_disagreement_score":0.02521045,"about_ca_system_score_codex":0.0089289835,"about_ca_system_score_gemma":0.010478165,"threshold_uncertainty_score":0.09068626},"labels":[],"label_agreement":null},{"id":"W2907894232","doi":"10.4000/books.aaccademia.4698","title":"Detecting Hate Speech Against Women in English Tweets","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Offensive; Task (project management); Identification (biology); Computer science; Voice activity detection; Speech recognition; Natural language processing; Social media; Value (mathematics); Artificial intelligence; Speech processing; World Wide Web; Machine learning; Engineering; Operations research","score_opus":0.01642825797693301,"score_gpt":0.19886743083769035,"score_spread":0.18243917286075734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907894232","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7936414,0.006219496,0.10852823,0.0057582133,0.0010290712,0.00023680447,0.0085685095,0.003818244,0.07219996],"genre_scores_gemma":[0.9030438,0.0022992392,0.046880797,0.0004964197,0.00036731226,0.00007680026,0.0077477763,0.0002838521,0.038803995],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996619,0.00009213699,0.000015841793,0.00008378438,0.00010756589,0.000038687915],"domain_scores_gemma":[0.9985662,0.00092762057,0.00010778133,0.0000953793,0.000251072,0.000052072744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007031608,0.0007883538,0.00028250905,0.0012213924,0.00054973847,0.0012598562,0.00041624584,0.0006337587,0.0030094434],"category_scores_gemma":[0.00284906,0.00020547205,0.00039048173,0.00053347566,0.0002771005,0.0012322494,0.00051128253,0.0007519832,0.0028526832],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000318017,0.0003051796,0.109502904,0.0004912713,0.00016815329,0.0006083349,0.0028856457,0.008393056,0.041075483,0.0035014,0.05406845,0.7786821],"study_design_scores_gemma":[0.000022547034,0.00050628796,0.2420121,0.0005098473,0.00027574415,0.0017192448,0.004682077,0.51792675,0.111236095,0.010707951,0.11019987,0.00020149321],"about_ca_topic_score_codex":0.0050025694,"about_ca_topic_score_gemma":0.012334839,"teacher_disagreement_score":0.0050025694,"about_ca_system_score_codex":0.00044101765,"about_ca_system_score_gemma":0.0002927495,"threshold_uncertainty_score":0.010067582},"labels":[],"label_agreement":null},{"id":"W2909073991","doi":"10.7202/1054353ar","title":"The Regulation of Hateful and Hurtful Speech: Liberalism’s Uncomfortable Predicament","year":2018,"lang":"en","type":"article","venue":"McGill Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Liberalism; Politics; Argument (complex analysis); Normative; Harm; Sociology; Law; Political science; Law and economics","score_opus":0.010978276933889202,"score_gpt":0.22115124661875163,"score_spread":0.21017296968486243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909073991","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09906801,0.0055946372,0.023993744,0.459258,0.0008982402,0.000042153442,0.00004085254,0.00012611762,0.41097826],"genre_scores_gemma":[0.94938165,0.0008409606,0.00241875,0.030144233,0.00092625106,0.00010414989,0.000012053938,0.00007558309,0.016096469],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.986249,0.007921694,0.00040822933,0.0012994217,0.0029095563,0.0012122117],"domain_scores_gemma":[0.97286475,0.01917825,0.0016456309,0.0028932448,0.0018964052,0.0015216651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016104536,0.00035070046,0.0005973684,0.00091800815,0.008575401,0.015762553,0.0022295201,0.009639673,0.0019971556],"category_scores_gemma":[0.017388508,0.0003681073,0.0005672216,0.0005830036,0.08670648,0.008098789,0.007280777,0.016007794,0.0006101444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008126676,0.000012710641,0.00015217761,0.000012851924,0.0000025892036,0.000037510235,0.009264181,0.000097265394,0.0002053155,0.98498327,0.0019681072,0.0032559077],"study_design_scores_gemma":[0.00001820637,0.000023950704,0.000454913,0.00011034632,0.000008733363,0.00006182393,0.0063027246,0.0005946205,0.00075391564,0.938662,0.052975383,0.00003335946],"about_ca_topic_score_codex":0.005572236,"about_ca_topic_score_gemma":0.0049347766,"teacher_disagreement_score":0.016104536,"about_ca_system_score_codex":0.007120849,"about_ca_system_score_gemma":0.005754697,"threshold_uncertainty_score":0.08516991},"labels":[],"label_agreement":null},{"id":"W2911244646","doi":"10.1177/2053168018816228","title":"Politicians in the line of fire: Incivility and the treatment of women on social media","year":2019,"lang":"en","type":"article","venue":"Research & Politics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":215,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Incivility; Politics; Social media; Face (sociological concept); Public relations; Public opinion; Test (biology); Political science; Sociology; Social psychology; Media studies; Criminology; Psychology; Law; Social science","score_opus":0.0630384512932419,"score_gpt":0.3518732818706828,"score_spread":0.28883483057744086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911244646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9933206,0.00010701532,0.00052394473,0.0006458286,0.000010608308,0.000009450559,0.00008917085,0.00000881732,0.0052845385],"genre_scores_gemma":[0.99903935,0.000048592283,0.00011394466,0.00007361312,0.0000085644515,0.000004788793,0.00003541906,0.0000034732125,0.00067228894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99895394,0.00049200054,0.000028707624,0.00011023769,0.00017326127,0.0002417769],"domain_scores_gemma":[0.9924045,0.00309284,0.0031047438,0.00035433777,0.00044375937,0.0005997597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014250618,0.00024356911,0.00022867451,0.0011804586,0.0013881994,0.0020600166,0.00049130205,0.0005547566,0.003543645],"category_scores_gemma":[0.011439086,0.00016193104,0.00022394376,0.0009765904,0.0014486029,0.0012308494,0.0009437662,0.0010382793,0.0005269947],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001684331,0.00010720091,0.9676784,0.000021570106,0.000039669147,0.00009320476,0.00839271,0.0004016086,0.00052935444,0.001016537,0.0006347118,0.020916533],"study_design_scores_gemma":[0.000008196611,0.00012916232,0.95385593,0.000084447405,0.00006355322,0.00018982317,0.030846935,0.007123541,0.0016081264,0.0015345099,0.0045270333,0.000028560435],"about_ca_topic_score_codex":0.054002624,"about_ca_topic_score_gemma":0.09763771,"teacher_disagreement_score":0.054002624,"about_ca_system_score_codex":0.001341003,"about_ca_system_score_gemma":0.0008437821,"threshold_uncertainty_score":0.107376575},"labels":[],"label_agreement":null},{"id":"W2911469717","doi":"10.2139/ssrn.1762559","title":"The Impossibility of Community Standards","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Impossibility; Political science; Law","score_opus":0.015878549173734817,"score_gpt":0.24671279190017678,"score_spread":0.23083424272644196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911469717","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14335021,0.0010013396,0.363868,0.0568503,0.0008614743,0.00032271413,0.0008736029,0.0013362226,0.4315361],"genre_scores_gemma":[0.9512103,0.00027958633,0.031318516,0.0019144176,0.00041556108,0.00030857677,0.00028753988,0.00024573045,0.0140197305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96995914,0.011582222,0.0016703166,0.005544678,0.008504628,0.0027389103],"domain_scores_gemma":[0.76917607,0.11772595,0.008411088,0.075141355,0.02309746,0.0064480575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020544631,0.0004515546,0.0018477787,0.0025067865,0.010773981,0.00979404,0.0043719923,0.010327027,0.018417822],"category_scores_gemma":[0.16538057,0.0012562213,0.0015312918,0.0027905193,0.016545454,0.028747277,0.012347684,0.010474254,0.003304369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028336512,0.000027708173,0.00053161505,0.000030173378,0.000009711532,0.000043249576,0.0005008372,0.000506903,0.00015662114,0.9859207,0.0030047968,0.009239291],"study_design_scores_gemma":[0.00002738504,0.000014437014,0.00025508885,0.000025743633,0.000008870414,0.00011102413,0.00034599713,0.003131581,0.00036855927,0.9875079,0.008186488,0.000016833601],"about_ca_topic_score_codex":0.0042769494,"about_ca_topic_score_gemma":0.002920505,"teacher_disagreement_score":0.020544631,"about_ca_system_score_codex":0.0022686806,"about_ca_system_score_gemma":0.0045489375,"threshold_uncertainty_score":0.1086517},"labels":[],"label_agreement":null},{"id":"W2911692568","doi":"10.1002/pra2.2018.14505501009","title":"“Being a butt while on the internet”: Perceptions of what is and isn't internet trolling","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Association for Information Science and Technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"The Internet; Psychology; Internet privacy; Prosocial behavior; Odds; Hacker; Perception; Social psychology; Computer science; World Wide Web; Computer security","score_opus":0.007681786427554117,"score_gpt":0.2234921449714792,"score_spread":0.21581035854392508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911692568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98666245,0.00047444322,0.00060518633,0.0030054648,0.00007564814,0.00002185342,0.000019462543,0.000008908741,0.00912657],"genre_scores_gemma":[0.9987185,0.0003734998,0.000084410996,0.00047456648,0.000026138436,0.00000666566,0.000010056777,0.0000061721926,0.00030007746],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.992636,0.00491359,0.00035641616,0.0004005801,0.0009185578,0.0007747612],"domain_scores_gemma":[0.97111356,0.01415932,0.0069747274,0.0007846265,0.0024247614,0.0045430562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009637165,0.00045415698,0.0005032931,0.0020740347,0.005218341,0.008874437,0.0010044756,0.0028021967,0.0036910204],"category_scores_gemma":[0.026207533,0.00056676735,0.00049288257,0.0011457605,0.011534917,0.00798149,0.004251594,0.0051523787,0.00039914172],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000740346,0.00010445393,0.051694866,0.000117348005,0.00003461332,0.00050049403,0.9347293,0.00006692917,0.00095170125,0.0037614768,0.0009969939,0.006967923],"study_design_scores_gemma":[0.0000029703278,0.00005210928,0.015090449,0.00010705649,0.000008120243,0.00018348049,0.9808724,0.00014911694,0.00007281983,0.00040503856,0.003034843,0.00002159821],"about_ca_topic_score_codex":0.0107351625,"about_ca_topic_score_gemma":0.009421094,"teacher_disagreement_score":0.0107351625,"about_ca_system_score_codex":0.0019430565,"about_ca_system_score_gemma":0.0016093241,"threshold_uncertainty_score":0.0509668},"labels":[],"label_agreement":null},{"id":"W2912026968","doi":"","title":"Cyberbullying Detection Task: the EBSI-LIA-UNAM System (ELU) at COLING’18 TRAC-1","year":2018,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"TRAC; Voting; Computer science; Task (project management); Aggression; Test (biology); Set (abstract data type); Computer security; Support vector machine; Machine learning; Artificial intelligence; Psychology; Engineering; Social psychology","score_opus":0.010522783911351134,"score_gpt":0.21851184613280855,"score_spread":0.2079890622214574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912026968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61567897,0.0065561994,0.023681294,0.003865237,0.0039178994,0.00355022,0.18007593,0.13195182,0.030722508],"genre_scores_gemma":[0.45125687,0.00051014626,0.061702047,0.0011342645,0.00049868505,0.0014320599,0.45158675,0.0016096499,0.030269552],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99791926,0.0005934928,0.00011212417,0.00079630536,0.00029248322,0.00028622794],"domain_scores_gemma":[0.9984524,0.00037121275,0.000066429486,0.00041524012,0.00043858515,0.00025602803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036055949,0.003329549,0.0021857885,0.001968744,0.0016787587,0.0019016557,0.00232604,0.004042483,0.010777189],"category_scores_gemma":[0.005044878,0.00071713905,0.0011186459,0.0008903493,0.00054881285,0.0017701758,0.002810312,0.0022530903,0.017257012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034541355,0.0024612532,0.02199295,0.001051309,0.00064464356,0.0017910798,0.00061437575,0.0067531695,0.01726959,0.00071526866,0.60882753,0.3344247],"study_design_scores_gemma":[0.0024011587,0.0035850455,0.11396362,0.00052150735,0.00062755967,0.0019391268,0.0020999978,0.6233843,0.052154556,0.0029513189,0.1958968,0.00047506095],"about_ca_topic_score_codex":0.036832247,"about_ca_topic_score_gemma":0.053090654,"teacher_disagreement_score":0.036832247,"about_ca_system_score_codex":0.0016325978,"about_ca_system_score_gemma":0.0015764079,"threshold_uncertainty_score":0.07323569},"labels":[],"label_agreement":null},{"id":"W2912049096","doi":"","title":"Chapter XII. “She’s Such a Slut!”: The Sexualized Cyberbullying of Teen Girls and the Education Law Response","year":2015,"lang":"en","type":"book-chapter","venue":"OpenEdition (OpenEdition)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criminology; Psychology; Political science","score_opus":0.023191201488590166,"score_gpt":0.25025073052093916,"score_spread":0.227059529032349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912049096","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015496134,0.10168879,0.00063923385,0.016714824,0.0031008204,0.000053023086,0.00018409707,0.00004872526,0.8760208],"genre_scores_gemma":[0.027170254,0.056502603,0.00038037676,0.00663943,0.0015188539,0.000045691744,0.00016168047,0.000073212526,0.9075078],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968874,0.000043457698,0.000006157325,0.000039678416,0.00016986183,0.000052153755],"domain_scores_gemma":[0.99978286,0.0000675793,0.000013612488,0.000009380054,0.00008718447,0.000039304985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027306273,0.0005556213,0.00022157689,0.0012953646,0.004588518,0.0035990856,0.000623151,0.0019181627,0.025723679],"category_scores_gemma":[0.0006621999,0.0001990808,0.00014596901,0.0014654093,0.003339341,0.001989418,0.0011800651,0.002676466,0.004905741],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004887776,0.000024344487,0.00030330868,0.00017562383,0.0000019411846,0.000093359435,0.007821152,0.00013055188,0.00015505608,0.22221574,0.6999632,0.06911093],"study_design_scores_gemma":[6.7910844e-7,0.0000040982204,0.00063314574,0.00036995398,0.000001187954,0.00007782365,0.0015570476,0.000026672198,0.000047760113,0.0060116015,0.9912657,0.0000041904086],"about_ca_topic_score_codex":0.27496564,"about_ca_topic_score_gemma":0.49884307,"teacher_disagreement_score":0.27496564,"about_ca_system_score_codex":0.010478871,"about_ca_system_score_gemma":0.0075723515,"threshold_uncertainty_score":0.54673034},"labels":[],"label_agreement":null},{"id":"W2912706553","doi":"10.1002/pra2.2018.14505501081","title":"Online targeting of researchers/academics: Ethical obligations and best practices","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Association for Information Science and Technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Harassment; Intimidation; Public relations; Work (physics); Political science; Ethnic group; Action (physics); University faculty; Sociology; Psychology; Medical education; Law; Medicine; Engineering","score_opus":0.04593611160794733,"score_gpt":0.3377034611931117,"score_spread":0.29176734958516437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912706553","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086396754,0.010581043,0.062896185,0.6475536,0.003707173,0.0010340166,0.00009131758,0.0010349425,0.18670496],"genre_scores_gemma":[0.7547493,0.010810417,0.064861715,0.10979803,0.0021249265,0.0010363979,0.00013277018,0.0007349005,0.055751566],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83429104,0.12685052,0.005833155,0.0045058327,0.02273106,0.005788313],"domain_scores_gemma":[0.7193881,0.15111953,0.020096455,0.03777785,0.050667603,0.020950431],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.13090326,0.0005864193,0.00049158995,0.0025780026,0.013934231,0.020607632,0.0036853096,0.010527074,0.010335345],"category_scores_gemma":[0.16622579,0.00086938444,0.00058301864,0.0018761622,0.014562337,0.01805047,0.011974726,0.008903625,0.005548662],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011122293,0.0009201375,0.020280262,0.0009903987,0.00004036765,0.0014562557,0.14722139,0.0005915148,0.0031900066,0.0706629,0.14315842,0.6113772],"study_design_scores_gemma":[0.000050462033,0.0004205565,0.011194253,0.006428264,0.000057404886,0.0026766888,0.25167844,0.0021173898,0.0051205102,0.08076421,0.6392587,0.00023320215],"about_ca_topic_score_codex":0.0021242443,"about_ca_topic_score_gemma":0.00431074,"teacher_disagreement_score":0.9894729,"about_ca_system_score_codex":0.003930455,"about_ca_system_score_gemma":0.018325279,"threshold_uncertainty_score":0.6922909},"labels":[],"label_agreement":null},{"id":"W2914441138","doi":"","title":"Fact Checking the Fact Checkers: Verification Web Sites, Partisanship and Sourcing","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Typology; Set (abstract data type); Computer science; World Wide Web; Model checking; Internet privacy; Political science; Public relations; Computer security; Data science; Sociology; Programming language","score_opus":0.011918197238914905,"score_gpt":0.21278123174280672,"score_spread":0.2008630345038918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914441138","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8401046,0.0029244511,0.04198762,0.0131017575,0.00015447465,0.00019714092,0.00046727384,0.00019482484,0.10086798],"genre_scores_gemma":[0.99454874,0.0003018118,0.0031145217,0.00022951982,0.000024972907,0.000015221148,0.00006844074,0.000029121762,0.0016675341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9871133,0.007448347,0.000514489,0.00081582513,0.003089943,0.0010180721],"domain_scores_gemma":[0.8313129,0.1203411,0.02225451,0.015688892,0.008251647,0.002150863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01817383,0.00024440378,0.00034044767,0.0067005777,0.0060764104,0.010938474,0.001094896,0.0018335703,0.00450131],"category_scores_gemma":[0.091796726,0.00049597496,0.00035525346,0.007663329,0.01547393,0.014032031,0.0031261425,0.0018071703,0.00034908566],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023542663,0.00010067459,0.34303814,0.00023859122,0.00008282632,0.0016558962,0.09946342,0.001378873,0.0009725177,0.42073658,0.006612874,0.12548429],"study_design_scores_gemma":[0.00006240469,0.00011054897,0.4053021,0.0014302098,0.00019283817,0.0030004696,0.21969004,0.01691775,0.0073778094,0.2187152,0.12696064,0.00024000589],"about_ca_topic_score_codex":0.042408448,"about_ca_topic_score_gemma":0.038927875,"teacher_disagreement_score":0.042408448,"about_ca_system_score_codex":0.0050497293,"about_ca_system_score_gemma":0.0039294343,"threshold_uncertainty_score":0.09611356},"labels":[],"label_agreement":null},{"id":"W2915571541","doi":"","title":"Controversy and Consensus, Pornography and Hate Speech: The Legal Challenge to the Playboy Channel","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pornography; Supreme court; Principle of legality; Law; Political science; Free speech; Analogy; Rhetorical question; Sociology; Linguistics","score_opus":0.005354528115706227,"score_gpt":0.19969408446549824,"score_spread":0.19433955634979203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915571541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47292525,0.008125762,0.015969327,0.11280999,0.00090628775,0.00008458804,0.00008601456,0.00004196255,0.38905078],"genre_scores_gemma":[0.99239993,0.0005971658,0.00045889718,0.002430334,0.00018451162,0.000016054506,0.000014146436,0.000009733187,0.003889074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9924159,0.0040342705,0.00022382184,0.0008105958,0.0018740142,0.00064135675],"domain_scores_gemma":[0.97559154,0.01979377,0.0017412915,0.0005864042,0.0015954444,0.0006917335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008393441,0.00027729874,0.0004287372,0.0024770186,0.012730017,0.013719874,0.0010786549,0.0061520934,0.0032029718],"category_scores_gemma":[0.027584778,0.00026772698,0.00021368577,0.0021279834,0.038765114,0.007124275,0.00481719,0.006627939,0.00029100277],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046218353,0.000039964638,0.0055447216,0.00008675153,0.000014158016,0.0005693098,0.12573816,0.0002026949,0.00042327875,0.8300119,0.0060555977,0.031267237],"study_design_scores_gemma":[0.00005922935,0.000091571885,0.015116404,0.00097485346,0.00006636113,0.0008775234,0.33136398,0.0024530846,0.0021270614,0.49164534,0.15511608,0.0001084627],"about_ca_topic_score_codex":0.024585582,"about_ca_topic_score_gemma":0.029964104,"teacher_disagreement_score":0.024585582,"about_ca_system_score_codex":0.0070835925,"about_ca_system_score_gemma":0.0047789556,"threshold_uncertainty_score":0.051395297},"labels":[],"label_agreement":null},{"id":"W2921517813","doi":"10.21991/cf29373","title":"What happens when the assumptions underlying our commitment to free speech no longer hold?","year":2019,"lang":"en","type":"article","venue":"Constitutional Forum / Forum constitutionnel","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Free speech; Censorship; Psychology; State (computer science); Expression (computer science); Social psychology; Communication; Computer science; Law; Political science","score_opus":0.02943114865596558,"score_gpt":0.2668799835422001,"score_spread":0.23744883488623453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921517813","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12087079,0.0037121498,0.027097935,0.63197917,0.0039948514,0.00008327681,0.0006852284,0.00019505629,0.21138154],"genre_scores_gemma":[0.9605378,0.0017954903,0.004029495,0.024931196,0.0014728145,0.000114623566,0.00023723526,0.00012995962,0.006751478],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98208076,0.0076673813,0.0006427097,0.002897504,0.0037787622,0.0029327788],"domain_scores_gemma":[0.93885696,0.03459774,0.007856934,0.0065947683,0.007893191,0.0042004515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027464539,0.00033375254,0.0008350401,0.0015905269,0.0067005586,0.015020038,0.0026345293,0.008209791,0.010581686],"category_scores_gemma":[0.11076116,0.0005221718,0.000534856,0.0016076289,0.024512162,0.020639857,0.0034703773,0.014048092,0.0022225513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002345186,0.00016427935,0.016775113,0.00019530846,0.00008880565,0.0013258386,0.030827185,0.0006673644,0.0006713896,0.8576925,0.040860344,0.050497368],"study_design_scores_gemma":[0.000039114464,0.000045569755,0.013041439,0.00033855284,0.000032354623,0.0007628493,0.039566234,0.001448449,0.0005209664,0.8826847,0.061417002,0.0001028622],"about_ca_topic_score_codex":0.014520922,"about_ca_topic_score_gemma":0.009864683,"teacher_disagreement_score":0.027464539,"about_ca_system_score_codex":0.007992935,"about_ca_system_score_gemma":0.0041899243,"threshold_uncertainty_score":0.14524812},"labels":[],"label_agreement":null},{"id":"W2929227504","doi":"10.24908/ss.v17i1/2.12908","title":"Defunding Hate: PayPal’s Regulation of Hate Groups","year":2019,"lang":"en","type":"article","venue":"Surveillance & Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Enforcement; Face (sociological concept); Payment; Hate crime; Political science; Law and economics; Law enforcement; Business; Public relations; Internet privacy; Law; Computer security; Sociology; Computer science; Finance","score_opus":0.00833094188535715,"score_gpt":0.2157341924571607,"score_spread":0.20740325057180353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929227504","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37288862,0.0017369968,0.030613419,0.13599387,0.0019252938,0.0005092074,0.0002437472,0.0008430111,0.45524582],"genre_scores_gemma":[0.9300231,0.00053461245,0.0042999377,0.021404888,0.00025535675,0.00020894049,0.000069642476,0.00007356876,0.043129925],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99562895,0.0015370541,0.00018086302,0.0005528605,0.0012698941,0.0008304296],"domain_scores_gemma":[0.98692316,0.0076539903,0.0010772703,0.0013940381,0.0020651056,0.00088640413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049975994,0.0003282164,0.00017813282,0.0013469029,0.0079426775,0.009395967,0.0012345121,0.004075807,0.0070635374],"category_scores_gemma":[0.020440938,0.0004375527,0.000362338,0.0006036631,0.004915786,0.0045205904,0.005446051,0.006946232,0.0011837572],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015360267,0.00065193535,0.06913896,0.0003530384,0.000075595424,0.0015196495,0.03560227,0.000975892,0.007983203,0.3436045,0.15782598,0.38211533],"study_design_scores_gemma":[0.00006836068,0.00050894497,0.06543835,0.0009598247,0.000108110784,0.0019437397,0.036133926,0.00853274,0.015809659,0.06596998,0.8042438,0.00028260608],"about_ca_topic_score_codex":0.013708259,"about_ca_topic_score_gemma":0.014818208,"teacher_disagreement_score":0.013708259,"about_ca_system_score_codex":0.0023273018,"about_ca_system_score_gemma":0.006200972,"threshold_uncertainty_score":0.027256966},"labels":[],"label_agreement":null},{"id":"W2935162758","doi":"10.2196/11263","title":"A Software Tool Aimed at Automating the Generation, Distribution, and Assessment of Social Media Messages for Health Promotion and Education Research","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; Center for Tobacco Products; National Institutes of Health; Southern California Clinical and Translational Science Institute","keywords":"Social media; Health promotion; Health communication; Software; Promotion (chess); Population health; Internet privacy; Computer science; Population; Public relations; Data science; Public health; Business; Psychology; World Wide Web; Medicine; Environmental health; Political science","score_opus":0.05331368785577468,"score_gpt":0.37856456073174427,"score_spread":0.3252508728759696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935162758","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023615174,0.00017594283,0.62409085,0.00047262583,0.00024806996,0.005719228,0.010422978,0.33256665,0.0026884745],"genre_scores_gemma":[0.07838266,0.00018116538,0.8826334,0.00042718044,0.00012347056,0.011733249,0.00957671,0.012380052,0.0045620897],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99309856,0.0025858807,0.0012412932,0.0013405409,0.0015079662,0.0002257172],"domain_scores_gemma":[0.89447665,0.08627475,0.0039489814,0.008389337,0.005625645,0.0012846487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019023277,0.0025133144,0.0012165931,0.0048763175,0.0007766578,0.002067483,0.0023629316,0.0012068563,0.013018713],"category_scores_gemma":[0.07257573,0.0015063665,0.0013719407,0.0019800202,0.0010446551,0.003031724,0.003689847,0.0014345059,0.0053146626],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026898144,0.0013188329,0.018565802,0.0035280504,0.00042406024,0.000736726,0.0043488373,0.004507112,0.032658387,0.0049063894,0.07788906,0.8484268],"study_design_scores_gemma":[0.005341996,0.00531914,0.05374019,0.002863294,0.001212507,0.0027956886,0.0019382923,0.3378825,0.19554791,0.04428386,0.34786704,0.0012075289],"about_ca_topic_score_codex":0.0019584107,"about_ca_topic_score_gemma":0.0015155871,"teacher_disagreement_score":0.019023277,"about_ca_system_score_codex":0.0009579371,"about_ca_system_score_gemma":0.0029468352,"threshold_uncertainty_score":0.100605965},"labels":[],"label_agreement":null},{"id":"W2942856046","doi":"","title":"When Law Frees Us to Speak","year":2019,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Silence; Argument (complex analysis); Harassment; Function (biology); Sexual assault; Face (sociological concept); Empirical research; Dimension (graph theory); Law; Sociology; Political science; Social psychology; Criminology; Psychology; Poison control; Human factors and ergonomics; Epistemology","score_opus":0.009149796041075145,"score_gpt":0.22044044712751903,"score_spread":0.2112906510864439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942856046","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08099384,0.0037352003,0.020360295,0.17245166,0.009308517,0.00014677049,0.00031201323,0.0008612929,0.7118304],"genre_scores_gemma":[0.73795545,0.0017250652,0.0025173263,0.07035935,0.0023051023,0.00014973432,0.00017410862,0.0006137889,0.18420006],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99219185,0.002648825,0.00038937168,0.000922111,0.0026281865,0.0012196772],"domain_scores_gemma":[0.99075794,0.0037885879,0.0014206799,0.001763443,0.0012323121,0.0010369697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043692756,0.00040247123,0.00050936476,0.0010699414,0.0075826645,0.0095828185,0.0008148437,0.0059299422,0.021829337],"category_scores_gemma":[0.030790145,0.00040781795,0.00045157658,0.0004886101,0.017004635,0.0115852235,0.0062048724,0.009917934,0.00997001],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008291272,0.00010460619,0.0065054735,0.00015957934,0.000038444316,0.0046698954,0.11276554,0.00009426259,0.003136938,0.6392254,0.17357579,0.059641078],"study_design_scores_gemma":[0.000012634292,0.000059344326,0.002419493,0.000507148,0.000032127118,0.0035999338,0.06795438,0.00022149265,0.0025204038,0.10033966,0.82226455,0.000069006455],"about_ca_topic_score_codex":0.0028851181,"about_ca_topic_score_gemma":0.0025876139,"teacher_disagreement_score":0.021829337,"about_ca_system_score_codex":0.0014055021,"about_ca_system_score_gemma":0.00206503,"threshold_uncertainty_score":0.07302642},"labels":[],"label_agreement":null},{"id":"W2945761407","doi":"10.31234/osf.io/x9y2p","title":"The psychology of hate: Moral concerns differentiate hate from dislike","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Morality; Negativity effect; Psychology; Social psychology; Conceptualization; Complaint; Valence (chemistry); Negativity bias; Epistemology; Political science; Linguistics","score_opus":0.03585986518770849,"score_gpt":0.2964003614192071,"score_spread":0.2605404962314986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945761407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9910443,0.00020977642,0.0012512621,0.00018189399,0.000015349553,0.000021296726,0.000025356805,0.0000065840863,0.007244178],"genre_scores_gemma":[0.9990226,0.000059108832,0.00042760963,0.000112933834,0.00001105577,0.000010085314,0.000019142104,0.0000032578448,0.00033416483],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998982,0.0003783737,0.00006542479,0.00013433439,0.00033921242,0.0001007202],"domain_scores_gemma":[0.9873663,0.0042224354,0.0050059143,0.0007379785,0.0013582857,0.0013090717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024348742,0.00032786236,0.00026713952,0.0012291173,0.0008338419,0.0018217127,0.00027255996,0.0006816877,0.0016693496],"category_scores_gemma":[0.012673628,0.00020844003,0.00022172312,0.0004889365,0.0032829333,0.0018425137,0.0013966764,0.0014313898,0.00018782304],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011077072,0.00093173434,0.77596164,0.0005453366,0.00012268078,0.0005303503,0.08642507,0.00027244515,0.0721101,0.012206016,0.0009244226,0.048862405],"study_design_scores_gemma":[0.000015046575,0.0003379707,0.9729279,0.00006484075,0.000031342086,0.0005401143,0.017336898,0.0005205212,0.003466374,0.002814013,0.0019060847,0.00003889857],"about_ca_topic_score_codex":0.0017547582,"about_ca_topic_score_gemma":0.0028208646,"teacher_disagreement_score":0.0024348742,"about_ca_system_score_codex":0.0008873652,"about_ca_system_score_gemma":0.00044992447,"threshold_uncertainty_score":0.012877047},"labels":[],"label_agreement":null},{"id":"W2947628635","doi":"10.48550/arxiv.1906.00896","title":"Can Women Break the Glass Ceiling?: An Analysis of #MeToo Hashtagged Posts on Twitter","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Silence; Social media; Harassment; Glass ceiling; Political science; Advertising; Media studies; Sociology; Law; Business; Art","score_opus":0.04173861887019083,"score_gpt":0.18829274086148173,"score_spread":0.14655412199129092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947628635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98163533,0.000666957,0.0009070606,0.0014860365,0.00013993797,0.00007691698,0.008700077,0.000072538925,0.006315053],"genre_scores_gemma":[0.98557335,0.0008741197,0.0011717982,0.0004948856,0.00026093246,0.00013151801,0.007395053,0.000070196074,0.0040281625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99936765,0.00016645763,0.000051461182,0.000102621525,0.00016291828,0.00014884994],"domain_scores_gemma":[0.9972383,0.0014759625,0.00067501125,0.00011728315,0.00029990144,0.00019354635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053429673,0.00039221483,0.00026760093,0.0023669447,0.0010438608,0.00143097,0.00034235185,0.0007256493,0.0016699671],"category_scores_gemma":[0.00372976,0.00019495492,0.00023099734,0.0029094948,0.0006419689,0.0017957687,0.0012067362,0.0007196766,0.001421152],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010038378,0.00024912594,0.681207,0.0012138193,0.00020796817,0.005621126,0.113340154,0.00076215883,0.021680977,0.0033914277,0.04035848,0.13096392],"study_design_scores_gemma":[0.000016726399,0.00016616084,0.75318176,0.00034624618,0.000086825865,0.0017127005,0.14840177,0.005162575,0.0044538844,0.0014828352,0.08490032,0.00008814777],"about_ca_topic_score_codex":0.0068497346,"about_ca_topic_score_gemma":0.0144287255,"teacher_disagreement_score":0.0068497346,"about_ca_system_score_codex":0.0004841912,"about_ca_system_score_gemma":0.00032553825,"threshold_uncertainty_score":0.013619721},"labels":[],"label_agreement":null},{"id":"W2948053614","doi":"","title":"Police-reported hate crime in Canada 2011","year":2013,"lang":"en","type":"article","venue":"IB","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criminology; Hate crime; Political science; Sociology","score_opus":0.011146506746581693,"score_gpt":0.20209229931083633,"score_spread":0.19094579256425465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948053614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9259238,0.0022763072,0.00037165204,0.0040545138,0.0002512827,0.00020883251,0.05366453,0.00012359943,0.013125341],"genre_scores_gemma":[0.9672093,0.0020473036,0.00030988996,0.00095443364,0.00006172947,0.00009016102,0.0139717525,0.00004858783,0.015306967],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981665,0.00010718529,0.000116498864,0.00016957145,0.00058854115,0.0008517799],"domain_scores_gemma":[0.9925659,0.00021512188,0.00090048456,0.000093752235,0.004255205,0.0019695028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089792354,0.0007221654,0.0009709492,0.0040226826,0.005976887,0.0028071683,0.0027533837,0.0016067941,0.0044106366],"category_scores_gemma":[0.0036475635,0.0009768147,0.0010678626,0.0062006367,0.0010585862,0.0009745061,0.0017096424,0.003300342,0.00082556467],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024407842,0.0002093577,0.96057683,0.0001420045,0.00020129242,0.000249656,0.0021096673,0.00042166858,0.00017630577,0.00048748174,0.025052939,0.010128841],"study_design_scores_gemma":[0.0000141107585,0.000027438862,0.9910608,0.00009208867,0.000047958343,0.000073565134,0.0038764954,0.00049240165,0.000104905215,0.000037641537,0.0041414876,0.00003113275],"about_ca_topic_score_codex":0.99893206,"about_ca_topic_score_gemma":0.99961674,"teacher_disagreement_score":0.07211048,"about_ca_system_score_codex":0.07211048,"about_ca_system_score_gemma":0.09880933,"threshold_uncertainty_score":0.52320063},"labels":[],"label_agreement":null},{"id":"W2948178099","doi":"","title":"One Night of Pleasure or Risk","year":2018,"lang":"en","type":"article","venue":"MacEwan University Student Research Proceedings","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Pleasure; Hook; Psychology; Sexual violence; Social psychology; Sexual attraction; Sexual abuse; Criminology; Suicide prevention; Poison control; Sexual behavior; Medical emergency; Medicine","score_opus":0.0966976881498203,"score_gpt":0.3421495791866062,"score_spread":0.2454518910367859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948178099","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1355144,0.009152999,0.004333447,0.064442575,0.019893713,0.00040332734,0.001902335,0.0010530618,0.76330423],"genre_scores_gemma":[0.3872913,0.00596042,0.002931521,0.026059642,0.0035234673,0.00025835852,0.00081126083,0.00045569646,0.57270837],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99838173,0.00052097224,0.00006560997,0.00019566012,0.00046506137,0.00037090995],"domain_scores_gemma":[0.99448496,0.0002613652,0.00042996148,0.00046045595,0.0006310551,0.003732118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096409785,0.0004902607,0.00067969295,0.00045045684,0.0051984917,0.0053094416,0.0012992877,0.0015763598,0.12095929],"category_scores_gemma":[0.005811537,0.00028926868,0.00081695523,0.00047374252,0.002315429,0.0031275249,0.0077630957,0.0044125193,0.03257358],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026630948,0.0009430315,0.02600741,0.00074694894,0.00007127094,0.0010055664,0.033645723,0.00008730323,0.0017269508,0.020446118,0.6458307,0.2692226],"study_design_scores_gemma":[0.00002473613,0.00036621714,0.026464174,0.0004160515,0.000018104038,0.0010683574,0.041869607,0.000060386916,0.00025758758,0.003938056,0.9254531,0.00006361492],"about_ca_topic_score_codex":0.0033469829,"about_ca_topic_score_gemma":0.0074458453,"teacher_disagreement_score":0.12095929,"about_ca_system_score_codex":0.0015023964,"about_ca_system_score_gemma":0.0018661311,"threshold_uncertainty_score":0.40464914},"labels":[],"label_agreement":null},{"id":"W2948877201","doi":"10.18653/v1/s19-2136","title":"UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of British Columbia","funders":"","keywords":"SemEval; Offensive; Task (project management); Macro; Computer science; Artificial intelligence; Natural language processing; Ensemble learning; Machine learning; Training set; Host (biology); Mathematics; Operations research; Biology","score_opus":0.07137152691733431,"score_gpt":0.2596168888994028,"score_spread":0.1882453619820685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948877201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6741408,0.004028979,0.09883227,0.0050400314,0.005489338,0.001874085,0.08249374,0.06155706,0.06654366],"genre_scores_gemma":[0.6382324,0.0005667365,0.11847106,0.0012441236,0.00088767055,0.0010994377,0.1926354,0.0036244032,0.043238696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957495,0.0016432677,0.00018073694,0.0010731615,0.00086662034,0.00048667568],"domain_scores_gemma":[0.99268407,0.0023238964,0.00024549977,0.0025176294,0.001617623,0.0006112352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068139737,0.00241392,0.0018836224,0.0021877298,0.001500965,0.001824064,0.0015391903,0.0026362902,0.01536012],"category_scores_gemma":[0.013044472,0.00050053885,0.0009730045,0.0018005292,0.0005474373,0.0028324502,0.0034158635,0.002593576,0.020324454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026159077,0.0026436883,0.020326188,0.0007794691,0.0005254522,0.0007790269,0.00052691496,0.025806274,0.02081078,0.0010628656,0.43679726,0.48732626],"study_design_scores_gemma":[0.0010297286,0.0025254458,0.056128085,0.00026970787,0.00030961036,0.0012235702,0.001651934,0.6661557,0.07646762,0.007366944,0.18661608,0.0002556625],"about_ca_topic_score_codex":0.005791001,"about_ca_topic_score_gemma":0.010629554,"teacher_disagreement_score":0.01536012,"about_ca_system_score_codex":0.00083756587,"about_ca_system_score_gemma":0.00087322417,"threshold_uncertainty_score":0.051384687},"labels":[],"label_agreement":null},{"id":"W2950756429","doi":"","title":"Social Media Threats: Examining the Canadian Criminal Law Response","year":2018,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fondation pour la recherche juridique","keywords":"Law; Criminology; Criminal law; Political science; Social media; Sociology","score_opus":0.03680112918806285,"score_gpt":0.26758500189701073,"score_spread":0.23078387270894787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950756429","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7299266,0.004417422,0.0007147409,0.03993367,0.00039164492,0.00035218208,0.0027310133,0.00006540542,0.22146735],"genre_scores_gemma":[0.9736878,0.0040460643,0.0006682892,0.003766101,0.00008786088,0.000094821946,0.00068804965,0.000040025545,0.01692097],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929898,0.0005498679,0.00015069917,0.00039650116,0.003865192,0.0020480638],"domain_scores_gemma":[0.9874988,0.0021545198,0.0013211325,0.00019329748,0.0072056893,0.0016265472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00424287,0.0005990612,0.00038439466,0.015108163,0.029731888,0.007853314,0.0026943858,0.0021624877,0.004610058],"category_scores_gemma":[0.016395047,0.0005700247,0.00038467324,0.018631874,0.007978392,0.0023340941,0.0036700096,0.0028603396,0.00036352008],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017777964,0.00017942859,0.23636317,0.00070441136,0.00007163806,0.0025671008,0.4266411,0.001137813,0.001233173,0.099388294,0.10185929,0.12967685],"study_design_scores_gemma":[0.000013173188,0.0000441373,0.33004513,0.00045287915,0.0000434817,0.00029552926,0.41557226,0.00083366915,0.00046988146,0.0017557695,0.25031245,0.00016158586],"about_ca_topic_score_codex":0.99845564,"about_ca_topic_score_gemma":0.99950004,"teacher_disagreement_score":0.22983788,"about_ca_system_score_codex":0.22983788,"about_ca_system_score_gemma":0.23795968,"threshold_uncertainty_score":0.8932789},"labels":[],"label_agreement":null},{"id":"W2953003953","doi":"10.4000/books.aaccademia.4740","title":"Vector Space Models for Automatic Misogyny Identification","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Exploit; Computer science; Task (project management); Identification (biology); Space (punctuation); Artificial intelligence; Natural language processing; Vector space; The Internet; Language identification; Human–computer interaction; Natural language; World Wide Web; Engineering; Computer security; Mathematics; Systems engineering","score_opus":0.029631030605453374,"score_gpt":0.21813463599650826,"score_spread":0.18850360539105487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953003953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014796355,0.0036655378,0.9724708,0.0008430572,0.00023968228,0.000072122326,0.0008869781,0.0033434324,0.0036821414],"genre_scores_gemma":[0.53580016,0.0048369854,0.4142016,0.0005624974,0.0006160458,0.0004648025,0.0052872063,0.00095757516,0.037273172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992204,0.00029952615,0.000056118723,0.00019238553,0.00016721408,0.0000644033],"domain_scores_gemma":[0.9985007,0.00090975,0.00010154208,0.00013809353,0.0003111959,0.000038758426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010355682,0.0012559593,0.0009013446,0.0010604454,0.0005018787,0.0018016822,0.0011846483,0.0011999449,0.008687673],"category_scores_gemma":[0.0038569297,0.00049091165,0.00084441097,0.0013365784,0.0004303189,0.0022846807,0.0011103594,0.002714908,0.004716294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032126615,0.00017749636,0.0024639734,0.00024511365,0.0001777969,0.00014563183,0.0003086818,0.26017928,0.004469918,0.037183437,0.02372838,0.6705991],"study_design_scores_gemma":[0.0000062269737,0.000023816903,0.0003766319,0.000014199293,0.000010163748,0.000029117253,0.000027731277,0.97374517,0.0007892007,0.021442743,0.0035199858,0.000015102577],"about_ca_topic_score_codex":0.009057044,"about_ca_topic_score_gemma":0.008853642,"teacher_disagreement_score":0.009057044,"about_ca_system_score_codex":0.00089733844,"about_ca_system_score_gemma":0.00071476534,"threshold_uncertainty_score":0.029063165},"labels":[],"label_agreement":null},{"id":"W2953775737","doi":"10.22230/cjc.2019v44n2a3511","title":"Political Bots: Disrupting Canada’s Democracy","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University; University of Ottawa","funders":"","keywords":"Politics; Attribution; Democracy; Enforcement; Internet privacy; Social media; Identification (biology); Political science; Public opinion; Law and economics; Public relations; Law enforcement; Sociology; Computer security; Social psychology; Law; Computer science; Psychology; Biology","score_opus":0.00699823150286189,"score_gpt":0.20958869810060415,"score_spread":0.20259046659774227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953775737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5716944,0.0025139593,0.004819183,0.12608926,0.0005985422,0.0002610304,0.0025481945,0.00047949477,0.29099587],"genre_scores_gemma":[0.96900016,0.0009777672,0.000907225,0.0059764665,0.000048197333,0.000029080009,0.00029754193,0.000043833395,0.022719778],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996342,0.0004646551,0.00005542173,0.00030605457,0.0016316001,0.0012001655],"domain_scores_gemma":[0.9896241,0.0031045573,0.00083350664,0.0005733163,0.0039805714,0.001883972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029610307,0.00022827978,0.00028404908,0.0021072116,0.016136535,0.005374023,0.0010426095,0.0022468176,0.0077291834],"category_scores_gemma":[0.012509004,0.00026741676,0.00023404408,0.0031574774,0.0059773684,0.0016707886,0.0019140773,0.0025599624,0.0003791358],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005220273,0.00018337293,0.28212836,0.00049231155,0.00015756607,0.002807383,0.030745994,0.0037356687,0.0024525411,0.25040314,0.25419262,0.17217906],"study_design_scores_gemma":[0.00018205702,0.00019171482,0.228755,0.00046475438,0.00023517098,0.00049865287,0.053353574,0.0125742555,0.0035608697,0.032038666,0.6678714,0.00027385773],"about_ca_topic_score_codex":0.98256725,"about_ca_topic_score_gemma":0.99123085,"teacher_disagreement_score":0.06386453,"about_ca_system_score_codex":0.06386453,"about_ca_system_score_gemma":0.099216,"threshold_uncertainty_score":0.46337175},"labels":[],"label_agreement":null},{"id":"W2954221744","doi":"10.18653/v1/s19-2116","title":"jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media","year":2019,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Offensive; SemEval; Computer science; Sentence; Task (project management); Artificial intelligence; Natural language processing; Categorization; Probabilistic logic; Social media; Recurrent neural network; Speech recognition; Artificial neural network; World Wide Web; Mathematics","score_opus":0.011169350213304724,"score_gpt":0.23633921961238277,"score_spread":0.22516986939907804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954221744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6872215,0.002703205,0.07730139,0.0039955354,0.008319735,0.004482856,0.102135405,0.043931194,0.06990924],"genre_scores_gemma":[0.5466081,0.00044377358,0.1362551,0.002131237,0.00095491676,0.0020214585,0.22504093,0.0020098463,0.08453461],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974305,0.0009322346,0.00014818023,0.0006083276,0.0006009014,0.0002798723],"domain_scores_gemma":[0.99519724,0.0014498485,0.00021029178,0.0010949408,0.0013597317,0.00068795565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004330702,0.0023334313,0.001189472,0.0016782753,0.0014933463,0.0019917712,0.0015269325,0.0026469661,0.010291933],"category_scores_gemma":[0.007002144,0.00041820324,0.0010927161,0.00065263483,0.0004920515,0.0023171403,0.0026047418,0.0022796819,0.010881424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028230236,0.0029073884,0.03185867,0.0012024632,0.000560888,0.00093890895,0.0010717432,0.0072752843,0.042154282,0.0018286009,0.36390227,0.5434764],"study_design_scores_gemma":[0.0010771061,0.0054594325,0.14624603,0.00044415865,0.0005551438,0.001735469,0.004161784,0.39239302,0.13779348,0.008985738,0.30046195,0.00068667444],"about_ca_topic_score_codex":0.009021533,"about_ca_topic_score_gemma":0.017471084,"teacher_disagreement_score":0.010291933,"about_ca_system_score_codex":0.00081402797,"about_ca_system_score_gemma":0.0013988661,"threshold_uncertainty_score":0.034429967},"labels":[],"label_agreement":null},{"id":"W2954510798","doi":"","title":"Hate crime in Canada : an overview of issues and data sources","year":2001,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Criminology; Sociology","score_opus":0.07299779202218994,"score_gpt":0.3084384772548026,"score_spread":0.2354406852326127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954510798","genre_codex":"dataset","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36051774,0.08617409,0.005431463,0.028433058,0.00073785405,0.002532248,0.46750575,0.00059886853,0.04806884],"genre_scores_gemma":[0.7061369,0.10648412,0.012221062,0.006128976,0.0004053589,0.0014470275,0.14499235,0.00036885386,0.02181541],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9918691,0.0006096309,0.00085912604,0.0007291989,0.0044849347,0.0014479465],"domain_scores_gemma":[0.9649092,0.0034945572,0.0024756677,0.0005474334,0.026718007,0.0018550704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042150095,0.0011665412,0.0013001362,0.02441675,0.007394777,0.0067109354,0.0030547758,0.0012556477,0.0036138664],"category_scores_gemma":[0.017245015,0.00086755306,0.0012281318,0.06755803,0.0017246159,0.0015000677,0.00239834,0.0018593718,0.0005073906],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020362958,0.00020777275,0.7543139,0.004160952,0.000456665,0.0007959937,0.005397296,0.0013518839,0.0002781023,0.005402752,0.11504669,0.11238443],"study_design_scores_gemma":[0.000031301035,0.000038855516,0.8969622,0.001827741,0.00034699467,0.00028016875,0.009685056,0.0015222862,0.0006303184,0.00073577365,0.08778798,0.00015134756],"about_ca_topic_score_codex":0.99901557,"about_ca_topic_score_gemma":0.9991303,"teacher_disagreement_score":0.1066204,"about_ca_system_score_codex":0.1066204,"about_ca_system_score_gemma":0.2745437,"threshold_uncertainty_score":0.7735888},"labels":[],"label_agreement":null},{"id":"W2954811041","doi":"10.18653/v1/s19-2092","title":"UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language","year":2019,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Offensive; Computer science; Language model; Language identification; Task (project management); Natural language processing; Artificial intelligence; SemEval; Character (mathematics); Language understanding; Word (group theory); Speech recognition; Linguistics; Natural language","score_opus":0.011659809836106986,"score_gpt":0.23399181160018387,"score_spread":0.2223320017640769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954811041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7230721,0.0027439562,0.08226609,0.004509862,0.0051842714,0.0030152304,0.08491626,0.028552221,0.06573995],"genre_scores_gemma":[0.74438035,0.00034491852,0.06932218,0.0015393057,0.00057889655,0.001504291,0.13870908,0.0010250569,0.04259588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99649745,0.0017764958,0.00022020595,0.00064055383,0.0005595483,0.0003057286],"domain_scores_gemma":[0.992351,0.0030742607,0.00037728497,0.0017942379,0.0016348597,0.0007683779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067449543,0.0020352702,0.0013672475,0.0012579628,0.0014531015,0.0023722516,0.0017424271,0.0037840605,0.015196639],"category_scores_gemma":[0.011598357,0.0004478406,0.0009258426,0.0005510621,0.00047578244,0.0030501788,0.0027693454,0.0029901282,0.018151764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0058012144,0.0062958524,0.046799377,0.001013374,0.00059373357,0.0013198786,0.00088361837,0.015339354,0.03827211,0.0025969092,0.31925353,0.5618311],"study_design_scores_gemma":[0.0017393326,0.0065461583,0.122624315,0.0004091831,0.00043495587,0.0027247164,0.0028980016,0.606802,0.09978537,0.011028822,0.14450109,0.00050601823],"about_ca_topic_score_codex":0.007954266,"about_ca_topic_score_gemma":0.012983146,"teacher_disagreement_score":0.015196639,"about_ca_system_score_codex":0.0008379778,"about_ca_system_score_gemma":0.0011269662,"threshold_uncertainty_score":0.050837874},"labels":[],"label_agreement":null},{"id":"W2955549617","doi":"10.18653/v1/w18-5103","title":"A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content","year":2018,"lang":"en","type":"review","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Medias Data Services (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Support vector machine; Inference; Natural language processing; Social media; Machine learning; Language identification; Speech recognition; Natural language; World Wide Web","score_opus":0.32750488386040044,"score_gpt":0.4401816853434807,"score_spread":0.11267680148308024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955549617","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063023604,0.88052607,0.090801924,0.0034117887,0.00199997,0.00073227804,0.002187121,0.0016323811,0.012406161],"genre_scores_gemma":[0.057132836,0.80060416,0.11884711,0.002834372,0.0028514853,0.0012492932,0.0059837024,0.00043364236,0.0100633325],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962872,0.00087082083,0.0004973363,0.0008415553,0.0013965578,0.00010646136],"domain_scores_gemma":[0.9825577,0.010889741,0.001321735,0.0007749743,0.0042323363,0.00022349271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006697922,0.001603857,0.002273719,0.010114755,0.00070258375,0.002439263,0.0028018353,0.0016487758,0.003187402],"category_scores_gemma":[0.017930523,0.0006270135,0.0015971344,0.0055892756,0.0009499079,0.0034673214,0.00089732785,0.0017207888,0.0045739273],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043812033,0.00007802553,0.0014940366,0.00844301,0.00013432051,0.000044701465,0.00013553302,0.00053864496,0.0015913784,0.001637468,0.016309131,0.96955],"study_design_scores_gemma":[0.000071305796,0.0007408667,0.026249655,0.029064793,0.0015221268,0.002859737,0.0010581653,0.022007942,0.023680266,0.017881537,0.8744731,0.00039049462],"about_ca_topic_score_codex":0.0026171023,"about_ca_topic_score_gemma":0.00314056,"teacher_disagreement_score":0.010114755,"about_ca_system_score_codex":0.001167925,"about_ca_system_score_gemma":0.0018732428,"threshold_uncertainty_score":0.035422444},"labels":[],"label_agreement":null},{"id":"W2955561636","doi":"10.1609/icwsm.v13i01.3264","title":"From “Welcome New Gabbers” to the Pittsburgh Synagogue Shooting: The Evolution of Gab","year":2019,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Moderation; Safe haven; Social media; Judaism; Haven; Sociology; History; Computer science; Psychology; World Wide Web; Social psychology; Mathematics; Archaeology","score_opus":0.01692607930520725,"score_gpt":0.225894551813122,"score_spread":0.20896847250791475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955561636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752524,0.00078308024,0.0007192643,0.0022559972,0.00007683469,0.000023324987,0.00026163473,0.0000669118,0.02056058],"genre_scores_gemma":[0.9958241,0.0002381742,0.00034383868,0.00026387122,0.000019687372,0.0000060734005,0.00010292785,0.000047211786,0.0031541085],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.999209,0.00031480187,0.000023075907,0.00013446542,0.00017390882,0.00014481763],"domain_scores_gemma":[0.99596316,0.001401354,0.00072446,0.00036476058,0.0006750115,0.00087133824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017726995,0.00021491417,0.00023586153,0.0021569273,0.004324187,0.0047674645,0.00045396894,0.0008292356,0.004935528],"category_scores_gemma":[0.0084025385,0.00027380258,0.000118370735,0.0014298686,0.004949167,0.0040070643,0.003130007,0.0017986952,0.00094511197],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004604639,0.00009879633,0.2257079,0.00020673386,0.000042486263,0.0024849963,0.6147406,0.00021579061,0.0055726795,0.020005744,0.015018913,0.115444936],"study_design_scores_gemma":[0.00001293333,0.00014548955,0.43424833,0.00027279503,0.000027936443,0.0015454006,0.38485938,0.0006615729,0.0020465422,0.0037543243,0.17234819,0.00007707941],"about_ca_topic_score_codex":0.024637409,"about_ca_topic_score_gemma":0.076949134,"teacher_disagreement_score":0.024637409,"about_ca_system_score_codex":0.0020725876,"about_ca_system_score_gemma":0.0010233534,"threshold_uncertainty_score":0.048987985},"labels":[],"label_agreement":null},{"id":"W2957349348","doi":"10.17721/apmv.2017.133.0.95-105","title":"«HATE SPEECH» IN INTERNATIONAL AND EUROPEAN LEGAL CONTEXT","year":2017,"lang":"en","type":"article","venue":"ACTUAL PROBLEMS OF INTERNATIONAL RELATIONS","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Democracy; Freedom of expression; Law; Context (archaeology); Political science; Expression (computer science); Term (time); Foundation (evidence); Fundamental rights; Human rights; Freedom of thought; International law; Law and economics; Sociology; Politics; Computer science; History","score_opus":0.016671594990121368,"score_gpt":0.2516493861513827,"score_spread":0.23497779116126136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957349348","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16308948,0.024622016,0.02097784,0.011094229,0.0011292524,0.000052143147,0.00020896462,0.00007340624,0.7787527],"genre_scores_gemma":[0.9848245,0.0031562832,0.0024087217,0.00088595523,0.00037743064,0.00003476227,0.000073049116,0.000015619795,0.008223749],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941158,0.0032717818,0.0004700812,0.0006697772,0.0008340951,0.00063837],"domain_scores_gemma":[0.9920731,0.0047566597,0.0013636808,0.00068099337,0.00083812117,0.00028744305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00527517,0.0003740243,0.00028634394,0.003301302,0.003171998,0.009445311,0.00047211713,0.0021407674,0.0029353788],"category_scores_gemma":[0.011093274,0.00014139005,0.00025236738,0.0029256118,0.011834108,0.005412139,0.003139748,0.0018680177,0.00033543166],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013305545,0.000011572389,0.0019632373,0.000062991865,0.0000052809837,0.00033076465,0.010937691,0.0001655443,0.00022476139,0.9690776,0.0020067433,0.015200584],"study_design_scores_gemma":[0.000007291305,0.00007755785,0.023727637,0.0013356447,0.000053596872,0.0027938145,0.041994076,0.0024654432,0.0019095287,0.38662717,0.53891903,0.0000892043],"about_ca_topic_score_codex":0.0033086056,"about_ca_topic_score_gemma":0.0022312382,"teacher_disagreement_score":0.009445311,"about_ca_system_score_codex":0.0025432536,"about_ca_system_score_gemma":0.0011309675,"threshold_uncertainty_score":0.027898133},"labels":[],"label_agreement":null},{"id":"W2959053582","doi":"10.1075/jlac.00026.jak","title":"Online hatred of women in the Incels.me forum","year":2019,"lang":"en","type":"article","venue":"Journal of Language Aggression and Conflict","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":210,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Profiling (computer programming); Incitement; Identity (music); Vernacular; Sociology; Psychology; Social psychology; Gender studies; Media studies; Computer science; Political science; Law; Linguistics; Art","score_opus":0.008065531850930818,"score_gpt":0.252304037574202,"score_spread":0.2442385057232712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959053582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958412,0.00006679291,0.0006745572,0.00013462719,0.000028344435,0.000033147073,0.00016294942,0.000048354992,0.00301002],"genre_scores_gemma":[0.9964568,0.000036001748,0.00072507,0.000059601607,0.000021849577,0.000025748877,0.00013182223,0.000011891334,0.0025311545],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99916565,0.00044904876,0.000026305643,0.000113918424,0.00013617137,0.00010892068],"domain_scores_gemma":[0.99257046,0.0044779372,0.0012507676,0.00038030714,0.00066674314,0.00065371156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014229517,0.00031713973,0.00022273234,0.0011889475,0.0013121789,0.0014372824,0.00033735347,0.00056185183,0.0032107944],"category_scores_gemma":[0.0073995567,0.0001314009,0.0001320095,0.0004922433,0.000692228,0.0013457347,0.0010289439,0.000639764,0.00058443623],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010769371,0.0008056542,0.5308024,0.0004031042,0.000048211838,0.0013362172,0.28309858,0.0004810423,0.02036775,0.0019758907,0.0076941783,0.1519101],"study_design_scores_gemma":[0.000030450252,0.0008334017,0.68907225,0.00024425256,0.000042463067,0.0011032873,0.23897584,0.008728912,0.011111836,0.0010747933,0.048669413,0.000113043876],"about_ca_topic_score_codex":0.0034840782,"about_ca_topic_score_gemma":0.008309064,"teacher_disagreement_score":0.0034840782,"about_ca_system_score_codex":0.00059406954,"about_ca_system_score_gemma":0.000221835,"threshold_uncertainty_score":0.010741115},"labels":[],"label_agreement":null},{"id":"W2965551556","doi":"10.29173/cais1068","title":"Remove me from this list: A case study of trolling in an academic mass email thread","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"The Internet; World Wide Web; Thread (computing); Internet privacy; Psychology; Computer science","score_opus":0.025774777458194873,"score_gpt":0.2612512030396228,"score_spread":0.23547642558142792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965551556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96479744,0.00043558478,0.002659418,0.012969724,0.00029975502,0.00018369604,0.00011282182,0.00015613514,0.018385325],"genre_scores_gemma":[0.9681809,0.00074529037,0.0022919744,0.0027979433,0.00030122936,0.00011765685,0.00007607306,0.00012968793,0.025359195],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99745995,0.0013377657,0.00015025126,0.00024042482,0.0004387425,0.00037283706],"domain_scores_gemma":[0.9943516,0.0025022905,0.0010336814,0.00035350755,0.00047322133,0.0012857651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001814519,0.0006786288,0.00056514476,0.0017473525,0.015220057,0.0028530348,0.0012212419,0.0052849823,0.007293005],"category_scores_gemma":[0.011252611,0.00045297644,0.0005735357,0.001254343,0.0038225674,0.004024901,0.0029626554,0.0028171437,0.0019386229],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012270104,0.00041726086,0.03300625,0.00027286715,0.000025260828,0.077014476,0.8333035,0.00022026846,0.003008183,0.0044697793,0.013861573,0.03427783],"study_design_scores_gemma":[0.000022339276,0.00049847545,0.025341598,0.0005376526,0.000046667337,0.04104482,0.8075508,0.0010169786,0.0021985976,0.0017514754,0.11988958,0.000101113306],"about_ca_topic_score_codex":0.011309504,"about_ca_topic_score_gemma":0.03912194,"teacher_disagreement_score":0.015220057,"about_ca_system_score_codex":0.0025163144,"about_ca_system_score_gemma":0.0015300142,"threshold_uncertainty_score":0.024397552},"labels":[],"label_agreement":null},{"id":"W2966845988","doi":"10.2139/ssrn.3313660","title":"Terrorist Speech under Bills C-51 and C-59 and the Othman Hamdan Case: The Continued Incoherence of Canada’s Approach","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrorism; Psychology; Criminology; Law; Political science","score_opus":0.003578817367030715,"score_gpt":0.17932854084996616,"score_spread":0.17574972348293544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966845988","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15675974,0.005680989,0.0013480785,0.48712113,0.0015831324,0.00012059489,0.00058504695,0.00003907392,0.3467622],"genre_scores_gemma":[0.84857553,0.002809766,0.0005843126,0.08547824,0.00041457746,0.000053263164,0.00011901775,0.0000705621,0.06189467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.983095,0.0017094254,0.00030125072,0.001126639,0.006023998,0.0077436795],"domain_scores_gemma":[0.9784198,0.006937882,0.001257045,0.0006255036,0.0074930843,0.0052667237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068195933,0.00047787986,0.00069145556,0.0027725194,0.0472987,0.015011533,0.0033063798,0.014655837,0.008671177],"category_scores_gemma":[0.026117623,0.0007291916,0.00054900435,0.004456659,0.018044619,0.004360212,0.0040645073,0.018238487,0.0006865256],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015435948,0.00010802659,0.02765697,0.00017515458,0.0000855642,0.0018944988,0.06331588,0.00079483795,0.0005552863,0.60020906,0.26089177,0.044158615],"study_design_scores_gemma":[0.00010668459,0.000055534776,0.09603231,0.0011171076,0.00016963766,0.00073579483,0.1678151,0.001980208,0.001267158,0.057115316,0.6731685,0.0004366845],"about_ca_topic_score_codex":0.99401677,"about_ca_topic_score_gemma":0.9979341,"teacher_disagreement_score":0.13450803,"about_ca_system_score_codex":0.13450803,"about_ca_system_score_gemma":0.22947025,"threshold_uncertainty_score":0.9759286},"labels":[],"label_agreement":null},{"id":"W2971468303","doi":"","title":"Subverting Democracy to Save Democracy: Canada’s Extra-Constitutional Approaches to Battling 'Fake News'","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Democracy; Politics; Context (archaeology); Political science; Internet privacy; Marketplace of ideas; Free speech; Scope (computer science); Law and economics; Constitutional law; Fake news; Freedom of expression; Law; Human rights; Sociology; Computer science; History","score_opus":0.02129176603461211,"score_gpt":0.20628897357663384,"score_spread":0.18499720754202173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971468303","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22773445,0.0037845552,0.018505003,0.28432024,0.0009795105,0.00016917258,0.00025953146,0.00023204956,0.4640155],"genre_scores_gemma":[0.9536178,0.0012351803,0.002810425,0.013312331,0.00010544004,0.000033144723,0.00005310415,0.000045746347,0.028786765],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9891234,0.0029262418,0.00017088169,0.0007948882,0.003932772,0.0030518365],"domain_scores_gemma":[0.9818796,0.00793917,0.0013254841,0.0014848299,0.004911235,0.0024596595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007493649,0.00044558887,0.00034967336,0.0027341817,0.029456967,0.014563001,0.0016764706,0.0047338996,0.005386693],"category_scores_gemma":[0.022337383,0.00034111599,0.00034779013,0.003051903,0.024541391,0.003891424,0.0039205234,0.0082927905,0.00044709825],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007357766,0.00006085335,0.022136284,0.00013572231,0.000034519904,0.00052011904,0.061652165,0.0010546487,0.0010408239,0.7507167,0.057627846,0.10494669],"study_design_scores_gemma":[0.00006750297,0.00008369222,0.047574732,0.0005255561,0.00011942724,0.0005711798,0.08585095,0.005614666,0.004182359,0.111458756,0.7436469,0.0003042448],"about_ca_topic_score_codex":0.9613669,"about_ca_topic_score_gemma":0.98461413,"teacher_disagreement_score":0.9363451,"about_ca_system_score_codex":0.063654885,"about_ca_system_score_gemma":0.114026085,"threshold_uncertainty_score":0.4618507},"labels":[],"label_agreement":null},{"id":"W2980235613","doi":"10.24926/15529541.3822","title":"When Cute Becomes Criminal: Emoji, Threats and Online Grooming","year":2019,"lang":"en","type":"article","venue":"Minnesota journal of law, science & technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Emoji; Exploit; Internet privacy; Vicarious liability; Psychology; Criminology; Public relations; Social psychology; Computer security; Political science; Social media; Tort; Computer science; Liability; Law","score_opus":0.015761889329670892,"score_gpt":0.263393754690004,"score_spread":0.24763186536033308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980235613","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29639608,0.56613165,0.0057941605,0.020899298,0.0018250506,0.00018186818,0.00045000832,0.000068315334,0.10825351],"genre_scores_gemma":[0.6275441,0.3549134,0.0030334413,0.0077479957,0.0006232138,0.0001043101,0.0003018405,0.00003414293,0.0056974255],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9968593,0.0016751128,0.0003750729,0.00021186186,0.00071062054,0.00016798006],"domain_scores_gemma":[0.99108535,0.0066005653,0.001466923,0.00016891435,0.00054334314,0.00013490628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002804088,0.00034808303,0.00057228113,0.0040651537,0.0013399165,0.0044961553,0.00050050905,0.0016036188,0.0018743317],"category_scores_gemma":[0.013843936,0.00024497468,0.0004722871,0.0027698933,0.0041683475,0.0046800296,0.0017622873,0.0014712645,0.0003616949],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019360498,0.000096101256,0.03362555,0.017606111,0.00034467736,0.0065418445,0.04972182,0.00024850413,0.0028438678,0.07066688,0.017976373,0.8001348],"study_design_scores_gemma":[0.00002037563,0.00029847375,0.15983294,0.05456702,0.0008448421,0.024649072,0.13369057,0.0003976704,0.0046662753,0.026819436,0.5940388,0.00017456537],"about_ca_topic_score_codex":0.0022989202,"about_ca_topic_score_gemma":0.0077376463,"teacher_disagreement_score":0.0044961553,"about_ca_system_score_codex":0.0009823982,"about_ca_system_score_gemma":0.0014316256,"threshold_uncertainty_score":0.014829576},"labels":[],"label_agreement":null},{"id":"W2981255322","doi":"10.5539/ijel.v9n6p106","title":"Injurious Effects of Hate Speech Acts in The Bluest Eye","year":2019,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psyche; White (mutation); Feeling; Psychology; Politics; Power (physics); Wickedness; Social psychology; Sociology; Aesthetics; Psychoanalysis; Epistemology; Law; Art; Philosophy; Political science","score_opus":0.005091090925554637,"score_gpt":0.2502193615054654,"score_spread":0.2451282705799108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981255322","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810404,0.0006032643,0.0012848875,0.0026448935,0.00012667425,0.0000556893,0.00006202112,0.000021306429,0.014160931],"genre_scores_gemma":[0.99377054,0.00030498623,0.0002988432,0.0005831006,0.000025247631,0.000039609607,0.000015401263,0.000026131658,0.0049361517],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99346715,0.0046639927,0.00013123358,0.0003349188,0.00073478225,0.00066799746],"domain_scores_gemma":[0.99261624,0.005051719,0.00094896596,0.00029142073,0.00048202602,0.0006095399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003481015,0.0005517035,0.00063970825,0.0008803617,0.0073215966,0.0037958836,0.0011682782,0.0017723097,0.0033535936],"category_scores_gemma":[0.009763727,0.000436686,0.00028259016,0.00046883416,0.014374814,0.0036079802,0.005324389,0.0026093407,0.0003407534],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027599062,0.000023153203,0.001026428,0.000110131885,0.0000031443474,0.0006944477,0.9922564,0.000019038658,0.0011144944,0.0026562905,0.000534138,0.0015346879],"study_design_scores_gemma":[0.0000028618053,0.000045146153,0.0009817855,0.0001120355,0.0000030563688,0.00039112158,0.98465353,0.00003985829,0.000500002,0.0003171448,0.012943809,0.000009705972],"about_ca_topic_score_codex":0.0044848532,"about_ca_topic_score_gemma":0.0065203244,"teacher_disagreement_score":0.0073215966,"about_ca_system_score_codex":0.00295408,"about_ca_system_score_gemma":0.0015584722,"threshold_uncertainty_score":0.021433473},"labels":[],"label_agreement":null},{"id":"W2981299949","doi":"","title":"Hate Speech Regulations in Canada","year":2008,"lang":"en","type":"article","venue":"Florida State University law review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Commercial speech; Political science; Law; First amendment; Business; Law and economics; Economics; Supreme court","score_opus":0.012693050713939865,"score_gpt":0.18351958916141978,"score_spread":0.17082653844747991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981299949","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17174672,0.027153017,0.0013542929,0.19912627,0.0028741832,0.00046618038,0.006783606,0.00025339262,0.5902424],"genre_scores_gemma":[0.6513472,0.019881453,0.0011503441,0.08360562,0.0007175524,0.0002140491,0.0017226076,0.0001415901,0.24121962],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9856089,0.0006133587,0.0004919498,0.00080056814,0.007132923,0.0053523313],"domain_scores_gemma":[0.9578556,0.00815004,0.0014569686,0.00073080073,0.025689216,0.0061174524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051195137,0.00054495037,0.0010381982,0.005694994,0.020173652,0.011391334,0.0040394664,0.010998218,0.011268815],"category_scores_gemma":[0.023965089,0.0011661268,0.0009920596,0.0060683466,0.00476573,0.0016845785,0.0017473549,0.0083632255,0.00093112706],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020634594,0.00029909515,0.047904197,0.0004959725,0.00017050552,0.0010819489,0.0061587277,0.0025174748,0.00094528514,0.2981586,0.5815484,0.060513515],"study_design_scores_gemma":[0.00018626427,0.00012865447,0.18819523,0.0012284546,0.0003916517,0.00026838455,0.009846414,0.0025180185,0.0016240248,0.0102354,0.7850322,0.00034528782],"about_ca_topic_score_codex":0.9976655,"about_ca_topic_score_gemma":0.9988919,"teacher_disagreement_score":0.14885654,"about_ca_system_score_codex":0.14885654,"about_ca_system_score_gemma":0.38024253,"threshold_uncertainty_score":0.9872058},"labels":[],"label_agreement":null},{"id":"W2982541006","doi":"10.5430/ijhe.v8n7p69","title":"Freedom through Online Education. Are We Safe Online?","year":2019,"lang":"en","type":"article","venue":"International Journal of Higher Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Human rights; Obligation; Intermediary; Freedom of expression; Extraterritoriality; State (computer science); Public relations; Business; Affect (linguistics); Internet privacy; The Internet; Political science; Law and economics; Law; Sociology; Marketing; Computer science; Jurisdiction","score_opus":0.017312253187365786,"score_gpt":0.31387204822541853,"score_spread":0.29655979503805274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982541006","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06654746,0.012209449,0.01061631,0.2647592,0.00312757,0.000085019936,0.0004381657,0.00017702853,0.6420398],"genre_scores_gemma":[0.8935605,0.006778518,0.001865051,0.020773435,0.0014863615,0.00006171511,0.0002007675,0.000106558306,0.075167015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99690187,0.0014020401,0.00009454655,0.0002534283,0.0007102755,0.00063779595],"domain_scores_gemma":[0.9908783,0.0040859287,0.0013422527,0.0009907917,0.00066217675,0.002040549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026870028,0.00028469783,0.00031208486,0.000975706,0.0043038363,0.010781475,0.0005514638,0.0028713483,0.028486583],"category_scores_gemma":[0.013895392,0.00011695101,0.000351054,0.0008366937,0.010263215,0.01863752,0.004648879,0.0043352176,0.0039918325],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006320179,0.00022580202,0.013852862,0.00028209548,0.000018164259,0.00049262017,0.019466475,0.00020876403,0.00038875992,0.60265326,0.0858998,0.27644822],"study_design_scores_gemma":[0.000011022381,0.000041625495,0.008174384,0.00093078095,0.000013082114,0.00064252835,0.030363312,0.00033594732,0.00076815917,0.24917516,0.70949584,0.000048154707],"about_ca_topic_score_codex":0.001905819,"about_ca_topic_score_gemma":0.0028907217,"teacher_disagreement_score":0.028486583,"about_ca_system_score_codex":0.001548424,"about_ca_system_score_gemma":0.0023667961,"threshold_uncertainty_score":0.0952971},"labels":[],"label_agreement":null},{"id":"W2983553948","doi":"10.1007/s41701-019-00065-w","title":"The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments","year":2019,"lang":"en","type":"article","venue":"Corpus Pragmatics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Simon Fraser University; Nvidia","keywords":"Computer science; Corpus linguistics; Focus (optics); Meaning (existential); Newspaper; Negation; Linguistics; Constructive; Interpretation (philosophy); Annotation; Natural language processing; Artificial intelligence; Psychology; Process (computing); Sociology; Media studies","score_opus":0.01791365588722305,"score_gpt":0.27367386310372693,"score_spread":0.2557602072165039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983553948","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15328962,0.0038133769,0.012660825,0.002060849,0.0014091664,0.0028463718,0.7849947,0.0013776415,0.037547484],"genre_scores_gemma":[0.16890982,0.0020045,0.037946265,0.000595154,0.00067757186,0.011300784,0.75620127,0.00097651384,0.021388186],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933882,0.002102099,0.0009297793,0.000986039,0.0021727653,0.0004211104],"domain_scores_gemma":[0.97713,0.011069866,0.0020079166,0.0013149652,0.0076015065,0.000875668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033790378,0.0009980648,0.0008003631,0.01088201,0.0034112905,0.0018984565,0.0010759768,0.0019218031,0.01125415],"category_scores_gemma":[0.0225212,0.00041288312,0.0004529581,0.01390172,0.0013890815,0.0017580421,0.0028126729,0.0012907966,0.007974826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000695024,0.00048132168,0.016751388,0.008363558,0.0000801545,0.002996758,0.0438268,0.0007939021,0.012298384,0.008675121,0.68360144,0.2214362],"study_design_scores_gemma":[0.00010457518,0.00008086271,0.054906834,0.0012471633,0.00004469753,0.0007618596,0.012436815,0.0013377499,0.0037136977,0.0015021182,0.9237455,0.00011812561],"about_ca_topic_score_codex":0.023073733,"about_ca_topic_score_gemma":0.040609393,"teacher_disagreement_score":0.023073733,"about_ca_system_score_codex":0.0025027273,"about_ca_system_score_gemma":0.0041159745,"threshold_uncertainty_score":0.045878887},"labels":[],"label_agreement":null},{"id":"W2993816893","doi":"10.21810/jicw.v2i2.1060","title":"Is There a Gap in Canada’s Hate Crime Laws? The Identification Of Soft Violence as a Tool for Current Right-Wing Extremist","year":2019,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hate crime; Popularity; Presidency; Racism; Criminology; Law; Political science; Harmony (color); Terrorism; Right wing; Sociology; Politics","score_opus":0.027806751987925475,"score_gpt":0.27792318436071395,"score_spread":0.25011643237278847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993816893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45682585,0.009966095,0.0039933724,0.31825167,0.0018122385,0.00018993417,0.0033401283,0.00018998834,0.2054307],"genre_scores_gemma":[0.9604032,0.003179026,0.0013997395,0.017496353,0.00010181093,0.0000311475,0.0006951287,0.000067349676,0.01662618],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9934191,0.00052641344,0.0001602605,0.00050848257,0.0029165077,0.0024691615],"domain_scores_gemma":[0.9758932,0.0026496837,0.0016692966,0.0007230516,0.014021869,0.005042825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045823976,0.00025031727,0.0005497694,0.0028121823,0.014240333,0.010354305,0.0020893954,0.00188612,0.01131161],"category_scores_gemma":[0.023985833,0.00038164703,0.00035949133,0.0047186804,0.0043561673,0.003119218,0.0020742756,0.004321675,0.0008444436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026812236,0.0003651214,0.32424888,0.00056266476,0.00012632272,0.0010961076,0.047817573,0.0005198476,0.0014768925,0.09073826,0.20235896,0.33042127],"study_design_scores_gemma":[0.00003514014,0.00009799083,0.40810865,0.0012888934,0.000092047194,0.0004646116,0.15242189,0.0016282538,0.0015785008,0.008073456,0.42592308,0.00028750885],"about_ca_topic_score_codex":0.9889692,"about_ca_topic_score_gemma":0.99597484,"teacher_disagreement_score":0.06535575,"about_ca_system_score_codex":0.06535575,"about_ca_system_score_gemma":0.1540739,"threshold_uncertainty_score":0.47419137},"labels":[],"label_agreement":null},{"id":"W2994635475","doi":"10.5539/ijel.v10n1p157","title":"Tweaking Harassment Through Tweets: A Critical Discourse Study of #MeToo","year":2019,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Harassment; Categorization; Ideology; Criminology; Psychology; Sociology; Social psychology; Politics; Political science; Law; Epistemology","score_opus":0.01666747206860621,"score_gpt":0.32928189441514083,"score_spread":0.31261442234653464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994635475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792311,0.0007043157,0.0014487844,0.006586935,0.00022474304,0.0001577499,0.00021328201,0.000023959994,0.011409137],"genre_scores_gemma":[0.99134505,0.0008661928,0.0009398991,0.0014217895,0.00019523819,0.00019240075,0.00012734985,0.000086659806,0.004825404],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9937779,0.0040764455,0.00023462661,0.00043781006,0.00082611374,0.00064717495],"domain_scores_gemma":[0.96958995,0.024083387,0.0024067906,0.00061872945,0.0022510616,0.0010500035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064325565,0.00074495765,0.00064944965,0.0041197,0.01197656,0.008159871,0.0014220122,0.0026617849,0.0027469795],"category_scores_gemma":[0.0222529,0.00050021306,0.0003199638,0.0034304825,0.010212298,0.008150195,0.0053962613,0.0041135517,0.00071685103],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033111526,0.000049082748,0.0023486833,0.000086083455,0.0000036145454,0.0006535043,0.9898173,0.000013650483,0.00092022744,0.0017119477,0.0008544524,0.0035082947],"study_design_scores_gemma":[0.0000027304645,0.000028356419,0.0025781181,0.00010411782,0.0000036088327,0.00015971196,0.9859249,0.00007572407,0.0004018836,0.0004468568,0.010263044,0.000010991198],"about_ca_topic_score_codex":0.006836191,"about_ca_topic_score_gemma":0.011362282,"teacher_disagreement_score":0.01197656,"about_ca_system_score_codex":0.0048426995,"about_ca_system_score_gemma":0.0020171527,"threshold_uncertainty_score":0.0351364},"labels":[],"label_agreement":null},{"id":"W2997815825","doi":"","title":"I comportamenti devianti e criminali dei digital native:una riflessione comparata in materia dicyberbullismo e sexting","year":2018,"lang":"it","type":"article","venue":"Research Padua  Archive (University of Padua)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"The Internet; Digital native; Relevance (law); Political science; Relation (database); Entertainment; Sociology; Internet privacy; ICTS; Media studies; Criminology; Law; Information and Communications Technology; Computer science; World Wide Web","score_opus":0.07590000220677807,"score_gpt":0.33047044295305833,"score_spread":0.25457044074628027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997815825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9604799,0.0087647345,0.00025550261,0.0022244665,0.00017410461,0.0000392011,0.00012487202,0.000002935737,0.027934168],"genre_scores_gemma":[0.989627,0.006082459,0.00011326008,0.00046871006,0.00010001089,0.000022318722,0.0000634084,0.0000039747506,0.0035188189],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9986431,0.00063042995,0.00007514645,0.00014151678,0.00025219793,0.00025763677],"domain_scores_gemma":[0.9982999,0.0007207482,0.00036926224,0.00013351595,0.00023637427,0.00024018479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013938367,0.00022194946,0.00032428064,0.0020537141,0.0021371595,0.0020942658,0.0005313238,0.0008292932,0.0052908342],"category_scores_gemma":[0.0046178387,0.00013975143,0.00024822727,0.0012458224,0.0039701913,0.0021982098,0.002425205,0.0009692033,0.000367341],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023423565,0.000499951,0.3772439,0.0008496525,0.00007792274,0.0038675228,0.4579467,0.00006439374,0.0004727292,0.04164105,0.0058744475,0.11122759],"study_design_scores_gemma":[0.0000075413755,0.00029232207,0.3204647,0.001075358,0.00007206769,0.004867196,0.61245424,0.00012468756,0.0002966883,0.0026510605,0.057661302,0.000032891003],"about_ca_topic_score_codex":0.010961516,"about_ca_topic_score_gemma":0.023856567,"teacher_disagreement_score":0.010961516,"about_ca_system_score_codex":0.0012234622,"about_ca_system_score_gemma":0.0011657659,"threshold_uncertainty_score":0.021795392},"labels":[],"label_agreement":null},{"id":"W2998472830","doi":"10.48550/arxiv.1912.11278","title":"From Welcome New Gabbers to the Pittsburgh Synagogue Shooting: The Evolution of Gab","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Moderation; Haven; Safe haven; Judaism; Social media; Sociology; History; Computer science; World Wide Web; Mathematics; Economics; Archaeology","score_opus":0.040396786988380465,"score_gpt":0.1771196095719393,"score_spread":0.13672282258355883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998472830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.984706,0.0005775957,0.000774274,0.0012724714,0.000049152695,0.00002717438,0.00064581877,0.000078549296,0.0118689425],"genre_scores_gemma":[0.9953903,0.00022399567,0.0005558924,0.00018246895,0.000021659891,0.000010785066,0.0003269057,0.000052296928,0.0032355755],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9992893,0.00024321998,0.000023927216,0.0001496404,0.00017919231,0.00011466995],"domain_scores_gemma":[0.9937925,0.002341919,0.0012403103,0.00046820764,0.0009950642,0.0011621623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015516195,0.00023275737,0.0002506552,0.0026496425,0.002260832,0.0036252716,0.00041844015,0.00064762286,0.004736686],"category_scores_gemma":[0.010642717,0.00025752521,0.000104484185,0.0018120214,0.0024284534,0.0030627367,0.0025830176,0.0015593887,0.0013109385],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005608362,0.000111589005,0.56796813,0.00026397844,0.00006568675,0.0017956458,0.26589015,0.00035438978,0.0059957607,0.0090102665,0.017365143,0.13061829],"study_design_scores_gemma":[0.000011076407,0.00013302246,0.7613877,0.00019740153,0.000027717477,0.0008878089,0.15364267,0.0012258296,0.0020835993,0.0022301949,0.07811009,0.00006279719],"about_ca_topic_score_codex":0.021077063,"about_ca_topic_score_gemma":0.0661847,"teacher_disagreement_score":0.021077063,"about_ca_system_score_codex":0.0014215154,"about_ca_system_score_gemma":0.00072500226,"threshold_uncertainty_score":0.04190874},"labels":[],"label_agreement":null},{"id":"W3008101578","doi":"","title":"Police-reported hate crime in Canada 2009","year":2011,"lang":"en","type":"article","venue":"IB","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criminology; Hate crime; Political science; Psychology","score_opus":0.025526793780741772,"score_gpt":0.21166334672633577,"score_spread":0.186136552945594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008101578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92565453,0.0019485427,0.0003772654,0.0037406867,0.0002472426,0.00017576673,0.054263797,0.00011838046,0.013473754],"genre_scores_gemma":[0.9730969,0.0014242077,0.00031031691,0.0007724781,0.000045494613,0.000059183345,0.011190901,0.0000428523,0.013057623],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987363,0.0000682495,0.00007873454,0.00012941181,0.0004202114,0.00056715106],"domain_scores_gemma":[0.99458283,0.0001277542,0.00060560904,0.00006679452,0.0031403054,0.0014767328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065091235,0.00056324515,0.0008855454,0.003002948,0.0049813576,0.0022529487,0.0025493598,0.0012558104,0.0043194797],"category_scores_gemma":[0.0026132106,0.0007246362,0.0008844104,0.005196406,0.0009152043,0.00077382836,0.0013727399,0.0027016734,0.00071334885],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028242334,0.0001865309,0.9594138,0.000108176464,0.00019203073,0.00027199398,0.0017566092,0.00039087242,0.00025366896,0.000514596,0.02607495,0.01055429],"study_design_scores_gemma":[0.000017833385,0.00002864702,0.9911753,0.00007711281,0.00004975023,0.000081274564,0.0029683786,0.0005274151,0.00014716895,0.00004139377,0.0048576784,0.000028024111],"about_ca_topic_score_codex":0.99897873,"about_ca_topic_score_gemma":0.99962246,"teacher_disagreement_score":0.063029915,"about_ca_system_score_codex":0.063029915,"about_ca_system_score_gemma":0.082272954,"threshold_uncertainty_score":0.45731616},"labels":[],"label_agreement":null},{"id":"W3011245502","doi":"10.5539/gjhs.v12n5p20","title":"Influence of Hate Speech on Public Perception of Presidential Candidates’ Credibility During the 2015 Presidential Election in Nigeria","year":2020,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Presidential system; Presidential election; Credibility; Politics; Political science; General election; Public opinion; Psychology; Social psychology; Law","score_opus":0.01362534299198034,"score_gpt":0.2909068563512413,"score_spread":0.277281513359261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011245502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99704736,0.00021336671,0.000045774435,0.00025964854,0.000015863441,0.0000059636673,0.000016447617,0.0000012783453,0.0023942932],"genre_scores_gemma":[0.9994918,0.00016001606,0.000026998796,0.000029746552,0.000011009679,0.0000022997394,0.000009782896,5.716822e-7,0.00026777235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991241,0.0004026527,0.000068115296,0.000057065998,0.00018950946,0.00015849716],"domain_scores_gemma":[0.99220943,0.0027745487,0.0036298751,0.00019670809,0.00062423863,0.0005651538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014339697,0.00017536426,0.00018509915,0.0004856361,0.00093900407,0.0012010896,0.0001354465,0.00037327607,0.0019035917],"category_scores_gemma":[0.0072779004,0.00015549794,0.00016260642,0.00028326857,0.000590693,0.0005215316,0.0006062314,0.00072913006,0.00017765381],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037537236,0.00024264482,0.9141515,0.00025106015,0.00005879339,0.001319551,0.05035537,0.00012894206,0.003177882,0.00066721824,0.0009496993,0.028321937],"study_design_scores_gemma":[0.000004044379,0.00016136967,0.94222486,0.00012334554,0.000027240978,0.0002824668,0.05359984,0.00028021887,0.00058916514,0.00010979957,0.0025791957,0.000018446763],"about_ca_topic_score_codex":0.005111607,"about_ca_topic_score_gemma":0.009072597,"teacher_disagreement_score":0.005111607,"about_ca_system_score_codex":0.0006204149,"about_ca_system_score_gemma":0.0005449527,"threshold_uncertainty_score":0.010163724},"labels":[],"label_agreement":null},{"id":"W3016050002","doi":"10.5210/fm.v28i4.13163","title":"Classifying constructive comments","year":2023,"lang":"en","type":"article","venue":"First Monday","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Computer science; Constructive; Annotation; Adaptation (eye); Feature (linguistics); Natural language processing; Artificial intelligence; Domain adaptation; Quality (philosophy); Linguistics; Classifier (UML); Biology; Programming language","score_opus":0.016451617554851458,"score_gpt":0.22910181357195084,"score_spread":0.2126501960170994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016050002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7391042,0.0036069734,0.14614347,0.0027728844,0.0023485732,0.0023237122,0.043844793,0.009741764,0.050113633],"genre_scores_gemma":[0.81373596,0.0010720073,0.10213272,0.000727144,0.00093278545,0.0015940191,0.053037565,0.0009545147,0.02581332],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9947061,0.0015740079,0.0004598346,0.0007817978,0.0020148696,0.00046344334],"domain_scores_gemma":[0.96265393,0.018427692,0.002973078,0.0022328037,0.01240087,0.001311554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031241905,0.0016147908,0.00050418143,0.0069762017,0.0015380217,0.0021468645,0.0012169257,0.0016544028,0.005566474],"category_scores_gemma":[0.03075282,0.00029022724,0.0006243397,0.0029936996,0.00089042605,0.002724406,0.0021204564,0.0015275562,0.0046138074],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017189712,0.00082229124,0.20646755,0.003388272,0.0002069675,0.0033211294,0.013920123,0.010427562,0.038887173,0.013993505,0.17161793,0.53522855],"study_design_scores_gemma":[0.00015868928,0.0006755814,0.18427303,0.0016312992,0.00019306986,0.0029919664,0.023616359,0.20035768,0.052132957,0.0151066,0.5184368,0.0004260233],"about_ca_topic_score_codex":0.00553628,"about_ca_topic_score_gemma":0.010642164,"teacher_disagreement_score":0.0069762017,"about_ca_system_score_codex":0.0012815051,"about_ca_system_score_gemma":0.001423443,"threshold_uncertainty_score":0.018621743},"labels":[],"label_agreement":null},{"id":"W3016263129","doi":"10.1017/9781108348423.005","title":"When Religion is the Source of Hate Speech","year":2018,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Content (measure theory); Computer science; Internet privacy; Speech recognition; World Wide Web; Mathematics","score_opus":0.015269297042101075,"score_gpt":0.1859997653537588,"score_spread":0.17073046831165772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016263129","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04041198,0.028128698,0.011425015,0.028008563,0.0077728955,0.000098391385,0.0017662302,0.00075319636,0.88163507],"genre_scores_gemma":[0.4323175,0.028135322,0.0060517923,0.0067110704,0.0023217138,0.00005654301,0.0023031055,0.00061173324,0.5214912],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993851,0.00015178781,0.000026050184,0.00006351241,0.00027201642,0.000101471254],"domain_scores_gemma":[0.99847776,0.0007143976,0.00021723294,0.00013358788,0.00030065814,0.0001564057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093804096,0.0002815991,0.0002931053,0.0011284911,0.001436449,0.0035712149,0.00042600255,0.0014086463,0.035828967],"category_scores_gemma":[0.0063501587,0.00023662082,0.00025932782,0.0008769187,0.0012173094,0.003498423,0.0011451008,0.0019581683,0.018239243],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014254247,0.00006649595,0.00980553,0.00069470407,0.000038061,0.0014706445,0.017191757,0.00010534375,0.002544653,0.05001147,0.32872236,0.58920646],"study_design_scores_gemma":[0.000008105251,0.000053944415,0.030864881,0.0024960102,0.000038130922,0.004185362,0.01892992,0.0004003962,0.0051321317,0.020165043,0.9176633,0.00006274232],"about_ca_topic_score_codex":0.0029603436,"about_ca_topic_score_gemma":0.006548695,"teacher_disagreement_score":0.035828967,"about_ca_system_score_codex":0.00075266534,"about_ca_system_score_gemma":0.00060577044,"threshold_uncertainty_score":0.119859874},"labels":[],"label_agreement":null},{"id":"W3016785094","doi":"10.3145/epi.2020.mar.16","title":"Retweeting Covid-19 disability issues: Risks, support and outrage","year":2020,"lang":"es","type":"article","venue":"El Profesional de la Informacion","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outrage; Thematic analysis; Value (mathematics); Disease; Quarter (Canadian coin); Social media; Psychology; Pandemic; Coronavirus disease 2019 (COVID-19); Perspective (graphical); Medicine; Public relations; Internet privacy; Political science; Sociology; Qualitative research; Geography; Computer science; Infectious disease (medical specialty); Social science","score_opus":0.04336636728976389,"score_gpt":0.35917289598952246,"score_spread":0.31580652869975856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016785094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97715855,0.0003539369,0.0013457402,0.0024903922,0.000105379375,0.00010252003,0.0017856475,0.000031097043,0.016626662],"genre_scores_gemma":[0.9911699,0.00048028512,0.0011995482,0.00030842723,0.00007552621,0.000087705645,0.0007247833,0.000023433347,0.0059304214],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990246,0.0003574578,0.000084445724,0.00008585706,0.00027614034,0.00017139959],"domain_scores_gemma":[0.99465346,0.0029288065,0.0012094309,0.00018985508,0.00077678356,0.00024162044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012017557,0.00025287605,0.00020554477,0.002463054,0.0019576075,0.0024202978,0.00026318335,0.0006260629,0.0031375268],"category_scores_gemma":[0.0076150135,0.00012045525,0.00021285731,0.0021476909,0.0009275564,0.0026546232,0.001964599,0.0006812893,0.0007279689],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052003196,0.00019327531,0.43516636,0.0013976283,0.00013973651,0.0017205538,0.37633553,0.0006589861,0.009200986,0.009327253,0.018013017,0.14732659],"study_design_scores_gemma":[0.00000889917,0.00014521083,0.39195162,0.00057926064,0.00008499656,0.00080055,0.5117204,0.0027250657,0.0041349856,0.0027145713,0.08504751,0.00008690322],"about_ca_topic_score_codex":0.007708906,"about_ca_topic_score_gemma":0.014387663,"teacher_disagreement_score":0.007708906,"about_ca_system_score_codex":0.0010198453,"about_ca_system_score_gemma":0.00054171745,"threshold_uncertainty_score":0.01532805},"labels":[],"label_agreement":null},{"id":"W3021029234","doi":"10.1089/elj.2019.0566","title":"Disinformation, Digital Information Equality, and Electoral Integrity","year":2020,"lang":"en","type":"article","venue":"Election Law Journal Rules Politics and Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disinformation; Democracy; Political science; Context (archaeology); Politics; Law and economics; Election law; Law; Internet privacy; Sociology; Computer science; Social media","score_opus":0.015071713911813058,"score_gpt":0.2613447676255433,"score_spread":0.24627305371373026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021029234","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10656154,0.0028190673,0.09315816,0.035794716,0.00046771317,0.00015275388,0.00018864531,0.00018847465,0.760669],"genre_scores_gemma":[0.9713309,0.00070222653,0.0073643895,0.0027218272,0.0001532629,0.00006727165,0.00004815847,0.000028000026,0.017583948],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9889109,0.0033920815,0.00061394286,0.001197038,0.0043991255,0.0014868821],"domain_scores_gemma":[0.9805911,0.009383248,0.0027064644,0.0037862374,0.0026241124,0.0009088867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007826838,0.00027434767,0.00036233643,0.002486105,0.005637828,0.010879479,0.0011125566,0.003244967,0.0065901624],"category_scores_gemma":[0.025274597,0.0003271249,0.00040342828,0.001596774,0.028147392,0.007913563,0.0060931537,0.00349554,0.0007626663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028408564,0.000025991834,0.0026311304,0.000049689694,0.000009191879,0.0000859585,0.002877686,0.00036348746,0.0004415779,0.9609469,0.0018458465,0.030694183],"study_design_scores_gemma":[0.000035648034,0.00012081261,0.011669645,0.00044494207,0.00007219254,0.00064587937,0.007586681,0.0023081738,0.0074792826,0.79978186,0.16975766,0.00009727918],"about_ca_topic_score_codex":0.013978459,"about_ca_topic_score_gemma":0.018837497,"teacher_disagreement_score":0.013978459,"about_ca_system_score_codex":0.004393736,"about_ca_system_score_gemma":0.006875353,"threshold_uncertainty_score":0.041392744},"labels":[],"label_agreement":null},{"id":"W3022303172","doi":"10.1007/s12083-019-00832-1","title":"Toward efficient and effective bullying detection in online social network","year":2020,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Popularity; The Internet; Data mining; Harm; Categorization; Social media; Scheme (mathematics); Social network (sociolinguistics); Machine learning; Artificial intelligence; World Wide Web","score_opus":0.019685359894035227,"score_gpt":0.25688717547589324,"score_spread":0.23720181558185802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022303172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22956893,0.0011365035,0.75795823,0.0013884216,0.00014432639,0.0006056189,0.0005446769,0.0041242787,0.0045289667],"genre_scores_gemma":[0.7542835,0.00036527583,0.24129975,0.00019341851,0.00012500118,0.00021207283,0.00052248925,0.00004116339,0.0029572619],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814236,0.000686087,0.00007270397,0.00029197862,0.00063368224,0.0001731527],"domain_scores_gemma":[0.9968136,0.0014580317,0.0003127492,0.00035872502,0.0008754379,0.00018152983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016662389,0.0007178406,0.00088936667,0.0019878366,0.0006618876,0.0015126257,0.0012535648,0.001231905,0.00082460354],"category_scores_gemma":[0.0062385686,0.0003166717,0.00029405864,0.00074544514,0.00033691796,0.0017691077,0.0016243568,0.0010452579,0.0008060079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000319086,0.002415471,0.048462853,0.0002976,0.00017626912,0.00022099909,0.00044293518,0.041960485,0.056147967,0.005660953,0.01071236,0.83318305],"study_design_scores_gemma":[0.000016222973,0.00019494981,0.009168301,0.000016531429,0.000039504754,0.00018639781,0.00037594442,0.96888036,0.014498871,0.0048157102,0.0017863892,0.000020848955],"about_ca_topic_score_codex":0.003963354,"about_ca_topic_score_gemma":0.0056935493,"teacher_disagreement_score":0.003963354,"about_ca_system_score_codex":0.00038239814,"about_ca_system_score_gemma":0.0011769056,"threshold_uncertainty_score":0.00881201},"labels":[],"label_agreement":null},{"id":"W3023173974","doi":"10.1089/elj.2019.0557","title":"Social Media and Democracy: Challenges for Election Law and Administration in Canada","year":2020,"lang":"en","type":"article","venue":"Election Law Journal Rules Politics and Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Nutrition Society","funders":"","keywords":"Politics; Social media; Political science; Transparency (behavior); Legislation; Enforcement; Democracy; Election law; Administration (probate law); Political communication; Context (archaeology); Law; Law and economics; Public relations; Sociology","score_opus":0.02154193923128146,"score_gpt":0.2624126067934447,"score_spread":0.24087066756216324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023173974","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0651472,0.014651268,0.003829564,0.6836726,0.0018052001,0.00029456,0.0009239288,0.00022734943,0.22944836],"genre_scores_gemma":[0.8247329,0.012903974,0.00577255,0.054223236,0.0008219511,0.00019433942,0.0005155224,0.00017505158,0.10066057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9737529,0.0032401874,0.0007151997,0.001957301,0.0097815795,0.010552763],"domain_scores_gemma":[0.9711565,0.006779001,0.0012714125,0.00086953444,0.01094797,0.008975609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011663055,0.00051694736,0.0010778876,0.0040900954,0.04622875,0.028061092,0.0046900646,0.006714814,0.012774879],"category_scores_gemma":[0.031105217,0.0009509528,0.0011493717,0.006409947,0.021788722,0.0064897635,0.0076389886,0.01038042,0.0008270363],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008894293,0.00009824366,0.009151781,0.0002666577,0.00005171856,0.00056533917,0.009221652,0.0019291518,0.00036052286,0.7930109,0.105624385,0.07963074],"study_design_scores_gemma":[0.00013731666,0.0000714729,0.044047482,0.00079762755,0.00010502389,0.00014177489,0.024243552,0.0054037315,0.00085351703,0.11033705,0.8134433,0.00041818293],"about_ca_topic_score_codex":0.9979938,"about_ca_topic_score_gemma":0.99849176,"teacher_disagreement_score":0.35119,"about_ca_system_score_codex":0.35119,"about_ca_system_score_gemma":0.5893898,"threshold_uncertainty_score":0.75252765},"labels":[],"label_agreement":null},{"id":"W3023387214","doi":"","title":"Reddit Mining to Understand Gendered Movements.","year":2020,"lang":"en","type":"article","venue":"EDBT/ICDT Workshops","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Social media; Data science; World Wide Web","score_opus":0.03823002472536464,"score_gpt":0.2431909168171851,"score_spread":0.20496089209182047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023387214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12939781,0.008454625,0.50022155,0.004118294,0.003000758,0.0009794771,0.23876446,0.04781529,0.06724774],"genre_scores_gemma":[0.4034471,0.0021506262,0.3005812,0.0011234239,0.0004540764,0.000968464,0.24293303,0.0021266472,0.046215493],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989741,0.0001417155,0.00009255198,0.00036556768,0.0002561674,0.00016999034],"domain_scores_gemma":[0.9986357,0.0005183104,0.00014623004,0.0003211505,0.00028573387,0.00009286033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001080679,0.0009876615,0.00058327656,0.0037166642,0.00088669406,0.0015850662,0.0016420082,0.0011105224,0.011325371],"category_scores_gemma":[0.004356802,0.00032083679,0.0009242922,0.002929859,0.00031312587,0.0012811518,0.0015025744,0.0011707648,0.011096152],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061745587,0.0004887996,0.03764533,0.0010285823,0.00024078072,0.0011058172,0.0013785353,0.0045390413,0.02143663,0.010215902,0.21969576,0.70160735],"study_design_scores_gemma":[0.000115477655,0.00035838204,0.07122726,0.0006080433,0.00026269897,0.0029326444,0.004586376,0.23840122,0.039417684,0.041689046,0.60027415,0.00012709631],"about_ca_topic_score_codex":0.007250837,"about_ca_topic_score_gemma":0.020755105,"teacher_disagreement_score":0.011325371,"about_ca_system_score_codex":0.00052835495,"about_ca_system_score_gemma":0.0010590107,"threshold_uncertainty_score":0.037887096},"labels":[],"label_agreement":null},{"id":"W3025313450","doi":"10.48550/arxiv.2005.06608","title":"Understanding and Detecting Dangerous Speech in Social Media","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Offensive; Exploit; Social media; Macro; Baseline (sea); Computer science; Internet privacy; Computer security; World Wide Web; Political science; Engineering; Operations research","score_opus":0.21272327388398576,"score_gpt":0.2023706039008456,"score_spread":0.010352669983140161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025313450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.945827,0.0020935095,0.025132902,0.0019457041,0.0007578711,0.0002456952,0.0074031823,0.0022165354,0.014377527],"genre_scores_gemma":[0.9530056,0.0012650491,0.020660195,0.00042672345,0.00057890714,0.0001527234,0.017333295,0.00018229401,0.0063951258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981305,0.0007469041,0.00010899519,0.0003829563,0.00039195502,0.00023871692],"domain_scores_gemma":[0.99469864,0.0028870665,0.00064932636,0.0005482243,0.00088252436,0.00033419262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015700452,0.0018590852,0.00065524434,0.00410033,0.0009929432,0.0019173138,0.00077981065,0.0023571644,0.0017187775],"category_scores_gemma":[0.0065188203,0.0003713304,0.0007732928,0.0010906283,0.0005802468,0.0029716908,0.0015828762,0.0017234259,0.0037349728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014419387,0.0022743251,0.21464568,0.0016917426,0.0004178652,0.002982052,0.0068958253,0.02071729,0.06701734,0.003256351,0.0936366,0.585023],"study_design_scores_gemma":[0.000075897275,0.00088477676,0.2667114,0.0004803995,0.00037835768,0.0039082174,0.0132165775,0.58047587,0.05945801,0.009253733,0.06484864,0.0003081233],"about_ca_topic_score_codex":0.004950038,"about_ca_topic_score_gemma":0.008861185,"teacher_disagreement_score":0.004950038,"about_ca_system_score_codex":0.0005896906,"about_ca_system_score_gemma":0.00054656476,"threshold_uncertainty_score":0.009842455},"labels":[],"label_agreement":null},{"id":"W3030913778","doi":"","title":"Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media","year":2020,"lang":"en","type":"article","venue":"CBS Research Portal (Copenhagen Business School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Stigma (botany); Scheme (mathematics); Annotation; Social media; Computer science; Internet privacy; Psychology; Sociology; Artificial intelligence; World Wide Web; Mathematics; Psychiatry","score_opus":0.06291211689254461,"score_gpt":0.3488565707511636,"score_spread":0.285944453858619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030913778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90521115,0.00066538027,0.05358921,0.0017018471,0.00046624962,0.00078860106,0.008843063,0.0026406655,0.026093869],"genre_scores_gemma":[0.9328295,0.00024249204,0.050453942,0.0001880188,0.00010848091,0.00077788543,0.0076306676,0.0001636837,0.007605317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689424,0.0014929244,0.00025375042,0.00047637188,0.00066707283,0.00021562625],"domain_scores_gemma":[0.99117494,0.004511389,0.0012633296,0.00096812047,0.0017892739,0.00029292464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031804438,0.0005290179,0.00025554016,0.003863479,0.0015557937,0.0013468722,0.00056139525,0.0008988111,0.0026274421],"category_scores_gemma":[0.013251012,0.00018714552,0.00036399046,0.001857318,0.0007510265,0.0018549573,0.0023871218,0.00080629834,0.0013810121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017004145,0.0005037686,0.23300037,0.002828791,0.00011726835,0.0017010429,0.050835345,0.005772561,0.06528789,0.009326269,0.036206998,0.5927194],"study_design_scores_gemma":[0.000063115476,0.00029071467,0.4994504,0.0012564594,0.0001412878,0.001114707,0.035517864,0.25327206,0.073312536,0.016264275,0.119007275,0.00030925733],"about_ca_topic_score_codex":0.0058000595,"about_ca_topic_score_gemma":0.008991668,"teacher_disagreement_score":0.0058000595,"about_ca_system_score_codex":0.00087628094,"about_ca_system_score_gemma":0.00083152624,"threshold_uncertainty_score":0.016820014},"labels":[],"label_agreement":null},{"id":"W3033561598","doi":"10.48550/arxiv.2006.01266","title":"Leveraging Affective Bidirectional Transformers for Offensive Language Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Offensive; Transformer; Computer science; Psychology; Natural language processing; Engineering; Electrical engineering; Voltage; Operations research","score_opus":0.05348892901252859,"score_gpt":0.18981308182980836,"score_spread":0.13632415281727978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033561598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18354356,0.0013871257,0.7731929,0.001385127,0.00084162084,0.00019633384,0.002058618,0.012747757,0.024646962],"genre_scores_gemma":[0.9187579,0.00037062573,0.06405463,0.00046357498,0.00020312739,0.00009421595,0.0021366128,0.00039105615,0.013528235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938464,0.00018792541,0.000025049387,0.00017756959,0.000121614576,0.0001032035],"domain_scores_gemma":[0.99877244,0.00048167136,0.00006964134,0.00019608624,0.00039577397,0.00008430823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009361388,0.0011400443,0.0005410786,0.0008371482,0.0005979229,0.0014822693,0.0009420903,0.0006450093,0.00540387],"category_scores_gemma":[0.0033560663,0.00038263464,0.00065736944,0.00048093332,0.00056353485,0.002131008,0.0018071135,0.0018293781,0.003854915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097268436,0.0006503668,0.0082009025,0.00023645654,0.00015414882,0.0003229553,0.0004344355,0.04730666,0.069526784,0.00947758,0.022531599,0.84018546],"study_design_scores_gemma":[0.000018859839,0.0001117376,0.0022977644,0.000027930726,0.000052148727,0.00012078202,0.00011864938,0.9531057,0.025007846,0.013398325,0.005709037,0.000031231473],"about_ca_topic_score_codex":0.0036881575,"about_ca_topic_score_gemma":0.007819017,"teacher_disagreement_score":0.00540387,"about_ca_system_score_codex":0.00064820924,"about_ca_system_score_gemma":0.00058898254,"threshold_uncertainty_score":0.01807779},"labels":[],"label_agreement":null},{"id":"W3034482811","doi":"10.23977/acss.2020.040104","title":"Online Active Learning for Offensive Language Detection","year":2020,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Offensive; Computer science; Active learning (machine learning); Artificial intelligence; Machine learning; Process (computing); Engineering; Operations research","score_opus":0.01652435245775879,"score_gpt":0.261567602640698,"score_spread":0.2450432501829392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034482811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08088905,0.0028753444,0.9071854,0.0007048082,0.00028328932,0.00009632874,0.00030227425,0.0035634332,0.0041001546],"genre_scores_gemma":[0.89274347,0.00084993715,0.09594743,0.00058770156,0.00033985314,0.00014037271,0.0008512286,0.00018552963,0.008354489],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874425,0.0004330827,0.000064794694,0.00034290765,0.0002857516,0.00012922686],"domain_scores_gemma":[0.99639064,0.0023164307,0.0002989264,0.00031788822,0.0005387895,0.00013732274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001604908,0.0012979561,0.0010048887,0.0016000469,0.00045522457,0.0009521957,0.0017104043,0.0010498321,0.0016158065],"category_scores_gemma":[0.00410355,0.00033419445,0.0005853592,0.0008923515,0.00082826737,0.0019223675,0.0012479192,0.0020551826,0.001067469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039419546,0.00076298957,0.005316467,0.00020375193,0.00015780296,0.00018167721,0.0002459741,0.07284007,0.020096716,0.00398898,0.007167474,0.88864386],"study_design_scores_gemma":[0.000014468437,0.00010259429,0.00081984175,0.00001564993,0.000024586867,0.00007831461,0.000042957003,0.98424524,0.008437775,0.0042020194,0.0019982369,0.00001842058],"about_ca_topic_score_codex":0.001988781,"about_ca_topic_score_gemma":0.002941323,"teacher_disagreement_score":0.001988781,"about_ca_system_score_codex":0.0004986452,"about_ca_system_score_gemma":0.0005550132,"threshold_uncertainty_score":0.008487701},"labels":[],"label_agreement":null},{"id":"W3036206793","doi":"10.48550/arxiv.2006.10145","title":"Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Conversation; Computer science; Sentiment analysis; Focus (optics); Task (project management); Artificial intelligence; Natural language processing; Data science; Psychology; Engineering; Communication","score_opus":0.10729263562834958,"score_gpt":0.22142266786445927,"score_spread":0.1141300322361097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036206793","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9004465,0.001379384,0.07135811,0.0012335691,0.0004435782,0.0005202624,0.0055700666,0.0018713644,0.0171772],"genre_scores_gemma":[0.947893,0.000596977,0.04396314,0.00022332328,0.00038942258,0.00016352975,0.0036376547,0.00010876106,0.0030241888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987525,0.00036823095,0.00010743883,0.00021188358,0.0004124402,0.00014762727],"domain_scores_gemma":[0.99079156,0.004522414,0.0016389372,0.00046098823,0.00213208,0.00045408285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017520149,0.0008295908,0.0005749092,0.0037587585,0.0007246728,0.0010158626,0.00035417642,0.0008918471,0.0022631395],"category_scores_gemma":[0.010752251,0.00023652134,0.00043102255,0.001322564,0.0003022386,0.0016068546,0.0008830597,0.0010357437,0.0019975966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021210187,0.00086211425,0.26298857,0.0013747136,0.00024375285,0.0015875683,0.0042015957,0.0035391578,0.21761109,0.0024336388,0.019121356,0.48391542],"study_design_scores_gemma":[0.000090548114,0.001109139,0.58150434,0.000339736,0.00035898216,0.0020653575,0.004760224,0.27304956,0.098629355,0.00815136,0.029673591,0.00026777448],"about_ca_topic_score_codex":0.0015568021,"about_ca_topic_score_gemma":0.002668506,"teacher_disagreement_score":0.0037587585,"about_ca_system_score_codex":0.00040539555,"about_ca_system_score_gemma":0.0004546924,"threshold_uncertainty_score":0.009265661},"labels":[],"label_agreement":null},{"id":"W3036386626","doi":"10.1017/s0003055420000258","title":"Human Rights are (Increasingly) Plural: Learning the Changing Taxonomy of Human Rights from Large-scale Text Reveals Information Effects","year":2020,"lang":"en","type":"article","venue":"American Political Science Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Center for Research Computing, University of Pittsburgh; York University; University of Pittsburgh; National Science Foundation","keywords":"Human rights; Plural; Political science; Taxonomy (biology); Amnesty; Computer science; Law; Linguistics","score_opus":0.013415210805946591,"score_gpt":0.2619111905815332,"score_spread":0.24849597977558663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036386626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9091016,0.0071082464,0.027518574,0.015541024,0.0002739764,0.00008159818,0.0017111218,0.00017187274,0.03849191],"genre_scores_gemma":[0.9900614,0.0022847094,0.005072805,0.0005318508,0.00020516598,0.000026243619,0.0005267285,0.000041662366,0.0012494664],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99522215,0.002576596,0.0002790631,0.000715842,0.0009918162,0.00021460885],"domain_scores_gemma":[0.9463677,0.035035383,0.010416096,0.0038392162,0.0037979907,0.00054372166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007039336,0.00018253448,0.0003389447,0.0040573427,0.0008013689,0.0030876305,0.0004445632,0.000644389,0.0035322541],"category_scores_gemma":[0.044120137,0.00015379627,0.00025440927,0.0038710786,0.0031447008,0.008733912,0.0015671729,0.0015379493,0.00050256064],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045613645,0.00018548296,0.29062897,0.0014495087,0.0002076556,0.00056826835,0.04637605,0.0018604173,0.0113190785,0.0814959,0.014280233,0.55117226],"study_design_scores_gemma":[0.000026307898,0.00013025696,0.70817876,0.0010348376,0.00014398673,0.00055515836,0.035541758,0.0114218565,0.005094232,0.1323785,0.10539736,0.0000970263],"about_ca_topic_score_codex":0.0027806116,"about_ca_topic_score_gemma":0.004800561,"teacher_disagreement_score":0.007039336,"about_ca_system_score_codex":0.00091091136,"about_ca_system_score_gemma":0.0006520265,"threshold_uncertainty_score":0.037228048},"labels":[],"label_agreement":null},{"id":"W3036997889","doi":"","title":"Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities","year":2019,"lang":"en","type":"article","venue":"Meeting of the Association for Computational Linguistics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Offensive; Harassment; Perspective (graphical); Margin (machine learning); Computer science; Social media; Artificial intelligence; Natural language processing; Psychology; Machine learning; Social psychology; World Wide Web; Mathematics","score_opus":0.02115456455403126,"score_gpt":0.2868773790427908,"score_spread":0.26572281448875956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036997889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6844278,0.0035624763,0.27646178,0.0021447048,0.001198644,0.0002592402,0.0053731077,0.010034158,0.01653807],"genre_scores_gemma":[0.94774604,0.00048988,0.037874665,0.00040576857,0.00011961005,0.0001563551,0.0042848284,0.00011252656,0.008810393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.000040380215,0.000012824321,0.00008381894,0.00002889702,0.000056470188],"domain_scores_gemma":[0.9995844,0.00019028402,0.000039180883,0.000032071344,0.00012787145,0.000026219619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047856953,0.0012188845,0.0004863312,0.0007437851,0.00031058476,0.000676792,0.0008773964,0.0008600107,0.0018865804],"category_scores_gemma":[0.001452094,0.00028455837,0.00053511193,0.00071331224,0.00029794427,0.0010282951,0.00084693485,0.0012762703,0.0011440227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010294454,0.00080217764,0.017726075,0.00050695054,0.00034022974,0.00063400157,0.00073349976,0.15324014,0.06715653,0.0022546912,0.019169718,0.73640656],"study_design_scores_gemma":[0.00001762234,0.00011394839,0.0034336578,0.000043127722,0.00006967279,0.000078102996,0.00013374937,0.9839684,0.008584346,0.0018893621,0.0016438153,0.000024245166],"about_ca_topic_score_codex":0.013070194,"about_ca_topic_score_gemma":0.017734371,"teacher_disagreement_score":0.013070194,"about_ca_system_score_codex":0.00069249154,"about_ca_system_score_gemma":0.00069972384,"threshold_uncertainty_score":0.025988221},"labels":[],"label_agreement":null},{"id":"W3037640539","doi":"10.5204/ijcjsd.v9i4.1514","title":"Planting Hate Speech to Harvest Hatred: How Does Political Hate Speech Fuel Hate Crimes in Turkey?","year":2020,"lang":"en","type":"article","venue":"International Journal for Crime Justice and Social Democracy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; University of Saskatchewan; Ontario Tech University","funders":"","keywords":"Hatred; Politics; Turkish; Rhetoric; Context (archaeology); Political science; Hate crime; Criminology; Sociology; Law; History; Linguistics","score_opus":0.028155259862755824,"score_gpt":0.3037456586424374,"score_spread":0.2755903987796816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037640539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99525106,0.0003533768,0.00012840124,0.0006334268,0.00002449419,0.000004736385,0.000028530516,0.0000020710272,0.0035738582],"genre_scores_gemma":[0.9993011,0.0002050219,0.00004096932,0.00007126604,0.0000066427096,0.0000018402528,0.000016784712,0.0000015445138,0.0003548165],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991454,0.00038025674,0.00003700781,0.000089601235,0.00011348596,0.00023414384],"domain_scores_gemma":[0.9974285,0.0009516474,0.0009910929,0.000067607776,0.00029633398,0.0002648868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011854722,0.00025324497,0.0002222241,0.0007777028,0.0014270222,0.002082064,0.00033579674,0.00069160777,0.0024292227],"category_scores_gemma":[0.0034727578,0.00020390157,0.00017770904,0.0005946814,0.0014452031,0.0015957906,0.0010219496,0.0010585635,0.00033601918],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041330562,0.00048173108,0.72144717,0.00039127312,0.00008605472,0.0032890893,0.18108618,0.0004974489,0.0026647088,0.010080169,0.004690442,0.074872464],"study_design_scores_gemma":[0.000010315313,0.00020246784,0.64313084,0.0002780896,0.000078152305,0.00078530994,0.33947465,0.0014839942,0.0011974763,0.002014728,0.0113001615,0.0000438859],"about_ca_topic_score_codex":0.010658221,"about_ca_topic_score_gemma":0.019334441,"teacher_disagreement_score":0.010658221,"about_ca_system_score_codex":0.0017440476,"about_ca_system_score_gemma":0.0013204481,"threshold_uncertainty_score":0.021192372},"labels":[],"label_agreement":null},{"id":"W3039061493","doi":"10.1080/17512786.2020.1786436","title":"Justice Reframed? A Comparative Critical Discourse Analysis of Twitter Campaigns and Print Media Discourse on Two High-Profile Sexual Assault Verdicts in Ireland and Spain","year":2020,"lang":"en","type":"article","venue":"Journalism Practice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Critical discourse analysis; Mainstream; Sociology; Framing (construction); Frame analysis; Media studies; Discourse analysis; Social media; Gender studies; Criminology; Politics; Law; Content analysis; Social science; Political science; Ideology; History","score_opus":0.05756416236230664,"score_gpt":0.3782051966285157,"score_spread":0.32064103426620905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039061493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96325433,0.0020860517,0.0008669544,0.004649294,0.00021224083,0.00012506265,0.00024550426,0.000020506517,0.028540116],"genre_scores_gemma":[0.993604,0.0010214339,0.00049846363,0.00074369705,0.00009910486,0.00013418686,0.00021740336,0.000072483985,0.0036092354],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99011594,0.005954057,0.00047083272,0.00071461196,0.0011776864,0.0015667849],"domain_scores_gemma":[0.97419614,0.018877,0.0022324792,0.00095872156,0.0028758617,0.0008599746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013964441,0.00063138787,0.00086980703,0.0065760263,0.007903514,0.012161213,0.0016904636,0.002389401,0.0026154236],"category_scores_gemma":[0.023314,0.00041262954,0.0004496908,0.0059655206,0.01500287,0.007915072,0.009179167,0.0031564466,0.0004470667],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004881664,0.000025535206,0.002580699,0.0001332775,0.000004880172,0.0004624609,0.98901105,0.000013113284,0.00030239468,0.0022958356,0.0006875894,0.004434323],"study_design_scores_gemma":[0.00000249863,0.000008408848,0.0035835165,0.00018755846,0.000004083979,0.00004261899,0.98731995,0.000025511292,0.00017797468,0.00022652904,0.008414019,0.000007355138],"about_ca_topic_score_codex":0.025156759,"about_ca_topic_score_gemma":0.03686651,"teacher_disagreement_score":0.025156759,"about_ca_system_score_codex":0.015030829,"about_ca_system_score_gemma":0.00578291,"threshold_uncertainty_score":0.10905683},"labels":[],"label_agreement":null},{"id":"W3040460157","doi":"10.48550/arxiv.2007.00700","title":"Response by the Montreal AI Ethics Institute to the Santa Clara Principles on Transparency and Accountability in Online Content Moderation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Moderation; Transparency (behavior); Accountability; Content (measure theory); Political science; Psychology; Law; Social psychology; Mathematics","score_opus":0.20943218168933062,"score_gpt":0.24779452449989908,"score_spread":0.03836234281056847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040460157","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032437584,0.0006830008,0.00078990584,0.9839857,0.007442013,0.000070596834,0.00016916316,0.000093469615,0.0064416584],"genre_scores_gemma":[0.008406975,0.0006838076,0.0024659333,0.9332938,0.0047696643,0.00031569664,0.00014615321,0.00022460063,0.049693346],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93626994,0.021594584,0.0030971675,0.0049667615,0.025900839,0.008170746],"domain_scores_gemma":[0.8541977,0.060936242,0.0051487223,0.0048725973,0.053690575,0.021154117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051748842,0.001457923,0.0013795241,0.0018938422,0.01470382,0.016529005,0.0062876553,0.034208324,0.028524537],"category_scores_gemma":[0.14390448,0.0017420473,0.0019830188,0.0017962646,0.011761698,0.007674185,0.00855486,0.048380487,0.008920388],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017619679,0.0000098594655,0.0001509425,0.000025343077,0.0000036029103,0.00006210881,0.0006496412,0.000033949964,0.00010352857,0.007832725,0.988214,0.0028966146],"study_design_scores_gemma":[0.000017439575,0.000012545452,0.0006019535,0.00015218547,0.000006248274,0.000050445087,0.00088946795,0.00011095639,0.00018607943,0.0016685621,0.99623966,0.00006442003],"about_ca_topic_score_codex":0.34171242,"about_ca_topic_score_gemma":0.4182294,"teacher_disagreement_score":0.34171242,"about_ca_system_score_codex":0.049334683,"about_ca_system_score_gemma":0.104370035,"threshold_uncertainty_score":0.6794468},"labels":[],"label_agreement":null},{"id":"W3041701425","doi":"10.51506/al-qalam.v25i1.673","title":"Laws of Blasphemy in Non-Muslim Countries An Analytical Study","year":2020,"lang":"id","type":"article","venue":"Al Qalam Jurnal Ilmiah Keagamaan dan Kemasyarakatan","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Blasphemy; Law; Political science; Freedom of religion; Contempt; Legislation; Hatred; Apostasy; Incitement; Human rights; Islam; Philosophy; Theology; Politics","score_opus":0.02315044923339458,"score_gpt":0.27764289560979694,"score_spread":0.25449244637640234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041701425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8205251,0.0013876049,0.00074509525,0.001749652,0.000029999173,0.00008370523,0.00011156978,0.000004400957,0.17536293],"genre_scores_gemma":[0.99560887,0.0006043725,0.00015859018,0.00014504309,0.000009471455,0.000013092228,0.000027237536,0.0000022590348,0.0034309868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987676,0.00059528154,0.000057956906,0.0000775663,0.00019988448,0.00030163847],"domain_scores_gemma":[0.996191,0.0018758527,0.0010727239,0.00017320209,0.0004620119,0.00022522503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011751804,0.0001223042,0.00013007795,0.0021600781,0.0033067476,0.00259354,0.00047424884,0.00057279906,0.0051491847],"category_scores_gemma":[0.0034306308,0.00014377564,0.0002231511,0.002281053,0.003295519,0.0015127755,0.0013593134,0.0010449045,0.00023205474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048504913,0.0003785078,0.1779623,0.0003724813,0.00004854513,0.003711808,0.13018644,0.0007651641,0.00063233986,0.6467511,0.004364891,0.03477798],"study_design_scores_gemma":[0.000020818701,0.00016533824,0.41257575,0.001243059,0.00007187883,0.0020201688,0.3635296,0.002984944,0.0010969592,0.03935894,0.17688069,0.00005191442],"about_ca_topic_score_codex":0.0145443585,"about_ca_topic_score_gemma":0.014350143,"teacher_disagreement_score":0.0145443585,"about_ca_system_score_codex":0.0022223545,"about_ca_system_score_gemma":0.001871596,"threshold_uncertainty_score":0.028919399},"labels":[],"label_agreement":null},{"id":"W3042828647","doi":"10.2307/jj.17610838.15","title":"Online Abuse, Chilling Effects, and Human Rights","year":2020,"lang":"en","type":"book-chapter","venue":"Les Presses de l’Université d’Ottawa | University of Ottawa Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Harassment; Political science; Neglect; Human rights; Law and economics; Criminology; Public relations; Law; Psychology; Sociology","score_opus":0.010992501377212622,"score_gpt":0.18252228707263338,"score_spread":0.17152978569542077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042828647","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14077386,0.017166533,0.0021299848,0.051129926,0.00038194735,0.000058485442,0.00019015213,0.000040016876,0.78812915],"genre_scores_gemma":[0.9528373,0.008403271,0.00032105684,0.004529869,0.0001790882,0.00003121202,0.000034845405,0.000024197712,0.033639103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946576,0.0012012083,0.00012180103,0.0003619251,0.001521225,0.0021362167],"domain_scores_gemma":[0.9941169,0.0025108147,0.00086788106,0.00038908995,0.0010944717,0.0010209067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020864895,0.00031961803,0.0002968427,0.0021139586,0.012020328,0.014007676,0.0009726445,0.0034411147,0.017989427],"category_scores_gemma":[0.007540918,0.000287338,0.00033737117,0.0021213808,0.040872365,0.005237385,0.005666162,0.0034515054,0.0006952838],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022838305,0.00006900295,0.010828907,0.00011672326,0.000015946565,0.0005672026,0.034868672,0.00038598236,0.00020450045,0.90507656,0.013703905,0.034139704],"study_design_scores_gemma":[0.000035095323,0.0000680185,0.079894386,0.0020026315,0.00008710183,0.00095374795,0.14683929,0.0009574862,0.0009669895,0.23969969,0.5283513,0.00014426415],"about_ca_topic_score_codex":0.7755013,"about_ca_topic_score_gemma":0.79712886,"teacher_disagreement_score":0.7755013,"about_ca_system_score_codex":0.0352793,"about_ca_system_score_gemma":0.028379166,"threshold_uncertainty_score":0.45164156},"labels":[],"label_agreement":null},{"id":"W3044335285","doi":"10.48550/arxiv.2007.10712","title":"On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Xenophobia; Pandemic; Coronavirus disease 2019 (COVID-19); Toll; Social media; 2019-20 coronavirus outbreak; Annotation; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Criminology; Psychology; Political science; Computer science; Artificial intelligence; World Wide Web; Racism; Medicine; Law; Virology","score_opus":0.11129424066063884,"score_gpt":0.23108534844103906,"score_spread":0.11979110778040022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044335285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810534,0.00039642537,0.0045343586,0.00051596673,0.00009855619,0.00009751896,0.00789454,0.0002928803,0.0051162764],"genre_scores_gemma":[0.9634396,0.00033379337,0.010939085,0.00024842867,0.00012462339,0.00016161586,0.02159428,0.00008306618,0.0030755235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990453,0.00035095811,0.000082502265,0.0001941765,0.00018043022,0.00014659461],"domain_scores_gemma":[0.9955395,0.002293215,0.00082392694,0.00035052677,0.0007002494,0.00029259018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087852153,0.0005069173,0.00032843582,0.0031139187,0.0012843852,0.0008721934,0.00036219624,0.0008212184,0.0008180778],"category_scores_gemma":[0.0048282733,0.00018444817,0.00026334877,0.0020233218,0.00050433906,0.0014403345,0.0010574773,0.0008135348,0.0009508903],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066448224,0.00052078493,0.798277,0.0007087361,0.00011140241,0.0020081573,0.012742975,0.0040851664,0.035470992,0.0022717437,0.023740314,0.11939826],"study_design_scores_gemma":[0.000015388834,0.00017690349,0.8619156,0.00015114079,0.00006403263,0.0013902513,0.019309023,0.06845553,0.009530204,0.002667492,0.036216818,0.00010763109],"about_ca_topic_score_codex":0.012379364,"about_ca_topic_score_gemma":0.028466934,"teacher_disagreement_score":0.012379364,"about_ca_system_score_codex":0.0006013248,"about_ca_system_score_gemma":0.00053663226,"threshold_uncertainty_score":0.024614632},"labels":[],"label_agreement":null},{"id":"W3045184003","doi":"10.5210/spir.v2018i0.10506","title":"HATRED OF/AND DEMOCRACY: THE POLITICAL CONTRADICTIONS OF REDDIT’S MODERATION STRUCTURE","year":2020,"lang":"en","type":"article","venue":"AoIR Selected Papers of Internet Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Moderation; Hatred; Democracy; Politics; Authoritarianism; Argument (complex analysis); Sociology; Epistemology; Political science; Political economy; Law and economics; Media studies; Social psychology; Law; Psychology; Philosophy","score_opus":0.03031917079480253,"score_gpt":0.2902581181487611,"score_spread":0.2599389473539586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045184003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46767104,0.0014505265,0.16334754,0.04547156,0.0005147384,0.00020177718,0.000133514,0.0004460052,0.32076323],"genre_scores_gemma":[0.9894655,0.000091198504,0.005718048,0.0010262817,0.00007795527,0.000050794188,0.000015612524,0.000076413584,0.0034782377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.975722,0.017229421,0.00055078993,0.0024534278,0.0030207492,0.0010237371],"domain_scores_gemma":[0.9258375,0.049773928,0.0071682422,0.011430483,0.003847741,0.0019420804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026538616,0.00034418463,0.00053245126,0.0023859912,0.0072490317,0.008806059,0.0011880615,0.001873176,0.0048876293],"category_scores_gemma":[0.05659648,0.00048531144,0.00042895545,0.0018770876,0.031217134,0.007611533,0.0064458326,0.0050144177,0.00054248166],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016426311,0.00004832335,0.01409017,0.00012935391,0.0000482286,0.00023543274,0.111786835,0.0006308125,0.0017524074,0.831057,0.0030835324,0.03697362],"study_design_scores_gemma":[0.00013486024,0.00021164113,0.031571563,0.00036397582,0.00009288381,0.0005843911,0.052457698,0.008942412,0.0053628073,0.7382872,0.161834,0.00015653673],"about_ca_topic_score_codex":0.0029363725,"about_ca_topic_score_gemma":0.002594838,"teacher_disagreement_score":0.026538616,"about_ca_system_score_codex":0.0055044843,"about_ca_system_score_gemma":0.001898656,"threshold_uncertainty_score":0.14035124},"labels":[],"label_agreement":null},{"id":"W3046393035","doi":"10.11159/cist20.105","title":"Classification Methods for Hate Speech Diffusion: Detecting theSpread of Hate Speech on Twitter","year":2020,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Voice activity detection; Diffusion; Speech recognition; Speech processing; Physics","score_opus":0.023122703916141737,"score_gpt":0.26347604535188723,"score_spread":0.2403533414357455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046393035","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7478133,0.002073341,0.23396796,0.0025541645,0.00026135132,0.00029164203,0.0023893318,0.003450058,0.007198806],"genre_scores_gemma":[0.9807376,0.0002666498,0.015690796,0.00010362683,0.000100557794,0.000045721055,0.000978395,0.000052212796,0.0020243605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992416,0.00023213922,0.00005102463,0.00023038323,0.00014228493,0.00010264694],"domain_scores_gemma":[0.9922408,0.0049121627,0.0011802252,0.00068186916,0.0007635844,0.00022126467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021640314,0.0011892256,0.00072591414,0.0031938977,0.0006481178,0.0011138709,0.0009545575,0.0011629028,0.0016034043],"category_scores_gemma":[0.008046814,0.00023256706,0.000603464,0.0010671898,0.00055945385,0.0028755413,0.00068885845,0.001519687,0.0015094401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018239622,0.0013753789,0.17432335,0.0005695057,0.00044942996,0.00043251485,0.0009618748,0.1958539,0.024580466,0.0064006066,0.012714785,0.5805143],"study_design_scores_gemma":[0.000010259991,0.000089155015,0.010524493,0.000024777437,0.000028611328,0.00008886157,0.00013238442,0.9811282,0.004453304,0.002692807,0.0008053681,0.000021765853],"about_ca_topic_score_codex":0.005142345,"about_ca_topic_score_gemma":0.005522321,"teacher_disagreement_score":0.005142345,"about_ca_system_score_codex":0.00097472244,"about_ca_system_score_gemma":0.00046192278,"threshold_uncertainty_score":0.011444628},"labels":[],"label_agreement":null},{"id":"W3046519084","doi":"10.3390/make2030011","title":"Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Immigration; Computer science; Word (group theory); Character (mathematics); Task (project management); Voice activity detection; Recall; Precision and recall; Internet privacy; Artificial intelligence; World Wide Web; Speech processing; Political science; Psychology; Linguistics; Law; Cognitive psychology; Engineering","score_opus":0.02308940963608441,"score_gpt":0.2834093222664004,"score_spread":0.260319912630316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046519084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846311,0.00019812756,0.0020363606,0.00044960354,0.00005417782,0.00006917181,0.007943198,0.00043055252,0.0041877055],"genre_scores_gemma":[0.98330057,0.00017507805,0.006074634,0.000103232735,0.0000627257,0.00007768805,0.00803593,0.000023872444,0.0021463044],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944144,0.00018018014,0.0000519301,0.00012363069,0.00011837347,0.00008438368],"domain_scores_gemma":[0.9980627,0.0008125586,0.00040163464,0.00017814492,0.0003781953,0.00016667889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045671168,0.00038362664,0.00027140835,0.0017314102,0.0005141922,0.0005988006,0.00025033063,0.0005881388,0.0007505314],"category_scores_gemma":[0.0025742033,0.00010106284,0.00019207875,0.0010050745,0.0001918075,0.0007667475,0.00045452322,0.00037526488,0.0009909832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000859484,0.00041948975,0.76051337,0.0005471206,0.0001338486,0.0010165883,0.0038593495,0.0037696294,0.036492992,0.00084446446,0.020999987,0.17054372],"study_design_scores_gemma":[0.000025984586,0.0003422024,0.8370907,0.00012626701,0.000109670356,0.0009104105,0.0060616345,0.10094325,0.03041789,0.00082574505,0.023036113,0.00011008984],"about_ca_topic_score_codex":0.010695802,"about_ca_topic_score_gemma":0.0236473,"teacher_disagreement_score":0.010695802,"about_ca_system_score_codex":0.00040379266,"about_ca_system_score_gemma":0.00027381277,"threshold_uncertainty_score":0.021267116},"labels":[],"label_agreement":null},{"id":"W3082035266","doi":"10.48550/arxiv.2009.01046","title":"Generalisation of Cyberbullying Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Software portability; Computer science; Classifier (UML); Variety (cybernetics); Generalization; Machine learning; Artificial intelligence; Data science","score_opus":0.08310204673315018,"score_gpt":0.17868047585065946,"score_spread":0.09557842911750929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082035266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4572254,0.005294715,0.49388528,0.007650363,0.0007522994,0.00065533724,0.011274078,0.0043890965,0.01887353],"genre_scores_gemma":[0.9402011,0.0012941542,0.037883352,0.0010431963,0.0007847724,0.00028470482,0.014775625,0.00033301528,0.003399971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99042374,0.0035051215,0.0005604155,0.003634493,0.0013652239,0.0005109744],"domain_scores_gemma":[0.97255355,0.016051937,0.0011320503,0.0066932305,0.0030661616,0.00050309557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014899531,0.0014861984,0.0020149807,0.0063505764,0.0014989068,0.0037972091,0.0025436396,0.0029623683,0.005006468],"category_scores_gemma":[0.047659695,0.0006734103,0.0028623464,0.0038295956,0.0017710667,0.005340687,0.0031947144,0.003868162,0.0019898792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091765716,0.00082777167,0.33930722,0.0013242003,0.0025801812,0.0011808797,0.0019343066,0.14095335,0.010891412,0.040512748,0.04447358,0.4150967],"study_design_scores_gemma":[0.00009997479,0.00033245765,0.1348252,0.00029546747,0.00039322505,0.0019365868,0.0010391963,0.68603456,0.0036012044,0.15285353,0.018474162,0.000114367045],"about_ca_topic_score_codex":0.009505509,"about_ca_topic_score_gemma":0.0100274775,"teacher_disagreement_score":0.014899531,"about_ca_system_score_codex":0.0019259003,"about_ca_system_score_gemma":0.0009319236,"threshold_uncertainty_score":0.07879722},"labels":[],"label_agreement":null},{"id":"W3082105611","doi":"10.1007/978-3-030-58323-1_27","title":"Authorship Verification with Personalized Language Models","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Reputation; Social media; Task (project management); Word (group theory); Language model; Support vector machine; Class (philosophy); Artificial intelligence; Natural language processing; Machine learning; World Wide Web","score_opus":0.02136597471387049,"score_gpt":0.23353636150388368,"score_spread":0.21217038679001318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082105611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025520701,0.0005431178,0.95699036,0.000889364,0.00036687282,0.00015606181,0.0013107102,0.00911001,0.005112929],"genre_scores_gemma":[0.74135476,0.0003470354,0.23730503,0.00052446604,0.0005302484,0.00019433803,0.003965946,0.00090573035,0.014872389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935196,0.00270608,0.0004226962,0.001551478,0.001236057,0.00056406594],"domain_scores_gemma":[0.98211527,0.008677118,0.0008970596,0.006519688,0.0014770067,0.0003138787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039158054,0.0009937746,0.0016988087,0.0020190899,0.0013212583,0.0030571248,0.0017602738,0.0025374745,0.008575365],"category_scores_gemma":[0.019553924,0.0009656547,0.0017124261,0.001317247,0.0010674853,0.0061311233,0.003316668,0.002316759,0.008388303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010160002,0.0005522628,0.0055692694,0.000337115,0.00033495825,0.0008131246,0.00031853854,0.10389116,0.017685475,0.034800645,0.038329657,0.7963518],"study_design_scores_gemma":[0.00004796332,0.000069522306,0.00057977706,0.000036765145,0.00004980067,0.00028561012,0.00008367544,0.90442824,0.013435268,0.077297784,0.003652119,0.000033499444],"about_ca_topic_score_codex":0.0014419367,"about_ca_topic_score_gemma":0.0027076644,"teacher_disagreement_score":0.008575365,"about_ca_system_score_codex":0.0006451062,"about_ca_system_score_gemma":0.0017088644,"threshold_uncertainty_score":0.028687418},"labels":[],"label_agreement":null},{"id":"W3085627079","doi":"10.1177/1461444820958123","title":"Upvoting extremism: Collective identity formation and the extreme right on Reddit","year":2020,"lang":"en","type":"article","venue":"New Media & Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":203,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Extreme right; Mainstream; Identity (music); Voting; Collective identity; Sociology; Social media; Thematic analysis; Media studies; Public relations; Social identity theory; Internet privacy; Political science; Politics; Social group; Law; Computer science; Qualitative research; Social science; Aesthetics","score_opus":0.03347209508835352,"score_gpt":0.22205242539243497,"score_spread":0.18858033030408144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085627079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9171499,0.00020533493,0.0035725224,0.0019113657,0.00011872173,0.000027103863,0.000024229383,0.000026718515,0.0769641],"genre_scores_gemma":[0.99629396,0.000052477462,0.000488069,0.00018621083,0.000028813116,0.000008157388,0.000012852296,0.0000128465,0.0029164925],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9963456,0.002273012,0.00008319999,0.00030128285,0.00064045936,0.0003565206],"domain_scores_gemma":[0.9935475,0.0030631926,0.0014994,0.0007656977,0.00050138135,0.0006227904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032289664,0.00022746196,0.0002176046,0.0009483375,0.0051399353,0.0046495744,0.00053951796,0.0008600693,0.0031391436],"category_scores_gemma":[0.010084877,0.00016574869,0.00016040643,0.0005644796,0.0068495814,0.0030956313,0.0042285286,0.0017757566,0.00038766675],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002947597,0.00015256513,0.05830947,0.0001606361,0.000038263624,0.0011476208,0.7752492,0.00019333715,0.006767039,0.08298863,0.004290127,0.07040843],"study_design_scores_gemma":[0.000022864893,0.00017080267,0.09154504,0.00025633074,0.0000377101,0.0010144155,0.7628141,0.0014479579,0.0048160846,0.015504836,0.12228301,0.000086880784],"about_ca_topic_score_codex":0.0019874168,"about_ca_topic_score_gemma":0.0031896406,"teacher_disagreement_score":0.0051399353,"about_ca_system_score_codex":0.0011380038,"about_ca_system_score_gemma":0.0006404491,"threshold_uncertainty_score":0.017076612},"labels":[],"label_agreement":null},{"id":"W3086731473","doi":"10.2196/19833","title":"The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sadness; Disgust; Racism; Anger; Sentiment analysis; Pandemic; China; Social media; Coronavirus disease 2019 (COVID-19); Psychology; Hatred; Social psychology; Criminology; Advertising; Sociology; Political science; Medicine; Computer science; Gender studies; Disease; Politics; Law; Artificial intelligence; Business","score_opus":0.041048980659837225,"score_gpt":0.3109415700293051,"score_spread":0.2698925893694679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086731473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930814,0.00032420518,0.0003885666,0.0006783625,0.0001132606,0.00006685027,0.0015167972,0.000024338868,0.003806147],"genre_scores_gemma":[0.9956012,0.00036822434,0.0007207891,0.00021434322,0.00011480605,0.000092082075,0.0013277364,0.00001509305,0.0015457805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995259,0.00013728952,0.000038664577,0.000062906445,0.00013894944,0.00009633332],"domain_scores_gemma":[0.9983333,0.0006520985,0.00052016356,0.00004786844,0.0002865849,0.00016003847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005513668,0.0002547153,0.00023449483,0.0010790835,0.00087317504,0.0013064427,0.00014208148,0.00040112392,0.0009641456],"category_scores_gemma":[0.002465267,0.0001013599,0.00030228237,0.0009748511,0.00044821732,0.0010442208,0.0007491419,0.0006399909,0.00041907525],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015073944,0.00032874502,0.75748503,0.0015831004,0.00023882157,0.0047684032,0.07832047,0.0014941974,0.027027845,0.001994237,0.028547943,0.09670384],"study_design_scores_gemma":[0.00001083816,0.00024508787,0.89571285,0.00021544093,0.00008338409,0.00073126476,0.07091449,0.00525572,0.0035736933,0.0006469857,0.022534907,0.0000753186],"about_ca_topic_score_codex":0.0029910693,"about_ca_topic_score_gemma":0.0046640113,"teacher_disagreement_score":0.0029910693,"about_ca_system_score_codex":0.00053774565,"about_ca_system_score_gemma":0.00029548455,"threshold_uncertainty_score":0.0059473515},"labels":[],"label_agreement":null},{"id":"W3087096149","doi":"10.1016/j.osnem.2020.100096","title":"Hate and offensive speech detection on Arabic social media","year":2020,"lang":"en","type":"article","venue":"Online Social Networks and Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Offensive; Computer science; Annotation; Natural language processing; Artificial intelligence; Social media; Class (philosophy); Crawling; Variety (cybernetics); Arabic; Speech recognition; Machine learning; Linguistics; World Wide Web","score_opus":0.02048245754773978,"score_gpt":0.22785761133351823,"score_spread":0.20737515378577845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087096149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9821951,0.00048339585,0.008226806,0.000146626,0.000109824294,0.00006560924,0.0011509866,0.00042634952,0.007195321],"genre_scores_gemma":[0.9909689,0.00018291916,0.0047966363,0.00004049492,0.00007326419,0.00003300683,0.0006524227,0.000015009064,0.0032373406],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999556,0.000101227226,0.000024611076,0.00006776038,0.00017048774,0.000079773505],"domain_scores_gemma":[0.9985997,0.0005248755,0.00021848726,0.00010892108,0.00039531384,0.00015267772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039705524,0.00049636065,0.00022909482,0.0025400913,0.0005356616,0.0005763628,0.0001735347,0.00053870113,0.0023614562],"category_scores_gemma":[0.0019534049,0.000086769134,0.0001998598,0.0006872559,0.00024726233,0.00043286209,0.0005815726,0.0003568512,0.0015699601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023046604,0.0006949639,0.2899293,0.00059223414,0.00019442728,0.0032463565,0.0025622556,0.0033674098,0.1992951,0.0014623866,0.010786305,0.48556465],"study_design_scores_gemma":[0.00003150744,0.0008434921,0.6693813,0.00011193659,0.00020648124,0.0050402754,0.0055344426,0.19280906,0.10959678,0.0010416001,0.015264525,0.00013858781],"about_ca_topic_score_codex":0.0021242353,"about_ca_topic_score_gemma":0.0033217536,"teacher_disagreement_score":0.0025400913,"about_ca_system_score_codex":0.00021536462,"about_ca_system_score_gemma":0.00020829559,"threshold_uncertainty_score":0.007899821},"labels":[],"label_agreement":null},{"id":"W3088002500","doi":"10.3167/ghs.2020.130210","title":"Chalk Back","year":2020,"lang":"en","type":"article","venue":"Girlhood Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Popularity; Empowerment; Harassment; Power (physics); Work (physics); Sociology; Media studies; History; Gender studies; Political science; Engineering; Law","score_opus":0.07593645691182632,"score_gpt":0.27295016710321035,"score_spread":0.19701371019138403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088002500","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048044555,0.0009035769,0.0013498876,0.008619468,0.0025133814,0.0001988036,0.0014351791,0.0006460462,0.9362892],"genre_scores_gemma":[0.11297295,0.00059270934,0.00068641757,0.0038228673,0.00029827896,0.00009812342,0.0008123712,0.0004904737,0.88022566],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981261,0.00029568165,0.000044986744,0.00026231012,0.0006639121,0.0006070202],"domain_scores_gemma":[0.9962081,0.0005463258,0.00021993007,0.00049782667,0.0011672832,0.0013605196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001180368,0.0006698431,0.0003587144,0.0016969896,0.009883018,0.007833365,0.00085835456,0.0019919304,0.16300416],"category_scores_gemma":[0.0055324617,0.00037266343,0.00031287165,0.0012962426,0.0032037955,0.003636635,0.0048963367,0.0025938596,0.05084749],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020513278,0.00009873345,0.009819811,0.0004017059,0.00001603427,0.0014314573,0.070935644,0.0000764054,0.0024070165,0.041139953,0.71742845,0.15603971],"study_design_scores_gemma":[0.0000033465162,0.00002508851,0.0038324757,0.00013091356,0.0000037723946,0.00025332358,0.022155056,0.00002837846,0.00036503462,0.00077271386,0.97241175,0.000018271874],"about_ca_topic_score_codex":0.037131976,"about_ca_topic_score_gemma":0.09680825,"teacher_disagreement_score":0.16300416,"about_ca_system_score_codex":0.0032349513,"about_ca_system_score_gemma":0.004141291,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3091943591","doi":"10.1007/978-3-030-59830-3_60","title":"Abusive Language Detection Using BERT Pre-trained Embedding","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Language model; Embedding; Recall; Artificial intelligence; Natural language processing; Word embedding; Meaning (existential); Linguistics","score_opus":0.012534629575203607,"score_gpt":0.25467544026353983,"score_spread":0.24214081068833623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091943591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1839839,0.0027887023,0.77535,0.00058893184,0.0017437355,0.00037022622,0.0022653393,0.014279987,0.018629158],"genre_scores_gemma":[0.6264708,0.0015976321,0.31414393,0.0003564221,0.00038844455,0.0002460078,0.006445793,0.0008624054,0.04948863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948275,0.00006309527,0.00003009698,0.00017170157,0.00015374577,0.00009862135],"domain_scores_gemma":[0.99938977,0.00015324476,0.000037383088,0.00013691481,0.00024519104,0.000037511974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004685855,0.0016827896,0.0009729758,0.0011671598,0.0004087815,0.0010386192,0.0007680356,0.0011221552,0.00705602],"category_scores_gemma":[0.001262345,0.000361873,0.000851773,0.0006325111,0.00037345386,0.0014788046,0.0013706244,0.0014421159,0.01094722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033307835,0.0002446076,0.0022001555,0.00020026819,0.00009003487,0.00038069027,0.000078898236,0.009946131,0.106610216,0.0012398278,0.008710481,0.86996573],"study_design_scores_gemma":[0.000034978275,0.0006105701,0.009043624,0.00012713272,0.00020774255,0.0018685564,0.00023603032,0.75716615,0.20824933,0.0040324344,0.018330647,0.00009287145],"about_ca_topic_score_codex":0.0017243548,"about_ca_topic_score_gemma":0.0033011825,"teacher_disagreement_score":0.00705602,"about_ca_system_score_codex":0.00026008103,"about_ca_system_score_gemma":0.00055774796,"threshold_uncertainty_score":0.02360475},"labels":[],"label_agreement":null},{"id":"W3092013050","doi":"10.1093/ojlr/rwaa024","title":"Masterpiece Cakeshop and the Foundations of Free Speech and Toleration","year":2020,"lang":"en","type":"article","venue":"Oxford Journal of Law and Religion","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Civility; Toleration; Argument (complex analysis); Law; Sociology; Context (archaeology); Politics; Dialogical self; Free speech; Political science; Philosophy; Epistemology; History","score_opus":0.012967765610551544,"score_gpt":0.2238879050854606,"score_spread":0.21092013947490906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092013050","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08074797,0.004832888,0.028739443,0.16075012,0.00065693585,0.00006921802,0.000071695904,0.00008903642,0.7240427],"genre_scores_gemma":[0.9893945,0.00032179692,0.0010748416,0.0026645418,0.00020058129,0.00003352085,0.000011507682,0.000017557195,0.006281085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9819324,0.0108173145,0.00046777015,0.0019878345,0.002849718,0.0019450046],"domain_scores_gemma":[0.95135826,0.035713017,0.0038267327,0.0037955723,0.0029778732,0.0023286068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014530246,0.00033205934,0.0005569073,0.0024673056,0.008625515,0.014973037,0.001578289,0.0074563543,0.011332021],"category_scores_gemma":[0.034978334,0.00039898805,0.00041311307,0.0011130653,0.093410924,0.011720947,0.008101636,0.009083299,0.0007890941],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006843517,0.00001049516,0.00032264242,0.00001140093,0.0000019540998,0.00005376738,0.0042735636,0.0000768958,0.00002213788,0.99119407,0.0014814169,0.0025447689],"study_design_scores_gemma":[0.000017783033,0.0000226643,0.0012767017,0.00024820075,0.0000064493347,0.0001332619,0.008428514,0.00064003456,0.00022772487,0.9487666,0.04020255,0.000029569996],"about_ca_topic_score_codex":0.0076844185,"about_ca_topic_score_gemma":0.0057182563,"teacher_disagreement_score":0.014973037,"about_ca_system_score_codex":0.006350462,"about_ca_system_score_gemma":0.00631478,"threshold_uncertainty_score":0.076844156},"labels":[],"label_agreement":null},{"id":"W3092586868","doi":"10.5210/spir.v2020i0.11195","title":"DISPLACED DISCUSSION: THE IMPLICATIONS OF REDDIT QUARANTINE AND THEMOVEMENT OF THEREDPILL TO SELF-HOSTING","year":2020,"lang":"en","type":"article","venue":"AoIR Selected Papers of Internet Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Action (physics); Identity (music); Sociology; Ethnography; Social media; Media studies; Ideology; Online community; Limiting; Public relations; Political science; Internet privacy; Politics; Law; Computer science; Aesthetics; Engineering","score_opus":0.02861989384716544,"score_gpt":0.3007621575162747,"score_spread":0.27214226366910926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092586868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6457945,0.001915632,0.00405825,0.14092869,0.0014753622,0.00028232517,0.0002848854,0.00010294659,0.20515738],"genre_scores_gemma":[0.95536685,0.00057448534,0.0010310377,0.014840119,0.00021171494,0.00018335089,0.000106141575,0.00016446131,0.02752187],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.96226513,0.027289947,0.00056494935,0.0025961674,0.0038435007,0.0034402322],"domain_scores_gemma":[0.95022124,0.03601875,0.0034083435,0.0023109836,0.0036861731,0.004354497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02322829,0.00063156843,0.0005468331,0.002693677,0.050591256,0.019302636,0.0051457123,0.0074709496,0.018026153],"category_scores_gemma":[0.040687136,0.00068809016,0.0005678155,0.003100671,0.04877397,0.019979708,0.02003397,0.010841436,0.00198489],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026525475,0.00004309897,0.001496393,0.000056799025,0.0000029323737,0.0007321475,0.9628452,0.000022869704,0.00018119579,0.027189804,0.0039998414,0.0034031936],"study_design_scores_gemma":[0.00000636666,0.000013942674,0.0006995423,0.00010290154,0.0000022285142,0.000117528114,0.9311883,0.000039636114,0.00017532746,0.0022424357,0.06539875,0.000013057522],"about_ca_topic_score_codex":0.04435617,"about_ca_topic_score_gemma":0.06438617,"teacher_disagreement_score":0.050591256,"about_ca_system_score_codex":0.022651322,"about_ca_system_score_gemma":0.013921042,"threshold_uncertainty_score":0.16434759},"labels":[],"label_agreement":null},{"id":"W3092696693","doi":"10.18653/v1/2020.alw-1.20","title":"On Cross-Dataset Generalization in Automatic Detection of Online Abuse","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Generalization; Computer science; Ambiguity; Task (project management); Class (philosophy); Set (abstract data type); Artificial intelligence; Test set; Machine learning; Training set; Natural language processing; Test (biology); Domain (mathematical analysis); Data set; Data mining; Information retrieval; Mathematics","score_opus":0.022619505070127832,"score_gpt":0.28951822015698286,"score_spread":0.26689871508685503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092696693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6337358,0.003511658,0.34299183,0.0016630744,0.00048958405,0.00080768904,0.003362477,0.007611699,0.0058262325],"genre_scores_gemma":[0.8862119,0.00035853463,0.10049702,0.00069815817,0.00021241035,0.00047090076,0.009433554,0.00054387446,0.0015736299],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9527655,0.028212013,0.0025361103,0.011598179,0.003944731,0.0009434923],"domain_scores_gemma":[0.87372106,0.07272739,0.008604303,0.03820324,0.005705388,0.0010385836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046344306,0.0022557795,0.0017367079,0.0048403675,0.0025176972,0.0032806105,0.0025959318,0.0027694467,0.0010860437],"category_scores_gemma":[0.09129391,0.000772381,0.0019339814,0.003686716,0.0029112496,0.005179777,0.006593647,0.003322881,0.0012810475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023000445,0.0013953282,0.2141366,0.0009961093,0.0023535716,0.0013687456,0.0053086705,0.0797985,0.027134055,0.007339108,0.03243756,0.6254317],"study_design_scores_gemma":[0.00018224877,0.000940663,0.17626025,0.0003742816,0.0006774715,0.00255819,0.0037638834,0.71383744,0.040234905,0.037530784,0.023322986,0.00031686356],"about_ca_topic_score_codex":0.0042295265,"about_ca_topic_score_gemma":0.0069871373,"teacher_disagreement_score":0.046344306,"about_ca_system_score_codex":0.0015029758,"about_ca_system_score_gemma":0.0010250581,"threshold_uncertainty_score":0.24509507},"labels":[],"label_agreement":null},{"id":"W3092739266","doi":"10.1007/s00530-020-00701-5","title":"Cyberbullying detection solutions based on deep learning architectures","year":2020,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Recurrent neural network; Machine learning; Lexical analysis; Identification (biology); Artificial neural network","score_opus":0.020943877762893342,"score_gpt":0.2159304773142739,"score_spread":0.19498659955138056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092739266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1742663,0.0029575666,0.800215,0.0021971895,0.00053262635,0.00020123905,0.00094443816,0.005975312,0.012710435],"genre_scores_gemma":[0.89372736,0.00081118284,0.09560021,0.0004771546,0.00012712715,0.00009110546,0.0009972563,0.000071782255,0.008096918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998097,0.000026907786,0.00000789294,0.00005292829,0.000047140267,0.00005541634],"domain_scores_gemma":[0.9996927,0.00006983625,0.000038910453,0.00003357254,0.00013430924,0.000030614767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033872633,0.0010426706,0.00046701208,0.00081712264,0.00033635838,0.000614778,0.00086211314,0.00080612046,0.0027314138],"category_scores_gemma":[0.0010339253,0.00026437518,0.0004467453,0.00041420475,0.0001877448,0.00080986466,0.00078146136,0.0011674995,0.0008436024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034744604,0.00082151697,0.010018497,0.0001836156,0.0002577087,0.00013799152,0.000068248504,0.11458521,0.020812888,0.0038338643,0.014635309,0.8342978],"study_design_scores_gemma":[0.000014326505,0.00009766795,0.002130351,0.000025994452,0.00005101192,0.0000410222,0.000030731066,0.9851495,0.007256589,0.003842999,0.0013477209,0.000012156765],"about_ca_topic_score_codex":0.006542776,"about_ca_topic_score_gemma":0.012618899,"teacher_disagreement_score":0.006542776,"about_ca_system_score_codex":0.00054674165,"about_ca_system_score_gemma":0.00084534293,"threshold_uncertainty_score":0.013009369},"labels":[],"label_agreement":null},{"id":"W3094967092","doi":"10.1109/ms.2020.3036758","title":"Insights Into Nonmerged Pull Requests in GitHub: Is There Evidence of Bias Based on Perceptible Race?","year":2020,"lang":"en","type":"article","venue":"IEEE Software","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Race (biology); Computer science; Racial bias; Software; Data science; World Wide Web; Internet privacy; Programming language; Sociology; Gender studies","score_opus":0.04746115487556264,"score_gpt":0.26957009093035184,"score_spread":0.2221089360547892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094967092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9830503,0.00018130061,0.0012556273,0.0035206056,0.00006913614,0.00007662912,0.0001046612,0.00003736479,0.011704387],"genre_scores_gemma":[0.9953099,0.00011357799,0.00042198822,0.0011119696,0.000031162035,0.00009935609,0.000037278936,0.000068388304,0.0028063706],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.98050505,0.013608815,0.0005163979,0.00089319027,0.0025493177,0.0019272288],"domain_scores_gemma":[0.9248021,0.05393996,0.009925694,0.0020165695,0.005558255,0.0037574675],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013491769,0.0006390056,0.00064163655,0.0027983098,0.0078494325,0.0054448005,0.0012461287,0.0017179773,0.00741739],"category_scores_gemma":[0.06233641,0.0005226932,0.0001776511,0.0020608972,0.011474096,0.005230546,0.006810353,0.0028861675,0.0009601416],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043663968,0.000021957801,0.008030145,0.000100195575,0.0000032273213,0.00042021417,0.9863092,0.000011302724,0.0007240749,0.001317171,0.00068766746,0.002331255],"study_design_scores_gemma":[0.0000030030958,0.000019287118,0.0045133973,0.00007603008,0.000001946073,0.000118107135,0.9902699,0.000056715297,0.00019635315,0.000413912,0.0043167877,0.000014594768],"about_ca_topic_score_codex":0.014842361,"about_ca_topic_score_gemma":0.026321286,"teacher_disagreement_score":0.98650825,"about_ca_system_score_codex":0.0051145228,"about_ca_system_score_gemma":0.0042217453,"threshold_uncertainty_score":0.071352124},"labels":[],"label_agreement":null},{"id":"W3094997061","doi":"10.4224/40002027","title":"Towards Ethics by Design in Online Abusive Content Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Inclusion (mineral); Task (project management); Field (mathematics); Computer science; Process (computing); Identity (music); Annotation; Content analysis; Internet privacy; Data science; Computer security; Psychology; Artificial intelligence; Social psychology; Sociology; Engineering","score_opus":0.18482772586871518,"score_gpt":0.21959468750131897,"score_spread":0.03476696163260379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094997061","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017967554,0.00044290768,0.9516755,0.019734832,0.0002074877,0.00032446106,0.00012172867,0.00033396116,0.009191564],"genre_scores_gemma":[0.41541892,0.00047368513,0.57448536,0.003969992,0.0003455838,0.0013911715,0.00039521995,0.00052086957,0.002999213],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8392784,0.13292362,0.00438552,0.00871079,0.012945219,0.001756455],"domain_scores_gemma":[0.71314126,0.19049998,0.018698327,0.05326546,0.020301508,0.004093417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13100716,0.0011822563,0.0014456882,0.0036091134,0.0050728,0.018195288,0.0036229177,0.006506476,0.0025114564],"category_scores_gemma":[0.23144849,0.0017996604,0.0016198925,0.0024650896,0.03265242,0.022443406,0.012889095,0.009796843,0.0015645444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001246063,0.00022632681,0.013911217,0.00039375213,0.00008085454,0.00020953218,0.014185706,0.0075469045,0.0014496297,0.86934507,0.0065182713,0.08600809],"study_design_scores_gemma":[0.000028734556,0.000058498037,0.0010590211,0.00020568327,0.000025775478,0.00025998545,0.001541002,0.034952573,0.0015752343,0.93910754,0.021142257,0.000043701875],"about_ca_topic_score_codex":0.0014224348,"about_ca_topic_score_gemma":0.0014690469,"teacher_disagreement_score":0.13100716,"about_ca_system_score_codex":0.005678404,"about_ca_system_score_gemma":0.008739971,"threshold_uncertainty_score":0.6928404},"labels":[],"label_agreement":null},{"id":"W3099161326","doi":"10.18653/v1/2020.alw-1.17","title":"Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Taxonomy (biology); Pejorative; Natural language processing; Annotation; Artificial intelligence; Information retrieval; Linguistics","score_opus":0.04135385596207682,"score_gpt":0.24917963222612652,"score_spread":0.2078257762640497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099161326","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33254728,0.0050659482,0.25058803,0.0065542837,0.0020550292,0.0058130287,0.33513972,0.02108639,0.041150287],"genre_scores_gemma":[0.19312,0.0019803052,0.32941097,0.0013101045,0.00051009463,0.010188158,0.4476515,0.0031322266,0.012696625],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.990902,0.0031123976,0.001572052,0.001709419,0.0023310368,0.00037309038],"domain_scores_gemma":[0.9446985,0.023775447,0.005061368,0.0068107434,0.017986374,0.0016676531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058991755,0.0013458867,0.00089307583,0.014580125,0.0030224032,0.0027616052,0.001985988,0.0022634582,0.006969117],"category_scores_gemma":[0.033798106,0.0008184634,0.0006782903,0.008718437,0.0019026407,0.0051393355,0.005369043,0.0029872668,0.010020389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006362071,0.0010181319,0.06067606,0.010415672,0.0000980457,0.0027319659,0.042693302,0.0044619176,0.056982797,0.013400956,0.4300393,0.3768455],"study_design_scores_gemma":[0.00014617527,0.00039689467,0.16302398,0.00394408,0.000118995595,0.0037105444,0.032982886,0.032770745,0.021561677,0.009810912,0.7310114,0.0005217035],"about_ca_topic_score_codex":0.010515121,"about_ca_topic_score_gemma":0.023135148,"teacher_disagreement_score":0.014580125,"about_ca_system_score_codex":0.0021985685,"about_ca_system_score_gemma":0.004512649,"threshold_uncertainty_score":0.031198204},"labels":[],"label_agreement":null},{"id":"W3107025682","doi":"10.1145/3428091","title":"Online Xenophobic Behavior Amid the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"Digital Government Research and Practice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Xenophobia; Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Moderation; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Deterrence (psychology); Online and offline; Political science; Criminology; Psychology; Racism; Social psychology; Law; Infectious disease (medical specialty); Virology; Medicine","score_opus":0.18677726100711337,"score_gpt":0.3921599595648633,"score_spread":0.20538269855774996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107025682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6002867,0.004531575,0.004125969,0.21305028,0.0023655018,0.00010065446,0.00032143178,0.00011551928,0.17510243],"genre_scores_gemma":[0.9702856,0.002202754,0.00066088507,0.02059647,0.0007636239,0.000024712055,0.00006643467,0.000026036867,0.005373432],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99647164,0.0022037968,0.00009031951,0.0002475027,0.0004966465,0.0004901134],"domain_scores_gemma":[0.98983616,0.0059452443,0.0015151295,0.00058230286,0.0010180884,0.0011031267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033627844,0.00018017982,0.00026386502,0.00066389533,0.003922337,0.0029734473,0.00041198236,0.0031056423,0.0044188043],"category_scores_gemma":[0.016631301,0.00012387156,0.0002288178,0.00044215887,0.0035945263,0.0037546037,0.0021457023,0.0034907148,0.0006052609],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049323105,0.00026802727,0.14931796,0.00096936914,0.00009131195,0.009851387,0.19191952,0.0005120811,0.005220493,0.16783278,0.14833003,0.32519373],"study_design_scores_gemma":[0.000029991184,0.00043723496,0.17017834,0.00284913,0.00009585973,0.005475013,0.2635016,0.0019006919,0.0044843517,0.03478068,0.5160619,0.00020517921],"about_ca_topic_score_codex":0.0051025944,"about_ca_topic_score_gemma":0.010986969,"teacher_disagreement_score":0.0051025944,"about_ca_system_score_codex":0.0010715032,"about_ca_system_score_gemma":0.0009613453,"threshold_uncertainty_score":0.017784357},"labels":[],"label_agreement":null},{"id":"W3109954471","doi":"10.22215/etd/2019-13558","title":"\"I’m NOT A MONSTER”: A Corpus-Driven Genre Analysis of School Shooter Notes.","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Monster; Scope (computer science); Wright; Typology; Linguistics; Salient; Psychology; Multidisciplinary approach; Corpus linguistics; Sociology; Pedagogy; History; Computer science; Literature; Social science; Art; Artificial intelligence","score_opus":0.017084549313217668,"score_gpt":0.26016440195491214,"score_spread":0.24307985264169446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109954471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8909425,0.006119977,0.020647489,0.004446762,0.0008056454,0.00051483844,0.04153854,0.00068129046,0.03430309],"genre_scores_gemma":[0.8797453,0.0031594473,0.039382055,0.0007560801,0.0004076506,0.00085843983,0.05539218,0.00063290866,0.019665934],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99883527,0.0004867295,0.000119604425,0.00021658515,0.00025109106,0.00009075003],"domain_scores_gemma":[0.99344105,0.0038711105,0.00065838476,0.00056209794,0.0011760069,0.00029145935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018520164,0.00038012632,0.00028168122,0.005369687,0.0013813917,0.0026258267,0.00071123574,0.00081409974,0.003221838],"category_scores_gemma":[0.009557576,0.00020693523,0.00028331284,0.004240655,0.0009991227,0.0022506476,0.0021691334,0.0012368696,0.0025603776],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009872653,0.0006916889,0.12822698,0.002645473,0.00021047473,0.0023430772,0.17840366,0.0007325909,0.029263968,0.014739401,0.16360343,0.47815195],"study_design_scores_gemma":[0.00007346167,0.00013793664,0.56450224,0.0016155761,0.00018140892,0.0019883574,0.14348073,0.009061424,0.0060348166,0.008455267,0.2643334,0.0001354196],"about_ca_topic_score_codex":0.008822932,"about_ca_topic_score_gemma":0.024932073,"teacher_disagreement_score":0.008822932,"about_ca_system_score_codex":0.00074987544,"about_ca_system_score_gemma":0.0007884304,"threshold_uncertainty_score":0.017543137},"labels":[],"label_agreement":null},{"id":"W3111884354","doi":"10.5210/fm.v26i1.11075","title":"Shades of hatred online: 4chan duplicate circulation surge during hybrid media events","year":2020,"lang":"en","type":"article","venue":"First Monday","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Phenomenon; Circulation (fluid dynamics); White (mutation); Political science; Computer science; History; Sociology; Media studies; Politics; Law; Engineering","score_opus":0.02020176290101351,"score_gpt":0.20599275682865814,"score_spread":0.18579099392764464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111884354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99576086,0.00006299445,0.00027164293,0.00012097754,0.000012638276,0.00001496312,0.00017787363,0.000027183381,0.0035508734],"genre_scores_gemma":[0.99859494,0.00006021708,0.0001841687,0.000054512995,0.000022397278,0.00001216012,0.0001289553,0.000009752144,0.00093294866],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99930143,0.00021465638,0.000029464729,0.0001134075,0.00022277114,0.00011830271],"domain_scores_gemma":[0.99475336,0.0021104815,0.0016422947,0.00033648504,0.0005748809,0.00058249675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005701358,0.00021558692,0.00029268698,0.0014355308,0.0015890456,0.0019173503,0.0002841379,0.0005431385,0.003450761],"category_scores_gemma":[0.0047623497,0.00025881603,0.000141422,0.0009871188,0.0009546822,0.001411943,0.0017860587,0.0008175093,0.00054710783],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063959,0.00019809682,0.7495934,0.00029099733,0.00014118942,0.0018736061,0.1608159,0.00024301324,0.01127633,0.001302317,0.006763656,0.06686191],"study_design_scores_gemma":[0.0000051986335,0.00011546809,0.92581034,0.00005486044,0.000025788626,0.0005176989,0.06405553,0.0005367975,0.0015528012,0.0002041914,0.007090818,0.000030603787],"about_ca_topic_score_codex":0.010324323,"about_ca_topic_score_gemma":0.023357742,"teacher_disagreement_score":0.010324323,"about_ca_system_score_codex":0.0007476018,"about_ca_system_score_gemma":0.0002789366,"threshold_uncertainty_score":0.020528495},"labels":[],"label_agreement":null},{"id":"W3112082559","doi":"10.1109/isi49825.2020.9280551","title":"A Bootstrapped Model to Detect Abuse and Intent in White Supremacist Corpora","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"White (mutation); Computer science; Computer security","score_opus":0.03160097666419798,"score_gpt":0.2336245148612458,"score_spread":0.20202353819704783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112082559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6812096,0.0024483197,0.26570064,0.0033525,0.00060240005,0.00055293203,0.014552025,0.017689943,0.013891638],"genre_scores_gemma":[0.8414671,0.0002999051,0.11475972,0.00061583915,0.0001985737,0.00050716667,0.03138536,0.00058384426,0.010182547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892825,0.00043409003,0.000051769166,0.00033344727,0.00014941754,0.00010299349],"domain_scores_gemma":[0.9955759,0.0029758834,0.00020361524,0.0005620866,0.00055198715,0.0001305329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023062804,0.0011796779,0.0005894447,0.0021473628,0.00092712475,0.0009651176,0.0015806163,0.0018400188,0.0023250154],"category_scores_gemma":[0.007880217,0.000506564,0.0007544082,0.001257758,0.0010235357,0.001976486,0.0013845982,0.0022902645,0.0030253616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014003838,0.0013083597,0.04052311,0.00083865353,0.00035295147,0.002270599,0.002698412,0.22438174,0.02488233,0.012159131,0.09800842,0.5911759],"study_design_scores_gemma":[0.000037158432,0.00009259867,0.004408452,0.00004787477,0.000030509109,0.00013552085,0.00025998388,0.97942126,0.0030363686,0.006835251,0.0056678085,0.000027165552],"about_ca_topic_score_codex":0.0076954872,"about_ca_topic_score_gemma":0.016694117,"teacher_disagreement_score":0.0076954872,"about_ca_system_score_codex":0.0009240228,"about_ca_system_score_gemma":0.0007004714,"threshold_uncertainty_score":0.015301406},"labels":[],"label_agreement":null},{"id":"W3112904657","doi":"10.7202/1074014ar","title":"Free Speech Rhetoric and Normalizing Violence: Setting Higher Standards for University Guest Speaker Policies","year":2020,"lang":"en","type":"article","venue":"Atlantis Critical Studies in Gender Culture & Social Justice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Rhetoric; Conflation; Harm; Free speech; Academic freedom; Political science; Speech recognition; Psychology; Linguistics; Computer science; Law; Higher education","score_opus":0.0516410948536728,"score_gpt":0.3330102275203122,"score_spread":0.2813691326666394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112904657","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33786833,0.0036678086,0.0495075,0.17768173,0.0017339296,0.000307383,0.00005677864,0.0004122275,0.42876434],"genre_scores_gemma":[0.9854403,0.00020704987,0.0041332664,0.0059053516,0.00018373122,0.0000811574,0.000008702091,0.00005257375,0.0039879056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94939935,0.029703975,0.0018805662,0.0029058917,0.010137085,0.005973174],"domain_scores_gemma":[0.8896177,0.07154089,0.009778041,0.0065564136,0.013067312,0.009439624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05439473,0.00051280577,0.00055132527,0.0033281315,0.018299118,0.019375993,0.002463089,0.006475188,0.0046560955],"category_scores_gemma":[0.12575847,0.00050417514,0.00040041312,0.001283174,0.03249085,0.008584402,0.009907826,0.010997995,0.00073095807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005972186,0.00014154623,0.007392906,0.00012463519,0.000013321148,0.000202289,0.08783637,0.00061483006,0.0018453443,0.8420029,0.0109448945,0.04882127],"study_design_scores_gemma":[0.0000829151,0.00027199212,0.030887086,0.0018234227,0.000068604975,0.00033384914,0.13661674,0.003456772,0.0102542555,0.5935467,0.22235753,0.00030012295],"about_ca_topic_score_codex":0.029037405,"about_ca_topic_score_gemma":0.04113937,"teacher_disagreement_score":0.05439473,"about_ca_system_score_codex":0.01664948,"about_ca_system_score_gemma":0.03377669,"threshold_uncertainty_score":0.2876703},"labels":[],"label_agreement":null},{"id":"W3113056411","doi":"10.5325/jinfopoli.10.2020.0329","title":"Conducting Critical Analysis on International Communication Rights Standards: The Contributions of Graphical Knowledge Modeling","year":2020,"lang":"en","type":"article","venue":"Journal of Information Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université TÉLUQ","funders":"","keywords":"Human rights; Normative; Fundamental rights; International human rights law; Context (archaeology); Political science; Sociology; Law and economics; Law; Engineering ethics; Public relations; Engineering","score_opus":0.029483536858032452,"score_gpt":0.33810743196171616,"score_spread":0.3086238951036837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113056411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19828652,0.0022006454,0.59459895,0.06639144,0.000844659,0.0012520379,0.00025494103,0.00036441398,0.13580634],"genre_scores_gemma":[0.8997694,0.0006043188,0.09559451,0.0012612069,0.00012948968,0.0007910456,0.0000740113,0.00011637685,0.0016596372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8027648,0.17037046,0.0038395235,0.004833118,0.016401859,0.0017901685],"domain_scores_gemma":[0.40950787,0.5182416,0.014228684,0.031401012,0.025358934,0.0012618154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15329763,0.00090100145,0.000980394,0.014030917,0.007610611,0.019480214,0.0028786985,0.0028401928,0.0040202094],"category_scores_gemma":[0.27431342,0.0008077714,0.0007886047,0.0066915257,0.05155973,0.028724449,0.01123025,0.00582222,0.000374544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003969443,0.000042022024,0.002021876,0.00016806694,0.000017566552,0.00009650002,0.06792886,0.00094561605,0.00025652285,0.899258,0.0013078386,0.027917432],"study_design_scores_gemma":[0.000023750856,0.000048210404,0.0014933286,0.0012986736,0.000034789853,0.000111432586,0.09968226,0.009017577,0.001963242,0.8465324,0.039737422,0.00005684113],"about_ca_topic_score_codex":0.0037925886,"about_ca_topic_score_gemma":0.004011453,"teacher_disagreement_score":0.15329763,"about_ca_system_score_codex":0.016257834,"about_ca_system_score_gemma":0.016687967,"threshold_uncertainty_score":0.8107251},"labels":[],"label_agreement":null},{"id":"W3114538195","doi":"10.33972/jhs.146","title":"Unity Starts with U: A Case Study of a Counter-Hate Campaign Through the Use of Social Media Platforms","year":2020,"lang":"en","type":"article","venue":"Journal of Hate Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Viewpoints; Grassroots; Social media; Narrative; Sociology; Inclusion (mineral); Public relations; Political science; The Internet; Media studies; Law; Gender studies; Computer science; Politics","score_opus":0.1340138740364548,"score_gpt":0.29525593679448153,"score_spread":0.16124206275802674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114538195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98768175,0.00012880923,0.00087809237,0.0013712025,0.000039985254,0.00019102823,0.000026931451,0.000015734418,0.009666398],"genre_scores_gemma":[0.99104285,0.00029959535,0.0011891056,0.00054853084,0.000028709921,0.00010986049,0.000016319465,0.00001878717,0.006746234],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9936527,0.004223943,0.00013580867,0.000427428,0.0005207239,0.0010393502],"domain_scores_gemma":[0.9923767,0.004238246,0.00090050144,0.00038574162,0.0004520511,0.001646754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031230887,0.00095528894,0.0007283134,0.0019391057,0.025044002,0.004447731,0.0017829075,0.0046001147,0.004341652],"category_scores_gemma":[0.007836613,0.00069157314,0.0005638838,0.0011894205,0.008594439,0.0033143738,0.004909066,0.004287585,0.0006830069],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009320627,0.0010580033,0.013657334,0.0001981693,0.00001941077,0.022070035,0.94109654,0.00015587988,0.0014754042,0.005392465,0.0015577037,0.013225804],"study_design_scores_gemma":[0.000018192193,0.0005574339,0.006942023,0.00016467637,0.000020857651,0.0054450617,0.9664628,0.0004696455,0.0017355239,0.0008422959,0.017303769,0.00003769244],"about_ca_topic_score_codex":0.019132147,"about_ca_topic_score_gemma":0.05077645,"teacher_disagreement_score":0.025044002,"about_ca_system_score_codex":0.0036689914,"about_ca_system_score_gemma":0.0030315637,"threshold_uncertainty_score":0.038041532},"labels":[],"label_agreement":null},{"id":"W3115116643","doi":"10.1109/ictai50040.2020.00087","title":"Deep Learning Ensembles for Hate Speech Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Offensive; Artificial intelligence; Class (philosophy); Task (project management); Ensemble learning; Natural language processing; Word (group theory); Speech recognition; Arabic; Embedding; Word embedding; Ensemble forecasting; Machine learning; Pattern recognition (psychology); Linguistics; Mathematics","score_opus":0.01723717314677962,"score_gpt":0.22473454023354938,"score_spread":0.20749736708676977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115116643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5222361,0.0037249594,0.45734334,0.0008988195,0.0004604218,0.00013642004,0.0012027546,0.005339566,0.008657611],"genre_scores_gemma":[0.9391806,0.00043222905,0.05282307,0.0002429492,0.0001361451,0.000079290825,0.002153534,0.000097883094,0.0048542903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918705,0.00017778727,0.00004285989,0.00022851209,0.00019139824,0.00017238883],"domain_scores_gemma":[0.99826944,0.00056586764,0.00013131417,0.000288183,0.0006158228,0.00012944832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018550766,0.0015996348,0.0011273194,0.001243037,0.00058977545,0.00073294505,0.001079103,0.0008866853,0.0014419017],"category_scores_gemma":[0.0038466307,0.00037501685,0.0007448952,0.00077686016,0.00029004356,0.0018022037,0.0015919905,0.0022961064,0.0011382721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004315509,0.00058374275,0.017137019,0.000104206054,0.0004197486,0.00016318745,0.00020400103,0.29515162,0.013911619,0.0019572924,0.009234403,0.66070163],"study_design_scores_gemma":[0.0000053595545,0.00009941315,0.0018221582,0.000014997665,0.00003875971,0.000033740984,0.000042856715,0.99103725,0.0044078575,0.0015555571,0.0009293131,0.000012756777],"about_ca_topic_score_codex":0.006805127,"about_ca_topic_score_gemma":0.011777793,"teacher_disagreement_score":0.006805127,"about_ca_system_score_codex":0.00068146136,"about_ca_system_score_gemma":0.00089896144,"threshold_uncertainty_score":0.013531029},"labels":[],"label_agreement":null},{"id":"W3115320986","doi":"10.1613/jair.1.12590","title":"Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Unintended consequences; Perspective (graphical); Human rights; Transparency (behavior); Situational ethics; The Internet; Redress; Social media; Situation awareness","score_opus":0.14740933450327634,"score_gpt":0.44267803961297403,"score_spread":0.2952687051096977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115320986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9604198,0.01618596,0.0022396347,0.010757528,0.00019042152,0.00013487502,0.0005369636,0.00003956811,0.009495174],"genre_scores_gemma":[0.9613071,0.026727378,0.001585643,0.0075937617,0.00016829582,0.00016793766,0.00045716215,0.00006621884,0.0019264296],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99033725,0.0049174405,0.0010567938,0.0005404059,0.0022576118,0.00089044275],"domain_scores_gemma":[0.9653189,0.020640366,0.006531169,0.0008042003,0.004762816,0.0019424424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0101445215,0.00030770228,0.00066655583,0.0027212466,0.0013796116,0.003552408,0.0006985885,0.0016995756,0.0027129953],"category_scores_gemma":[0.036628857,0.00046197476,0.0006635677,0.0024134654,0.0025762452,0.007821193,0.0030417296,0.002820992,0.0010185664],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015293235,0.00036200878,0.5712671,0.0027057426,0.00015132717,0.001981202,0.21146058,0.00020395822,0.0014305776,0.0049449983,0.020163938,0.1851756],"study_design_scores_gemma":[0.000013796314,0.00046760612,0.35246518,0.0042299135,0.00008464019,0.0073252292,0.5045439,0.0010417233,0.0006456196,0.002437768,0.12663305,0.0001115479],"about_ca_topic_score_codex":0.0026852805,"about_ca_topic_score_gemma":0.003046143,"teacher_disagreement_score":0.0101445215,"about_ca_system_score_codex":0.0009411062,"about_ca_system_score_gemma":0.0016352845,"threshold_uncertainty_score":0.053649962},"labels":[],"label_agreement":null},{"id":"W3116059311","doi":"10.5204/mcj.1849","title":"Cyberhate and Performative Speech in Accelerated Time(s)","year":2000,"lang":"en","type":"article","venue":"M/C Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Performative utterance; Art; Computer science; Aesthetics","score_opus":0.007576425183970766,"score_gpt":0.21225775847147785,"score_spread":0.2046813332875071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116059311","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2303045,0.0031868757,0.02172068,0.013441121,0.0009570625,0.000044746233,0.0000818738,0.00012983623,0.7301333],"genre_scores_gemma":[0.98630905,0.00031960156,0.0008317269,0.00059157313,0.00011845203,0.000020757469,0.000014245353,0.000031919837,0.01176266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965244,0.0015678811,0.00014001098,0.0004944541,0.0006707472,0.00060244114],"domain_scores_gemma":[0.9942934,0.0020054593,0.0011050443,0.0011828314,0.0007425987,0.00067063444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030908333,0.0003404964,0.00023050891,0.0009174319,0.0052920664,0.010679011,0.0007671136,0.0022483766,0.00804558],"category_scores_gemma":[0.0068266573,0.00024461967,0.00042160045,0.00051514414,0.033206876,0.009889684,0.0062789703,0.0032468722,0.00084592303],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042723917,0.000018417046,0.0016718645,0.00005670924,0.000009019471,0.00034859354,0.07686962,0.00022215296,0.00089030794,0.90645504,0.001732167,0.011683412],"study_design_scores_gemma":[0.00004110965,0.0002179881,0.0166184,0.000763987,0.000054392818,0.0017659495,0.16481899,0.001150117,0.0049820114,0.39859477,0.41085005,0.00014225343],"about_ca_topic_score_codex":0.0038029747,"about_ca_topic_score_gemma":0.0033434902,"teacher_disagreement_score":0.010679011,"about_ca_system_score_codex":0.0028511728,"about_ca_system_score_gemma":0.0017739555,"threshold_uncertainty_score":0.026915133},"labels":[],"label_agreement":null},{"id":"W3121347346","doi":"10.20944/preprints201612.0112.v1","title":"Online Shaming and the Right to Privacy","year":2016,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Hertfordshire","keywords":"Humiliation; Dignity; Internet privacy; Political science; Right to privacy; Sociology; Law; Computer science","score_opus":0.05817798166506588,"score_gpt":0.31236357848910046,"score_spread":0.2541855968240346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121347346","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.252687,0.0082614655,0.083950534,0.087576926,0.0008627502,0.00009331317,0.00011166707,0.000118207994,0.56633806],"genre_scores_gemma":[0.98392475,0.0011374687,0.0019432247,0.001575359,0.00014488929,0.00003298847,0.000015056988,0.000015674823,0.01121052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9897934,0.006671109,0.00029054738,0.00063479115,0.0017027142,0.0009074818],"domain_scores_gemma":[0.9853925,0.009747991,0.0015940415,0.0016948008,0.0009480751,0.00062257575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006841937,0.00031190758,0.00026207237,0.0009215109,0.0063184886,0.006125239,0.0010156363,0.003261138,0.007140896],"category_scores_gemma":[0.012624996,0.00023046035,0.0004060113,0.00060614187,0.0317914,0.014033286,0.007215487,0.0040358957,0.0006430139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001826061,0.000014925682,0.0006797509,0.000062844985,0.0000039506335,0.00018696352,0.036995787,0.00018801918,0.00026339793,0.9496744,0.0011486462,0.010763159],"study_design_scores_gemma":[0.000017893428,0.00007279262,0.0013673964,0.00056625705,0.000013807058,0.000912355,0.043367773,0.0010995275,0.0010243602,0.75784963,0.19365978,0.00004838594],"about_ca_topic_score_codex":0.0013784249,"about_ca_topic_score_gemma":0.00082176126,"teacher_disagreement_score":0.007140896,"about_ca_system_score_codex":0.00227806,"about_ca_system_score_gemma":0.0023484833,"threshold_uncertainty_score":0.036184072},"labels":[],"label_agreement":null},{"id":"W3121919814","doi":"10.2139/ssrn.2865792","title":"Honey Let's Just Keep the Doors Openn: A Critical Study on the Abrogation of Spousal Privileged Communication in Canada and Its Implications in Criminology","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Doors; Criminology; Political science; Sociology; Law; Engineering","score_opus":0.029689196969604793,"score_gpt":0.2655704978924521,"score_spread":0.2358813009228473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121919814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8997096,0.0019302571,0.0006577824,0.04280485,0.0002394491,0.00018730607,0.0001663915,0.000011577517,0.054292813],"genre_scores_gemma":[0.98905444,0.0007442864,0.0001701017,0.004126776,0.000024878022,0.000028515202,0.000021268566,0.0000159828,0.0058137625],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9882804,0.0025290311,0.00026684866,0.0007938038,0.0028830925,0.005246855],"domain_scores_gemma":[0.9597125,0.016968533,0.0033033814,0.0010417871,0.014458901,0.0045149135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009299855,0.00043023113,0.00071511214,0.0030517508,0.071678534,0.012392703,0.005220324,0.0067015267,0.004534788],"category_scores_gemma":[0.04791969,0.0008447217,0.0004823035,0.0053205923,0.034295414,0.0049362215,0.005583972,0.015368941,0.00021587037],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120323675,0.00021423842,0.04196478,0.00015379903,0.000039437957,0.0011223044,0.8194868,0.0002545523,0.00043427144,0.103520125,0.014972988,0.017716339],"study_design_scores_gemma":[0.00001472724,0.00003066574,0.032275178,0.0002384614,0.000045350418,0.000102953556,0.9299378,0.00023595449,0.00037093178,0.0021636558,0.034523137,0.00006112887],"about_ca_topic_score_codex":0.9962374,"about_ca_topic_score_gemma":0.99820507,"teacher_disagreement_score":0.18465355,"about_ca_system_score_codex":0.18465355,"about_ca_system_score_gemma":0.36503127,"threshold_uncertainty_score":0.94568634},"labels":[],"label_agreement":null},{"id":"W3122442690","doi":"10.1080/17439884.2021.1878218","title":"The hidden costs of connectivity: nature and effects of scholars’ online harassment","year":2021,"lang":"en","type":"article","venue":"Learning Media and Technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Royal Roads University; Western University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Harassment; Scope (computer science); Public relations; Variety (cybernetics); Face (sociological concept); Work (physics); Identity (music); Identity theft; Political science; Sociology; Internet privacy; Psychology; Social psychology; Social science; Engineering; Computer science; Aesthetics","score_opus":0.0033477594204498785,"score_gpt":0.21902170365443333,"score_spread":0.21567394423398345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122442690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9843072,0.00029885428,0.0004561201,0.0016957208,0.000028100783,0.000019698835,0.000045131048,0.00001726805,0.013131868],"genre_scores_gemma":[0.9994155,0.000088376,0.000074044634,0.00007427222,0.000025829147,0.000008017774,0.0000148541885,0.000005848858,0.0002932102],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9907246,0.0058945897,0.00040590847,0.0005022895,0.0014492637,0.0010233014],"domain_scores_gemma":[0.9059459,0.05495295,0.02411609,0.0048475647,0.0034549816,0.00668251],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0039401595,0.0003008896,0.000372968,0.0022193708,0.0033555974,0.006302178,0.0011519305,0.001329122,0.009924483],"category_scores_gemma":[0.065155976,0.00036990247,0.00043556164,0.0019059633,0.0046730847,0.0066355504,0.0068191863,0.002357401,0.000609519],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045455302,0.00066332385,0.8242295,0.00021418389,0.00016795813,0.0014605565,0.10655642,0.00058537675,0.0010486603,0.009485031,0.0019494285,0.053185023],"study_design_scores_gemma":[0.00003411296,0.00043446553,0.7249821,0.00028788342,0.00009785104,0.0010217247,0.2583544,0.00093309814,0.00042184765,0.00714555,0.00622326,0.00006372745],"about_ca_topic_score_codex":0.0037819005,"about_ca_topic_score_gemma":0.005075631,"teacher_disagreement_score":0.99605983,"about_ca_system_score_codex":0.0015538809,"about_ca_system_score_gemma":0.00090648496,"threshold_uncertainty_score":0.03320068},"labels":[],"label_agreement":null},{"id":"W3122979885","doi":"10.29173/alr2574","title":"Terrorist Speech under Bills C-51 and C-59 and the Othman Hamdan Case: The Continued Incoherence of Canada’s Approach","year":2019,"lang":"en","type":"article","venue":"Alberta Law Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrorism; Law; Acquittal; Sociology; The Internet; Bill of rights; Political science; Criminology; Human rights","score_opus":0.006438297534978721,"score_gpt":0.20002646064973728,"score_spread":0.19358816311475857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122979885","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1268901,0.007949639,0.0014448087,0.2378793,0.0018821238,0.00014190962,0.00031810463,0.000059279115,0.62343466],"genre_scores_gemma":[0.8122166,0.0037957253,0.0012025029,0.089697644,0.00046334363,0.0000854518,0.000131697,0.0000758473,0.09233122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9834759,0.0014728217,0.00044488517,0.0013624589,0.006458834,0.006785087],"domain_scores_gemma":[0.98671407,0.00418062,0.0006938511,0.0006064877,0.0041767224,0.0036281527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058060978,0.00040912477,0.00052668585,0.002091978,0.02953499,0.013971094,0.0039259773,0.011654027,0.006771377],"category_scores_gemma":[0.01504229,0.0006892738,0.00054888014,0.0025561992,0.016781641,0.002382685,0.003873705,0.012267987,0.00062112283],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056702138,0.00004385088,0.005532536,0.00009061364,0.000018493303,0.0018401067,0.0133960135,0.0005713348,0.00041002646,0.8547288,0.10105722,0.022254443],"study_design_scores_gemma":[0.0000928439,0.000042725198,0.04174886,0.0010634277,0.00009592125,0.0009405687,0.050058477,0.0020223001,0.00089568755,0.06071546,0.8419598,0.000363954],"about_ca_topic_score_codex":0.9908874,"about_ca_topic_score_gemma":0.99532336,"teacher_disagreement_score":0.15100096,"about_ca_system_score_codex":0.15100096,"about_ca_system_score_gemma":0.20123169,"threshold_uncertainty_score":0.98471856},"labels":[],"label_agreement":null},{"id":"W3123996955","doi":"","title":"Internet Surveillance, Regulation, and Chilling Effects Online: A Comparative Case Study","year":2017,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"The Internet; Skepticism; Internet privacy; Censorship; Business; Legal aspects of computing; Work (physics); State (computer science); Public relations; Political science; Law; Engineering; Computer science","score_opus":0.04277157871036618,"score_gpt":0.33245164513110753,"score_spread":0.28968006642074134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123996955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98518294,0.00019499756,0.00070664875,0.00072356366,0.000014984433,0.0001973125,0.00011557389,0.0000056562762,0.012858209],"genre_scores_gemma":[0.9940211,0.00068777136,0.001158201,0.00042541022,0.000032734908,0.00025279625,0.00012228671,0.0000116691635,0.0032880318],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9942264,0.0039727483,0.00017740266,0.0002986286,0.000579614,0.0007452687],"domain_scores_gemma":[0.98377305,0.011233738,0.0017246768,0.0010913654,0.0011817091,0.0009956108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004909124,0.00051823654,0.000555128,0.002790449,0.008061793,0.0027508945,0.0014873445,0.0034352066,0.0068447404],"category_scores_gemma":[0.014468643,0.00040671558,0.0005590935,0.003052057,0.003896403,0.0049122022,0.0037026147,0.0030601579,0.00060536823],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008096087,0.026204882,0.23485614,0.0008440332,0.00014629791,0.03488879,0.55255115,0.0021767176,0.0022238467,0.056720372,0.013479533,0.0750986],"study_design_scores_gemma":[0.00017878391,0.002320611,0.10252464,0.00056219904,0.00011771791,0.009802887,0.83123547,0.004391912,0.0019692956,0.005917738,0.040856097,0.00012272403],"about_ca_topic_score_codex":0.017702138,"about_ca_topic_score_gemma":0.03663141,"teacher_disagreement_score":0.017702138,"about_ca_system_score_codex":0.0040735602,"about_ca_system_score_gemma":0.0016839524,"threshold_uncertainty_score":0.03519821},"labels":[],"label_agreement":null},{"id":"W3124868960","doi":"10.1086/524316","title":"Hate in the classroom: Free expression, Holocaust denial, and liberal education","year":2008,"lang":"en","type":"article","venue":"Repository@Hull (Worktribe) (University of Hull)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Denial; The Holocaust; Argument (complex analysis); Constructive; Expression (computer science); Sociology; Psychoanalysis; Law; Epistemology; Psychology; Pedagogy; Political science; Philosophy; Medicine; Computer science","score_opus":0.00748767919206042,"score_gpt":0.1735993123577153,"score_spread":0.1661116331656549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124868960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7199047,0.0066643786,0.0062778564,0.046837695,0.00020928579,0.000061307626,0.00003572654,0.000060375794,0.21994855],"genre_scores_gemma":[0.99591124,0.0005426898,0.00016556156,0.00055780314,0.000011378017,0.0000055676064,0.000003385918,0.0000058827745,0.002796476],"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936904,0.0030844123,0.00014092456,0.0003266563,0.001353426,0.0014042582],"domain_scores_gemma":[0.99191916,0.004575093,0.0012670095,0.0004751991,0.00069512235,0.0010683739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004096646,0.00030479106,0.0004070978,0.0018850734,0.016586611,0.009242998,0.00088566233,0.0022482835,0.0024277188],"category_scores_gemma":[0.010798311,0.0002458622,0.00019995742,0.0014030066,0.078723066,0.006492559,0.007551285,0.005734146,0.00016348746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021751874,0.00004573906,0.0077440664,0.000054622804,0.0000045989045,0.00030256697,0.76721495,0.00007668527,0.00030817385,0.2016513,0.0017596048,0.020815866],"study_design_scores_gemma":[0.000010454267,0.000035993362,0.020876586,0.0003258464,0.000014975502,0.0007201296,0.85241854,0.00033800723,0.0006482909,0.0411487,0.08337479,0.00008765556],"about_ca_topic_score_codex":0.4744764,"about_ca_topic_score_gemma":0.5470847,"teacher_disagreement_score":0.4744764,"about_ca_system_score_codex":0.026147313,"about_ca_system_score_gemma":0.014378044,"threshold_uncertainty_score":0.9434293},"labels":[],"label_agreement":null},{"id":"W3124930350","doi":"","title":"Fired Over Facebook: The Consequences of Discussing Work Online","year":2012,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Dismissal; Social media; Doctrine; Labour law; Unfair dismissal; Public relations; Context (archaeology); Work (physics); Political science; Internet privacy; Business; Sociology; Law and economics; Law; Engineering; Computer science; History","score_opus":0.09760661926807908,"score_gpt":0.3082317127904461,"score_spread":0.21062509352236702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124930350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9219555,0.00037141374,0.0009017693,0.006610273,0.00021933921,0.000037989335,0.00007762812,0.00004091718,0.06978515],"genre_scores_gemma":[0.9939283,0.00019551728,0.000196431,0.0011929276,0.00007294244,0.00001793578,0.000029093493,0.000016616796,0.004350225],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9916345,0.0045420094,0.00022308454,0.0003809817,0.0022823995,0.00093706656],"domain_scores_gemma":[0.9687284,0.018777957,0.0067790467,0.00178045,0.0019163312,0.0020178258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005515969,0.00037319175,0.00024617324,0.0016598761,0.012789742,0.0056354944,0.000985511,0.0029810923,0.0055977656],"category_scores_gemma":[0.03387088,0.0003811115,0.00038349902,0.0008190868,0.006619361,0.0030868584,0.004195969,0.004272888,0.00080836547],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006558201,0.00061765936,0.22999449,0.00013005917,0.000101441576,0.006002401,0.6420432,0.00045005154,0.003614492,0.028593142,0.01780083,0.06999642],"study_design_scores_gemma":[0.00003279743,0.0002256816,0.10956218,0.0003919991,0.000114315,0.0040699467,0.8161784,0.0020898515,0.003239988,0.007674553,0.056285076,0.00013530524],"about_ca_topic_score_codex":0.042319287,"about_ca_topic_score_gemma":0.0766927,"teacher_disagreement_score":0.042319287,"about_ca_system_score_codex":0.0029751149,"about_ca_system_score_gemma":0.0017281997,"threshold_uncertainty_score":0.08414596},"labels":[],"label_agreement":null},{"id":"W3125966627","doi":"10.29173/alr280","title":"Criminalizing Terrorist Babble: Canada&apos;s Dubious New Terrorist Speech Crime","year":2015,"lang":"en","type":"article","venue":"TSpace","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of Ottawa","funders":"","keywords":"Terrorism; Radicalization; Charter; Law; Criminal offence; Commission; Government (linguistics); Political science; Criminology; The Internet; Criminal law; Sociology; Computer science","score_opus":0.06568959317489126,"score_gpt":0.32602203119828577,"score_spread":0.2603324380233945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125966627","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3327436,0.0062969537,0.0031408248,0.15804772,0.001740253,0.00018709445,0.0011934206,0.0002448622,0.49640533],"genre_scores_gemma":[0.77381575,0.0042376295,0.002166738,0.025861343,0.00023498184,0.000039418064,0.0003058408,0.00009286557,0.19324552],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974809,0.00015947343,0.000039822608,0.0001972864,0.0013781799,0.00074435014],"domain_scores_gemma":[0.99699235,0.00058692676,0.00021026765,0.00016021541,0.0012747643,0.0007754086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011651797,0.00032830995,0.00017791842,0.0010080689,0.015804471,0.005456562,0.0010005549,0.0027002373,0.0054110303],"category_scores_gemma":[0.005840833,0.00027534814,0.00029695197,0.00092098815,0.0059664254,0.0016907001,0.0014013909,0.0044133114,0.00047431546],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013345756,0.00008220844,0.025682406,0.0002424178,0.000030987954,0.0018023432,0.0600698,0.00065409066,0.0022883955,0.36010835,0.39170176,0.15720382],"study_design_scores_gemma":[0.000026604052,0.000057394478,0.06531861,0.00033875293,0.0000658483,0.00097405777,0.041563943,0.0011360256,0.002248683,0.00603041,0.88208103,0.00015873095],"about_ca_topic_score_codex":0.9904146,"about_ca_topic_score_gemma":0.9973984,"teacher_disagreement_score":0.04527908,"about_ca_system_score_codex":0.04527908,"about_ca_system_score_gemma":0.091835774,"threshold_uncertainty_score":0.32852423},"labels":[],"label_agreement":null},{"id":"W3128801478","doi":"10.1177/0002764221989777","title":"Mapping out Violence Against Women of Influence on Twitter Using the Cyber–Lifestyle Routine Activity Theory","year":2021,"lang":"en","type":"article","venue":"American Behavioral Scientist","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada; Global Affairs Canada","keywords":"Harassment; Legal guardian; Moderation; Social media; Psychology; Online and offline; Criminology; Social psychology; Internet privacy; Political science; Computer science; Law","score_opus":0.022198406863354687,"score_gpt":0.29098055719573646,"score_spread":0.2687821503323818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128801478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98491096,0.00014989378,0.005472987,0.00033442778,0.000010438064,0.00011556755,0.00035481446,0.000031127758,0.008619759],"genre_scores_gemma":[0.99737644,0.00013401608,0.0016988617,0.00003723711,0.000010307075,0.000083616345,0.00014072987,0.0000045718266,0.000514219],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99914896,0.00038282442,0.00006170342,0.00013185607,0.00013326183,0.0001413986],"domain_scores_gemma":[0.9973579,0.001231276,0.00084276986,0.00019771332,0.00023038223,0.00013996195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080378057,0.0002954147,0.00018680535,0.0038388714,0.0009624729,0.0019118721,0.00050061085,0.00029380948,0.0016517689],"category_scores_gemma":[0.004165368,0.00017505836,0.00034926675,0.0028187782,0.0011179812,0.0016071972,0.0018178705,0.00040719137,0.00027246668],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008147334,0.00011570148,0.87686265,0.00020582574,0.00007300298,0.00038135512,0.042946357,0.0005866268,0.0011246179,0.005403437,0.00094235106,0.071276605],"study_design_scores_gemma":[0.0000069503485,0.00014675804,0.87584126,0.00018291805,0.000071191935,0.00043056833,0.10266079,0.008697288,0.00095901923,0.0031482624,0.007819881,0.000035058525],"about_ca_topic_score_codex":0.012296998,"about_ca_topic_score_gemma":0.0172058,"teacher_disagreement_score":0.012296998,"about_ca_system_score_codex":0.0009784498,"about_ca_system_score_gemma":0.00085900165,"threshold_uncertainty_score":0.024450839},"labels":[],"label_agreement":null},{"id":"W3132587254","doi":"10.1089/elj.2020.0652","title":"Combatting Foreign Election Interference: Canada's Electoral Ecosystem Approach to Disinformation and Cyber Threats","year":2021,"lang":"en","type":"article","venue":"Election Law Journal Rules Politics and Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disinformation; Legitimacy; Democracy; Politics; Political economy; Business; Political science; Economics; Social media; Law","score_opus":0.011456359068477522,"score_gpt":0.24303519004561383,"score_spread":0.2315788309771363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132587254","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1347788,0.0064790444,0.014638409,0.15526864,0.0011881368,0.00036603003,0.00045420238,0.00034554242,0.68648124],"genre_scores_gemma":[0.8846268,0.0036806297,0.00926486,0.011931178,0.00012830002,0.000073860894,0.00020024869,0.00006533972,0.09002885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99647397,0.00044437044,0.000051012204,0.00019943346,0.0014039719,0.0014273229],"domain_scores_gemma":[0.9968353,0.00034245153,0.00014612213,0.00010931774,0.0015867542,0.000980053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016630675,0.0004195017,0.00026690427,0.0029798339,0.016102005,0.010295494,0.0016325044,0.0021019974,0.0050752987],"category_scores_gemma":[0.0040640305,0.00024357378,0.0004961494,0.0022815145,0.0054866774,0.0024722633,0.0035260948,0.0029794571,0.00043093038],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011013294,0.00012667886,0.044375625,0.00036769567,0.000107386346,0.0013545906,0.019260155,0.004993491,0.001726832,0.627344,0.11283668,0.1873968],"study_design_scores_gemma":[0.000042813623,0.00009551794,0.038193714,0.00037212786,0.00011177867,0.00043789498,0.033967413,0.00597764,0.001544736,0.033900358,0.8852074,0.0001484806],"about_ca_topic_score_codex":0.97584856,"about_ca_topic_score_gemma":0.99154186,"teacher_disagreement_score":0.9261559,"about_ca_system_score_codex":0.07384409,"about_ca_system_score_gemma":0.18687192,"threshold_uncertainty_score":0.5357789},"labels":[],"label_agreement":null},{"id":"W3132798273","doi":"10.21810/jicw.v2i2.1058","title":"#FakeNews","year":2019,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Misinformation; Reputation; Internet privacy; Government (linguistics); Fake news; Political science; Dissemination; Computer security; Public relations; Advertising; Business; Computer science; Law","score_opus":0.016026223967942975,"score_gpt":0.2423823380108765,"score_spread":0.22635611404293352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132798273","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0100276265,0.0007695996,0.01727975,0.005615451,0.0032718107,0.00050112815,0.008537874,0.009479857,0.9445169],"genre_scores_gemma":[0.049046926,0.0009070314,0.0077705616,0.0032129865,0.00049802137,0.00024898423,0.0068232804,0.0023304701,0.9291617],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9967541,0.00039258055,0.00022386298,0.0005086255,0.001655656,0.00046511067],"domain_scores_gemma":[0.99299675,0.0011433315,0.0005494144,0.0028731788,0.0020476894,0.00038971825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016425522,0.0011637243,0.0006166817,0.0025198476,0.0034531623,0.0072292485,0.0018590312,0.0026938105,0.4575105],"category_scores_gemma":[0.010173806,0.0007109298,0.00068715046,0.0016191384,0.0014688392,0.0069636703,0.0051432136,0.0020324914,0.27781516],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022332593,0.000110253226,0.003157716,0.0003848869,0.00002394182,0.00066340837,0.0013706731,0.00023489774,0.0027911975,0.06478493,0.64243823,0.2838165],"study_design_scores_gemma":[0.000007606899,0.000022277829,0.001143474,0.00008810035,0.0000074220384,0.00037488082,0.00031956605,0.00037654335,0.0016599555,0.0038372932,0.99213785,0.000025009256],"about_ca_topic_score_codex":0.005793588,"about_ca_topic_score_gemma":0.0067492146,"teacher_disagreement_score":0.4575105,"about_ca_system_score_codex":0.0017242145,"about_ca_system_score_gemma":0.0015764041,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3134349690","doi":"10.22329/il.v41i1.6688","title":"Picturing a Thousand Unspoken Words","year":2021,"lang":"en","type":"article","venue":"Informal Logic","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Injustice; Context (archaeology); Epistemology; Argument (complex analysis); Argumentative; Identity (music); Aesthetics; Sociology; Mode (computer interface); Psychology; Philosophy; Social psychology; Computer science","score_opus":0.012149233236909368,"score_gpt":0.2239499615804703,"score_spread":0.21180072834356095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134349690","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18421179,0.0065028393,0.22412422,0.009074207,0.0064127427,0.0005415135,0.0047871755,0.0043624826,0.559983],"genre_scores_gemma":[0.75351954,0.0028189917,0.1200967,0.0015850532,0.0006995173,0.00044663175,0.0027092209,0.0016727566,0.11645166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99956506,0.00014168269,0.000033474204,0.00009483006,0.00012894759,0.000036022753],"domain_scores_gemma":[0.9981542,0.0010368816,0.00016355641,0.00028420228,0.00029041388,0.00007064693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004680748,0.0007301442,0.0002914495,0.0017865765,0.0018790461,0.003920385,0.0005536088,0.000974281,0.038499188],"category_scores_gemma":[0.0054731565,0.00025962887,0.00033627666,0.0018923501,0.0022458332,0.0057433452,0.0014413038,0.0014218383,0.011233259],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006524176,0.00007967421,0.0034201096,0.0012487771,0.000045575456,0.0020070137,0.061576422,0.001323213,0.022864893,0.46995425,0.09875069,0.3380769],"study_design_scores_gemma":[0.000017173676,0.00012301741,0.0033397104,0.00073753245,0.000025461606,0.001617598,0.017917477,0.0030353693,0.005370985,0.0872016,0.8805514,0.00006262303],"about_ca_topic_score_codex":0.00088995113,"about_ca_topic_score_gemma":0.001262265,"teacher_disagreement_score":0.038499188,"about_ca_system_score_codex":0.0007687247,"about_ca_system_score_gemma":0.00042750363,"threshold_uncertainty_score":0.12879258},"labels":[],"label_agreement":null},{"id":"W3135553445","doi":"","title":"Euphemism and Gender: A Computational Inquiry.","year":2020,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Euphemism; Psychology; Cognitive science; Computer science; Linguistics; Epistemology; Philosophy","score_opus":0.0280304265995758,"score_gpt":0.2205656155358484,"score_spread":0.1925351889362726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135553445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.651548,0.005323599,0.23022923,0.026512397,0.00040503495,0.00028224176,0.013349645,0.00066683354,0.07168292],"genre_scores_gemma":[0.95542365,0.00050636654,0.036974303,0.00053592946,0.00009593924,0.00018322894,0.002569569,0.000066124485,0.003644895],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99797326,0.0011226223,0.000075919255,0.00044522627,0.000282471,0.000100555764],"domain_scores_gemma":[0.97830963,0.018910283,0.0010149353,0.00089980394,0.00045501234,0.00041023953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033774967,0.0005256522,0.00046703743,0.0022817173,0.0014247504,0.003976234,0.0011425535,0.00087427726,0.00887447],"category_scores_gemma":[0.022102877,0.0003890147,0.0013832512,0.0026383796,0.0027914855,0.0053922217,0.0023739552,0.0014439003,0.0006814337],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065219414,0.0003470016,0.4064441,0.00073185744,0.000449256,0.0004901971,0.008096679,0.01409709,0.0023876983,0.39942452,0.020412402,0.1464669],"study_design_scores_gemma":[0.00008087663,0.00016228207,0.20065321,0.00023032795,0.00020028211,0.0012499276,0.011621507,0.20967211,0.0019514116,0.5057903,0.06826166,0.00012618331],"about_ca_topic_score_codex":0.0072442293,"about_ca_topic_score_gemma":0.010559144,"teacher_disagreement_score":0.00887447,"about_ca_system_score_codex":0.0014000969,"about_ca_system_score_gemma":0.0012074421,"threshold_uncertainty_score":0.02968806},"labels":[],"label_agreement":null},{"id":"W3136339167","doi":"","title":"Sentiment Analysis on Shedecides Post From Twitter","year":2021,"lang":"en","type":"article","venue":"Solid State Technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Social media; Microblogging; Quarter (Canadian coin); Computer science; Process (computing); Unit (ring theory); World Wide Web; Internet privacy; Data science; Advertising; Psychology; Artificial intelligence; Business; Geography; Mathematics education","score_opus":0.008206092621474405,"score_gpt":0.25071164858611716,"score_spread":0.24250555596464277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136339167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93902266,0.00032540242,0.0058860565,0.0011774526,0.000573524,0.00047242083,0.02596637,0.00051717483,0.02605886],"genre_scores_gemma":[0.94775254,0.00047885982,0.008550744,0.00025570043,0.00047851147,0.0005441719,0.0266528,0.000088872504,0.015197858],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995491,0.000058375772,0.000055792723,0.000056534922,0.00020148777,0.00007867787],"domain_scores_gemma":[0.99919695,0.00018290023,0.00013556908,0.000030620606,0.00039940208,0.00005463327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003906206,0.00035438838,0.0002486439,0.0021313953,0.0006237363,0.00073090446,0.00015669716,0.00027401882,0.0029613136],"category_scores_gemma":[0.0013398738,0.00007016368,0.00029856613,0.0015404541,0.0002010241,0.00056140946,0.000371446,0.00031881855,0.0016992036],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018672023,0.000647622,0.30766922,0.0018555344,0.00024379202,0.004509897,0.007963061,0.002454864,0.11568145,0.0040062554,0.15837756,0.39472362],"study_design_scores_gemma":[0.000041880463,0.00045311332,0.79716635,0.00015924108,0.00015427583,0.0013590691,0.015749725,0.038061988,0.03199645,0.0015764765,0.11317348,0.00010792202],"about_ca_topic_score_codex":0.001798542,"about_ca_topic_score_gemma":0.0039342428,"teacher_disagreement_score":0.0029613136,"about_ca_system_score_codex":0.00029356952,"about_ca_system_score_gemma":0.00023743146,"threshold_uncertainty_score":0.00990659},"labels":[],"label_agreement":null},{"id":"W3137260078","doi":"10.1177/17470161211003021","title":"Queer considerations: Exploring the use of social media for research recruitment within LGBTQ communities","year":2021,"lang":"en","type":"article","venue":"Research Ethics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Saint Vincent University","funders":"Mount Saint Vincent University","keywords":"Social media; Harassment; Queer; Public relations; Sociology; Coronavirus disease 2019 (COVID-19); Psychology; Internet privacy; Social psychology; Political science; Gender studies; Computer science","score_opus":0.9052681565015958,"score_gpt":0.5311391777554012,"score_spread":0.37412897874619455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137260078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7319384,0.0023590343,0.046882246,0.103813484,0.00058449194,0.0040658237,0.00021808087,0.0001557845,0.10998257],"genre_scores_gemma":[0.97115165,0.00102722,0.009182072,0.0077931066,0.00011714658,0.0017116874,0.00004847722,0.00007535386,0.008893271],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.8642982,0.12109801,0.0018763983,0.0027806289,0.0060496726,0.003897042],"domain_scores_gemma":[0.7611497,0.20260061,0.015188327,0.0054865023,0.010526316,0.005048521],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10335149,0.000689673,0.00046975614,0.002977782,0.027945835,0.016908104,0.003886839,0.0058284136,0.008968062],"category_scores_gemma":[0.20893212,0.0008499564,0.0007563435,0.0022010303,0.024158482,0.018257542,0.009879178,0.0071827965,0.0009715234],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040251376,0.000060416165,0.0042166268,0.00016985826,0.000010074673,0.0005732556,0.96930045,0.000041381383,0.00030352618,0.013581848,0.0011892316,0.0105131],"study_design_scores_gemma":[0.000033451917,0.00009335438,0.0031247935,0.00065491797,0.000022715572,0.000466202,0.9522289,0.00045541202,0.0005177219,0.008790532,0.03357444,0.000037567694],"about_ca_topic_score_codex":0.033266164,"about_ca_topic_score_gemma":0.07482203,"teacher_disagreement_score":0.8966485,"about_ca_system_score_codex":0.015315823,"about_ca_system_score_gemma":0.023516543,"threshold_uncertainty_score":0.54658145},"labels":[],"label_agreement":null},{"id":"W3138128104","doi":"10.31235/osf.io/ezfp8","title":"Online Disinformation and Harmful Speech: Dangers for Democratic Participation and Possible Policy Responses","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of British Columbia; Communications Security Establishment","funders":"","keywords":"Disinformation; Democracy; Civil society; Political science; Government (linguistics); Social media; Public relations; Politics; Context (archaeology); Journalism; Internet privacy; Law enforcement; Law; Computer science","score_opus":0.03719116041515821,"score_gpt":0.2956244163397363,"score_spread":0.2584332559245781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138128104","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05020991,0.010571178,0.0035414498,0.81543285,0.0013054578,0.00008742611,0.0002716365,0.00011532776,0.118464835],"genre_scores_gemma":[0.8224597,0.01682304,0.0037620019,0.13209718,0.0013804482,0.00016572393,0.0002445052,0.000105368985,0.022962004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9723722,0.010585038,0.0006015969,0.0014491865,0.00786254,0.007129433],"domain_scores_gemma":[0.9455916,0.030107176,0.005505743,0.0019091482,0.010624567,0.006261695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017449237,0.0005917698,0.00051914627,0.003889574,0.029340448,0.025828127,0.0024706435,0.009733102,0.008830578],"category_scores_gemma":[0.040513344,0.00048833614,0.0006842417,0.003574688,0.03454722,0.014389395,0.008223192,0.01286577,0.0013834408],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009912735,0.00011874337,0.017138762,0.00077487726,0.000046249144,0.0018670296,0.12752078,0.0008346481,0.0007986089,0.5795265,0.13293327,0.13834135],"study_design_scores_gemma":[0.000030832583,0.00005101889,0.012582411,0.0023403103,0.00006308595,0.00065961457,0.21202675,0.0012334274,0.0014029354,0.15330255,0.6161113,0.00019574312],"about_ca_topic_score_codex":0.6176028,"about_ca_topic_score_gemma":0.7031959,"teacher_disagreement_score":0.6176028,"about_ca_system_score_codex":0.043835312,"about_ca_system_score_gemma":0.088643916,"threshold_uncertainty_score":0.7692984},"labels":[],"label_agreement":null},{"id":"W3138190335","doi":"10.1007/978-3-030-75762-5_55","title":"AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection","year":2021,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Multi-task learning; Computer science; Voice activity detection; Task (project management); Artificial intelligence; Speech recognition; Emotion detection; Joint (building); Identification (biology); Natural language processing; Machine learning; Speech processing; Emotion recognition","score_opus":0.01452091940684636,"score_gpt":0.23903550936458817,"score_spread":0.22451458995774182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138190335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084582604,0.0014329451,0.8074286,0.00051001756,0.00070728146,0.00037150935,0.003474416,0.09467934,0.00681326],"genre_scores_gemma":[0.48982376,0.00050926197,0.44402307,0.0006048662,0.0003536592,0.00052315113,0.0074416245,0.0031553123,0.05356534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928904,0.00012829727,0.000017886932,0.00024271525,0.00018195322,0.00014010248],"domain_scores_gemma":[0.99948454,0.00017341647,0.000028221932,0.00010296774,0.00014317977,0.00006762791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010461679,0.0024553756,0.0013983,0.0011300596,0.000633419,0.00116425,0.0016075331,0.0023270515,0.009278738],"category_scores_gemma":[0.0019081904,0.00069978816,0.000779564,0.0006131884,0.00031672636,0.0013746887,0.002339873,0.001495179,0.008306351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001588036,0.00049958547,0.0042973934,0.00015084023,0.0003146282,0.00026888592,0.00012308733,0.016093362,0.07148074,0.00097346364,0.04358258,0.8606275],"study_design_scores_gemma":[0.0001165485,0.00036982636,0.006181554,0.00002185742,0.0001256815,0.00028712262,0.000068906666,0.89816934,0.082949184,0.0034058485,0.008231111,0.00007304139],"about_ca_topic_score_codex":0.0055325273,"about_ca_topic_score_gemma":0.012539322,"teacher_disagreement_score":0.009278738,"about_ca_system_score_codex":0.0005654042,"about_ca_system_score_gemma":0.000720149,"threshold_uncertainty_score":0.03104049},"labels":[],"label_agreement":null},{"id":"W3139526910","doi":"10.21810/jicw.v3i1.2361","title":"Eliminate Hate","year":2020,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Hate crime; Face (sociological concept); Criminology; Political science; Law; Psychology; State (computer science); Sociology; Computer science; Social science","score_opus":0.03585800743930783,"score_gpt":0.25372797309833944,"score_spread":0.2178699656590316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139526910","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39147794,0.0048321504,0.027454967,0.012235519,0.001459153,0.0011868947,0.002977957,0.0012803813,0.557095],"genre_scores_gemma":[0.7676882,0.0040067104,0.015845485,0.0056694886,0.00017129819,0.00020895526,0.0017592248,0.00018948926,0.2044611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874914,0.00011341856,0.000033835975,0.000112822854,0.0006159516,0.000374937],"domain_scores_gemma":[0.99755716,0.00017116562,0.0003051683,0.00031811232,0.0012232516,0.0004250509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010775421,0.00055203016,0.00037210644,0.0012502029,0.004185641,0.0023305803,0.0009445997,0.0008856399,0.026545381],"category_scores_gemma":[0.0049161944,0.00019650128,0.00052695704,0.0005546726,0.0018313148,0.001415835,0.0023061398,0.0011410828,0.0066441526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015728496,0.00034339778,0.09210637,0.0006429621,0.00008710937,0.00086465484,0.013984812,0.0003903586,0.012716054,0.025476491,0.12335249,0.72987807],"study_design_scores_gemma":[0.000037013713,0.00036084178,0.28307274,0.0008625535,0.00014478926,0.003108815,0.027044348,0.0017314213,0.017008426,0.009928342,0.6565637,0.00013699303],"about_ca_topic_score_codex":0.14566472,"about_ca_topic_score_gemma":0.2867767,"teacher_disagreement_score":0.14566472,"about_ca_system_score_codex":0.0036969185,"about_ca_system_score_gemma":0.007652798,"threshold_uncertainty_score":0.2896337},"labels":[],"label_agreement":null},{"id":"W3146515511","doi":"10.60082/2817-5069.3631","title":"Voter Privacy and Big-Data Elections","year":2021,"lang":"en","type":"article","venue":"Osgoode Hall law journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"Social Sciences and Humanities Research Council of Canada; University of Ottawa","keywords":"Big data; Appeal; Politics; Internet privacy; Information privacy; Election law; Democracy; Political science; Voter registration; Turnout; Voter turnout; Variety (cybernetics); Federal election; Analytics; Law; Voting; Computer science; Data science","score_opus":0.045430734376955224,"score_gpt":0.25187515748305733,"score_spread":0.20644442310610212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146515511","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13637142,0.016078873,0.0808214,0.26227576,0.0014016592,0.00048856647,0.004007789,0.00061805634,0.49793646],"genre_scores_gemma":[0.9399706,0.0050156144,0.010040652,0.021771945,0.00075395714,0.00018288672,0.0009643801,0.00011985931,0.021179982],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.974595,0.011425686,0.00081475,0.002097247,0.0076642656,0.0034031027],"domain_scores_gemma":[0.9492482,0.028309869,0.005118392,0.0082621435,0.006881624,0.0021797253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018900644,0.00026801456,0.0004929756,0.002429446,0.0077544074,0.010237353,0.0013977502,0.0026198144,0.0049565574],"category_scores_gemma":[0.059385747,0.0004616714,0.0005971356,0.0043242117,0.008878112,0.007006529,0.004326226,0.004885378,0.0008447579],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013663172,0.000056331453,0.038967203,0.00027894645,0.000054345957,0.00021708074,0.008787554,0.0014098547,0.00046042545,0.78299546,0.048857305,0.117778815],"study_design_scores_gemma":[0.000040705843,0.000064447995,0.05124653,0.0011486623,0.00007280218,0.00032563246,0.007572854,0.0054131094,0.0025531247,0.30906615,0.62236583,0.0001302423],"about_ca_topic_score_codex":0.21447991,"about_ca_topic_score_gemma":0.26254842,"teacher_disagreement_score":0.21447991,"about_ca_system_score_codex":0.00988692,"about_ca_system_score_gemma":0.019529294,"threshold_uncertainty_score":0.426463},"labels":[],"label_agreement":null},{"id":"W3149867861","doi":"10.1109/asonam49781.2020.9381476","title":"Generalisation of Cyberbullying Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Software portability; Computer science; Classifier (UML); Machine learning; Variety (cybernetics); Generalization; Artificial intelligence","score_opus":0.021577506108163225,"score_gpt":0.20119067841200722,"score_spread":0.179613172303844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149867861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6718159,0.0026666785,0.30092317,0.0028373667,0.00043866332,0.00045132168,0.0052580126,0.0039691073,0.011639789],"genre_scores_gemma":[0.9688336,0.00042217624,0.021289583,0.00035163038,0.00024404553,0.00011535945,0.0058097253,0.00022563161,0.0027083813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952708,0.0015724237,0.00023963442,0.0017854901,0.0007273439,0.00040437904],"domain_scores_gemma":[0.9852031,0.007563844,0.00071460044,0.0036005913,0.002545577,0.00037227845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0109847775,0.001418591,0.0017760559,0.0046538883,0.001014801,0.0022296156,0.0018065083,0.0020872839,0.0038782342],"category_scores_gemma":[0.027598664,0.00055361463,0.0024981091,0.0022160376,0.00096664095,0.0036821582,0.0019295454,0.0025820876,0.0016127464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000922848,0.0006434293,0.32052362,0.000693232,0.0025129104,0.00078493037,0.0013524898,0.20122205,0.015947856,0.012364244,0.02716933,0.4158631],"study_design_scores_gemma":[0.000044631484,0.00028302963,0.12902217,0.00012457973,0.0002616809,0.00087153085,0.00061478565,0.82924086,0.0031966649,0.029861672,0.00639375,0.000084686944],"about_ca_topic_score_codex":0.012978838,"about_ca_topic_score_gemma":0.014400706,"teacher_disagreement_score":0.012978838,"about_ca_system_score_codex":0.0014850209,"about_ca_system_score_gemma":0.0008123078,"threshold_uncertainty_score":0.058093727},"labels":[],"label_agreement":null},{"id":"W3153182107","doi":"10.1109/tse.2021.3073773","title":"On the Relationship Between the Developer’s Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Software Engineering","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Ethnic group; Race (biology); Diversity (politics); White (mutation); Empirical research; Computer science; Open source software; Software; Data science; Sociology; Mathematics; Gender studies; Statistics","score_opus":0.04667324933384913,"score_gpt":0.28521615133311873,"score_spread":0.23854290199926959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153182107","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916892,0.00012231205,0.0019890522,0.00037613197,0.000032779244,0.00013360957,0.00011475851,0.000008009277,0.005534155],"genre_scores_gemma":[0.9952324,0.00010210307,0.0019851278,0.00015715197,0.000025349695,0.00039941206,0.0001608137,0.00001525698,0.0019223463],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98056406,0.012709078,0.0013654069,0.0017371662,0.002477838,0.0011463925],"domain_scores_gemma":[0.67026794,0.26638928,0.032248225,0.008981235,0.01704319,0.0050702067],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03364614,0.0005169735,0.00049833255,0.0024495346,0.0020878094,0.0031575973,0.00096204435,0.0012644588,0.0074626603],"category_scores_gemma":[0.11467705,0.00038130808,0.0011507061,0.0023892806,0.0028273626,0.0025270213,0.003485383,0.0018377221,0.000922988],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030347385,0.0006207759,0.9671214,0.00009213253,0.00012540413,0.000093001734,0.019041296,0.00034461712,0.00033559438,0.0012193008,0.00047155868,0.010231504],"study_design_scores_gemma":[0.00002452569,0.00044362398,0.96844804,0.00018879081,0.00018515339,0.00006969171,0.024392689,0.0017113417,0.00097629585,0.0012555317,0.0022575583,0.000046759047],"about_ca_topic_score_codex":0.013195021,"about_ca_topic_score_gemma":0.019855184,"teacher_disagreement_score":0.96635383,"about_ca_system_score_codex":0.0025278414,"about_ca_system_score_gemma":0.0041659554,"threshold_uncertainty_score":0.17793989},"labels":[],"label_agreement":null},{"id":"W3153250219","doi":"10.1108/s1521-613620210000026009","title":"Hate Speech, Media, and Canadian Federal Law","year":2021,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Notice; Originality; Political science; Phenomenon; Law; Value (mathematics); Sociology; Computer science; Epistemology","score_opus":0.013558634862281328,"score_gpt":0.19727979958823072,"score_spread":0.1837211647259494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153250219","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08895555,0.012017466,0.001681407,0.1191748,0.0008998353,0.00014310672,0.0022864614,0.00016787401,0.7746735],"genre_scores_gemma":[0.838438,0.0054454,0.0016118953,0.024347221,0.00019137676,0.000077311925,0.00065737823,0.000067054745,0.12916437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930983,0.0005717284,0.00013671753,0.00054433895,0.0032768364,0.002372035],"domain_scores_gemma":[0.98713815,0.0035949594,0.00048604928,0.0004277843,0.006690881,0.0016623104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003601765,0.0003418651,0.00030580952,0.004573141,0.027174817,0.012853215,0.0020579502,0.0034762134,0.02099667],"category_scores_gemma":[0.012979241,0.00046010673,0.00052145857,0.004648868,0.010374122,0.002765617,0.0030873765,0.004343743,0.0005751761],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039045895,0.00003391205,0.008544882,0.00018638078,0.000017666089,0.00030474714,0.01177023,0.00039922542,0.00031755125,0.80102473,0.14369845,0.033663213],"study_design_scores_gemma":[0.000020453126,0.000024111992,0.0332789,0.0007555125,0.000051529703,0.00011293184,0.018196724,0.0010204298,0.0008752087,0.023588942,0.9219287,0.00014652089],"about_ca_topic_score_codex":0.9952514,"about_ca_topic_score_gemma":0.9970831,"teacher_disagreement_score":0.21066207,"about_ca_system_score_codex":0.21066207,"about_ca_system_score_gemma":0.25946966,"threshold_uncertainty_score":0.91552013},"labels":[],"label_agreement":null},{"id":"W3153628376","doi":"10.18653/v1/2021.eacl-main.179","title":"“Are you kidding me?”: Detecting Unpalatable Questions on Reddit","year":2021,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Harm; Publication; Context (archaeology); Task (project management); Set (abstract data type); Field (mathematics); Social media; Focus (optics); Internet privacy; Data science; Human–computer interaction; World Wide Web; Psychology; Social psychology","score_opus":0.018673344873688717,"score_gpt":0.24705534417852862,"score_spread":0.2283819993048399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153628376","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861255,0.00077749323,0.0061338595,0.0007825009,0.0000626007,0.000083335115,0.001485894,0.00031763554,0.0042311153],"genre_scores_gemma":[0.9869409,0.00017148095,0.008808335,0.00029144337,0.000054339205,0.00005626884,0.0023791236,0.00003446521,0.0012636154],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99718994,0.0016823582,0.00012477349,0.00039853787,0.00038613068,0.00021830367],"domain_scores_gemma":[0.98595214,0.009009349,0.0018521145,0.0012861599,0.0011170802,0.0007831613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002537384,0.00056673744,0.00042284714,0.0023314597,0.0010199128,0.0011851484,0.00082130294,0.0014891041,0.0013292491],"category_scores_gemma":[0.017015288,0.00022183098,0.00029084604,0.00095904263,0.0006962456,0.0031210482,0.0020593393,0.0013696287,0.0010213611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012917864,0.001350274,0.6856941,0.001018582,0.0002181703,0.0023980294,0.032886136,0.003925701,0.019445702,0.0038208864,0.022178221,0.22577241],"study_design_scores_gemma":[0.00009728463,0.0008291383,0.5834605,0.0005962004,0.00022819033,0.0068675834,0.06859292,0.22159423,0.035990223,0.0147351,0.06675075,0.00025790956],"about_ca_topic_score_codex":0.0025978473,"about_ca_topic_score_gemma":0.0051331567,"teacher_disagreement_score":0.0025978473,"about_ca_system_score_codex":0.00042301085,"about_ca_system_score_gemma":0.00044705713,"threshold_uncertainty_score":0.013419151},"labels":[],"label_agreement":null},{"id":"W3155638757","doi":"","title":"Zero-shot Cross-lingual Content Filtering: Offensive Language and Hate Speech Detection","year":2021,"lang":"en","type":"article","venue":"Queen Mary Research Online (Queen Mary University of London)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; European Commission; Javna Agencija za Raziskovalno Dejavnost RS; Institute for Catastrophic Loss Reduction","keywords":"Offensive; Computer science; Natural language processing; Speech recognition; Zero (linguistics); Classifier (UML); Artificial intelligence; Voice activity detection; Task (project management); Linguistics; Speech processing; Mathematics; Engineering","score_opus":0.05353052957588103,"score_gpt":0.30540083337836393,"score_spread":0.2518703038024829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155638757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45569888,0.0018181653,0.46667358,0.00053790526,0.0010229463,0.00043886414,0.005151157,0.044427734,0.024230754],"genre_scores_gemma":[0.7789714,0.00042905897,0.18498443,0.000551726,0.0002555771,0.00029712677,0.010478051,0.0010867736,0.02294587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989237,0.00012944893,0.00005457939,0.00040714644,0.00027152908,0.000213648],"domain_scores_gemma":[0.9987569,0.0002484039,0.00006933826,0.00029733856,0.00052075513,0.00010731785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010069712,0.000970025,0.0010489873,0.0016535479,0.0007807435,0.0011171274,0.0010375427,0.0012779916,0.003694078],"category_scores_gemma":[0.0022142816,0.00037600112,0.00092725107,0.0005943536,0.0004711346,0.0014174039,0.0022144164,0.000920115,0.0056775264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008898759,0.0007919057,0.018183023,0.000354794,0.00033285914,0.0010788956,0.0011499762,0.0030646105,0.18657556,0.0015896495,0.024494419,0.7614944],"study_design_scores_gemma":[0.00008863436,0.0009803117,0.07695441,0.0001630425,0.0004680751,0.005083459,0.0014569398,0.480652,0.38058463,0.0045785727,0.048622638,0.00036724677],"about_ca_topic_score_codex":0.0065103397,"about_ca_topic_score_gemma":0.010252042,"teacher_disagreement_score":0.0065103397,"about_ca_system_score_codex":0.00038182954,"about_ca_system_score_gemma":0.000730036,"threshold_uncertainty_score":0.012944877},"labels":[],"label_agreement":null},{"id":"W3156375715","doi":"10.1108/s1521-613620210000026008","title":"Freedom of Expression and Humor in Canada: The Case of <i>Jérémy Gabriel</i> v <i>Mike Ward</i>","year":2021,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dignity; Human rights; Tribunal; Appeal; Honor; Law; Value (mathematics); Political science; Sociology","score_opus":0.009365961885545292,"score_gpt":0.1851544987366391,"score_spread":0.17578853685109383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156375715","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27872512,0.013910927,0.0021420973,0.16562223,0.0015484933,0.00014161957,0.00015788502,0.000084258274,0.53766733],"genre_scores_gemma":[0.8495883,0.004418173,0.0012391455,0.018746316,0.0001243958,0.000040345523,0.000042656462,0.00006635252,0.12573442],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962205,0.0005306903,0.000055752796,0.00026298326,0.0014497499,0.0014802918],"domain_scores_gemma":[0.9976495,0.00072279264,0.000119649885,0.00005794151,0.0005834791,0.0008666979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019345077,0.00047924925,0.00047718515,0.001363352,0.046246704,0.006959948,0.0022445996,0.0057459134,0.006351067],"category_scores_gemma":[0.0044755936,0.00037760497,0.00037818067,0.0017876143,0.018906677,0.0016830908,0.003957416,0.009092879,0.0005081812],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004789997,0.00007529875,0.005102547,0.00016238523,0.000023984394,0.020376546,0.29955852,0.0008533968,0.0008813816,0.47056052,0.16000047,0.042357106],"study_design_scores_gemma":[0.000020452702,0.000032339725,0.007639137,0.0005625422,0.000031162148,0.0055509694,0.24322598,0.00108985,0.0008261795,0.019507712,0.7213565,0.00015715114],"about_ca_topic_score_codex":0.9794957,"about_ca_topic_score_gemma":0.9950288,"teacher_disagreement_score":0.102364704,"about_ca_system_score_codex":0.102364704,"about_ca_system_score_gemma":0.12110765,"threshold_uncertainty_score":0.7427114},"labels":[],"label_agreement":null},{"id":"W3157076142","doi":"10.18573/jcads.61","title":"A corpus analysis of online news comments using the Appraisal framework","year":2021,"lang":"en","type":"article","venue":"Journal of Corpora and Discourse Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Judgement; Affect (linguistics); Variety (cybernetics); Appraisal theory; Newspaper; Moderation; Globe; Constructive; Sentiment analysis; Psychology; Computer science; Linguistics; Natural language processing; Social psychology; Artificial intelligence; Sociology; Epistemology; Communication; Process (computing); Media studies","score_opus":0.05755865043613934,"score_gpt":0.38123123722350666,"score_spread":0.3236725867873673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157076142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87901294,0.0013231061,0.07899019,0.00079419493,0.0002840114,0.0022528009,0.019870512,0.0010187527,0.016453503],"genre_scores_gemma":[0.7827486,0.0008237325,0.17760661,0.0001480885,0.00022400351,0.0035768854,0.026218504,0.00039927952,0.008254272],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9953257,0.0022719535,0.00038242555,0.0006803113,0.0011561486,0.00018346337],"domain_scores_gemma":[0.9708307,0.019595776,0.0019726043,0.0014632893,0.0057583037,0.00037939995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032498147,0.0005312169,0.00042019848,0.0069673085,0.0018818743,0.0015557469,0.0004985877,0.0005070425,0.0025705209],"category_scores_gemma":[0.019380054,0.00024840742,0.00033948573,0.007906023,0.00087775674,0.0011520304,0.0010439607,0.00079817104,0.0009399098],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018019087,0.00075697224,0.088991135,0.0045378567,0.00017346785,0.0030467922,0.14484303,0.0027177513,0.1822074,0.009942372,0.03268848,0.5282929],"study_design_scores_gemma":[0.00017360787,0.0009969324,0.5370911,0.0011129723,0.0002734021,0.003439648,0.082876444,0.053981066,0.056917958,0.008483779,0.25422087,0.00043219674],"about_ca_topic_score_codex":0.0062636724,"about_ca_topic_score_gemma":0.010173613,"teacher_disagreement_score":0.0069673085,"about_ca_system_score_codex":0.00085808185,"about_ca_system_score_gemma":0.001131024,"threshold_uncertainty_score":0.01718682},"labels":[],"label_agreement":null},{"id":"W3157565532","doi":"10.1080/25785648.2021.1899510","title":"Fighting Hate with Speech Law: Media and German Visions of Democracy","year":2021,"lang":"en","type":"article","venue":"The Journal of Holocaust Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Law; German; Political science; Statute; Politics; Democracy; Public law; Sociology; Nazism; History","score_opus":0.04219601063420795,"score_gpt":0.3414923497936797,"score_spread":0.29929633915947174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157565532","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36896008,0.017052317,0.005925529,0.07506983,0.00079842284,0.000033917422,0.00015204141,0.000072723196,0.53193516],"genre_scores_gemma":[0.9902858,0.0017809509,0.00023658092,0.0020350835,0.00010179781,0.0000068359227,0.00001927428,0.000015078604,0.0055186157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981818,0.0008680855,0.000056790555,0.00019521412,0.00027828463,0.0004198804],"domain_scores_gemma":[0.9986627,0.0007682432,0.00022352826,0.000091013586,0.00009876162,0.00015566469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033081798,0.00036742768,0.000193522,0.0023834489,0.0053701326,0.01315699,0.00050733227,0.0027707468,0.003198285],"category_scores_gemma":[0.00309877,0.00026369625,0.00017086284,0.0019098115,0.030569881,0.009458499,0.0040043173,0.0027819327,0.00044690285],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018094026,0.000011382998,0.0008188138,0.000027486776,0.000003645894,0.00013577782,0.024094967,0.00013587522,0.00016788596,0.9641414,0.002645694,0.007798935],"study_design_scores_gemma":[0.0000452207,0.00009088603,0.012075765,0.0007307918,0.00003184928,0.0005575875,0.09619413,0.00119311,0.001267324,0.4285056,0.45922494,0.000082770275],"about_ca_topic_score_codex":0.009093838,"about_ca_topic_score_gemma":0.009347276,"teacher_disagreement_score":0.01315699,"about_ca_system_score_codex":0.007516441,"about_ca_system_score_gemma":0.0017381345,"threshold_uncertainty_score":0.054535866},"labels":[],"label_agreement":null},{"id":"W3158220826","doi":"10.1177/20597991211010416","title":"Big data and democratic speech: Predicting deliberative quality using machine learning techniques","year":2021,"lang":"en","type":"article","venue":"Methodological Innovations","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Newspaper; Quality (philosophy); Deliberative democracy; Computer science; Legislation; Democracy; Data science; Artificial intelligence; Machine learning; Political science; Law; Politics; Epistemology","score_opus":0.5102523518130553,"score_gpt":0.43858212598107876,"score_spread":0.07167022583197652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158220826","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81447923,0.0013112042,0.16595168,0.0053079342,0.00017576129,0.0004371564,0.0053961203,0.00049820525,0.006442793],"genre_scores_gemma":[0.9631516,0.00015653032,0.0332098,0.00011810572,0.00011324836,0.00021892757,0.002599113,0.0000312096,0.00040146447],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99101794,0.0059852717,0.0007121928,0.00084621867,0.0010691643,0.00036934303],"domain_scores_gemma":[0.89065367,0.08719129,0.009799161,0.0067567057,0.004250663,0.001348559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012297212,0.0009069869,0.0008099438,0.008383473,0.0013593723,0.0036838972,0.0013457182,0.0016159058,0.0014210776],"category_scores_gemma":[0.06362283,0.00045629413,0.0010251874,0.0061418223,0.0017557248,0.0035219388,0.002320994,0.0028825225,0.00062883855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036802833,0.00081915606,0.8084003,0.0003027122,0.0006757857,0.00024902495,0.0029057115,0.059678447,0.0010069355,0.0076872595,0.00499657,0.11291014],"study_design_scores_gemma":[0.00004381356,0.00013914032,0.2358526,0.00019200354,0.00009864419,0.00015319222,0.003926981,0.7087196,0.0019245821,0.0431022,0.005729296,0.00011792491],"about_ca_topic_score_codex":0.0084173065,"about_ca_topic_score_gemma":0.0119599085,"teacher_disagreement_score":0.012297212,"about_ca_system_score_codex":0.0016983342,"about_ca_system_score_gemma":0.0012675276,"threshold_uncertainty_score":0.06503463},"labels":[],"label_agreement":null},{"id":"W3158484785","doi":"10.2139/ssrn.3744883","title":"Understanding Human Rights on the Internet: An Exercise of Translation?","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Internet governance; The Internet; Human rights; Political science; Accountability; Civil society; Corporate governance; Metaphor; Public relations; Internet privacy; Sociology; Law and economics; Law; Business; Politics; Computer science; World Wide Web","score_opus":0.06807810526199809,"score_gpt":0.28095175386371174,"score_spread":0.21287364860171365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158484785","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05785602,0.042613562,0.100032404,0.39375687,0.007399163,0.0001846741,0.0012595204,0.00030481155,0.39659292],"genre_scores_gemma":[0.89740074,0.029082831,0.026277868,0.026969945,0.006160731,0.00025259014,0.0006129503,0.00063691055,0.012605353],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98676467,0.009266339,0.0007196944,0.0011591604,0.0016319647,0.00045813882],"domain_scores_gemma":[0.9085698,0.06940981,0.003140397,0.011827188,0.006303161,0.0007495671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019141298,0.000847374,0.0013322905,0.0040279916,0.003261318,0.013026577,0.0019668224,0.0050361087,0.021523967],"category_scores_gemma":[0.11300494,0.0005831487,0.0005061388,0.0035679177,0.02550654,0.037607927,0.0052203755,0.007527152,0.0036468548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000624299,0.000063056985,0.0023855572,0.001089896,0.00006144273,0.00032535224,0.053536598,0.0005060906,0.0007013679,0.80385286,0.0147445835,0.122670814],"study_design_scores_gemma":[0.000019703126,0.000037391423,0.0034501946,0.0039400654,0.00004973312,0.00043711776,0.034777224,0.0020890885,0.0009611764,0.80481625,0.14934789,0.00007415784],"about_ca_topic_score_codex":0.007004847,"about_ca_topic_score_gemma":0.0042348094,"teacher_disagreement_score":0.021523967,"about_ca_system_score_codex":0.003655441,"about_ca_system_score_gemma":0.0045810016,"threshold_uncertainty_score":0.101230085},"labels":[],"label_agreement":null},{"id":"W3162322285","doi":"10.32473/flairs.v34i1.128427","title":"Ensemble-based Semi-Supervised Learning for Hate Speech Detection","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Leverage (statistics); Computer science; Ensemble learning; Artificial intelligence; Labeled data; Voice activity detection; Machine learning; Supervised learning; Natural language processing; Speech recognition; Speech processing; Artificial neural network","score_opus":0.10150214186858707,"score_gpt":0.3376627144692195,"score_spread":0.23616057260063245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162322285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091057494,0.00071115326,0.9020253,0.00026427224,0.00012659574,0.00016930497,0.0004734782,0.0030076166,0.0021648013],"genre_scores_gemma":[0.6770008,0.00030198763,0.3164909,0.00022749051,0.0002093175,0.00030778084,0.0029478369,0.00017737866,0.0023364879],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969516,0.001472572,0.00018818727,0.0006118056,0.00059092575,0.00018502215],"domain_scores_gemma":[0.9885105,0.0057222457,0.00071107474,0.001970976,0.0027954902,0.00028973553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005103146,0.0011268315,0.0015815625,0.0018453248,0.0010220011,0.000974222,0.0015620206,0.0011105817,0.0009493916],"category_scores_gemma":[0.009548173,0.0003779428,0.0008465806,0.0011250688,0.00059796934,0.0020882257,0.0014717977,0.002173918,0.0012735819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043735933,0.00082444586,0.01798414,0.00017943807,0.00044508107,0.00015081407,0.00041693554,0.14992274,0.011784081,0.0028145595,0.009624645,0.80541575],"study_design_scores_gemma":[0.0000082508395,0.00007761239,0.0014007192,0.000013482962,0.00002806001,0.000051523362,0.00006405133,0.9891426,0.004939832,0.0031955603,0.0010604286,0.000017798222],"about_ca_topic_score_codex":0.0019473844,"about_ca_topic_score_gemma":0.004772728,"teacher_disagreement_score":0.005103146,"about_ca_system_score_codex":0.00052241527,"about_ca_system_score_gemma":0.00095076306,"threshold_uncertainty_score":0.026988328},"labels":[],"label_agreement":null},{"id":"W3165863879","doi":"10.18280/ria.350205","title":"Modified Fuzzy Approach to Automatic Classification of Cyber Hate Speech from the Online Social Networks (OSN’s)","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorization; Computer science; Conversation; Artificial intelligence; Class (philosophy); Machine learning; Fuzzy logic; Support vector machine; Social media; Process (computing); World Wide Web; Psychology; Communication","score_opus":0.05328971346494636,"score_gpt":0.2706646874873413,"score_spread":0.21737497402239492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165863879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16872382,0.00093779375,0.82198876,0.0004362204,0.0002250369,0.00027699946,0.00047355617,0.00078086305,0.00615698],"genre_scores_gemma":[0.78508085,0.00035486513,0.20893909,0.00011639313,0.00009654474,0.00016682726,0.00056369905,0.000029218048,0.0046525183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919266,0.00009905962,0.00008411525,0.00021590751,0.00030198475,0.00010628307],"domain_scores_gemma":[0.99938023,0.00018953328,0.00005486313,0.00002314897,0.0003231559,0.000028977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009344657,0.000563395,0.00071926427,0.0023128046,0.00077829283,0.0011232505,0.0008093336,0.0009342729,0.0019896992],"category_scores_gemma":[0.001590358,0.00024555647,0.0010147566,0.000963781,0.0004176867,0.0006150599,0.0003792383,0.0006788776,0.0004553645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011375363,0.00041217363,0.015754951,0.00042614935,0.0002607805,0.0008170757,0.00097208447,0.113338046,0.05251355,0.0056854608,0.0040909234,0.8045913],"study_design_scores_gemma":[0.000023035489,0.00016697233,0.008018704,0.00003691977,0.00006766163,0.00018975485,0.000315297,0.9760593,0.009783919,0.0029825296,0.002316653,0.00003923094],"about_ca_topic_score_codex":0.013811832,"about_ca_topic_score_gemma":0.011598898,"teacher_disagreement_score":0.013811832,"about_ca_system_score_codex":0.0011197136,"about_ca_system_score_gemma":0.0008706801,"threshold_uncertainty_score":0.02746284},"labels":[],"label_agreement":null},{"id":"W3167630016","doi":"","title":"Emotive Meaning in Political Argumentation","year":2019,"lang":"en","type":"article","venue":"PhilPapers (PhilPapers Foundation)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Fundação para a Ciência e a Tecnologia; Social Sciences and Humanities Research Council of Canada","keywords":"Emotive; Argumentation theory; Meaning (existential); Psychology; Politics; Epistemology; Social psychology; Psychotherapist; Philosophy; Law; Political science","score_opus":0.00740999717087671,"score_gpt":0.2303211555853988,"score_spread":0.2229111584145221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167630016","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39575082,0.01498038,0.04426382,0.0291578,0.0010862143,0.000092600785,0.00007741882,0.00011606736,0.5144749],"genre_scores_gemma":[0.9912815,0.0011500446,0.0023212584,0.0005906746,0.00017287907,0.0000391271,0.0000151668055,0.000026139745,0.004403105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936538,0.005011604,0.00015490549,0.00026470856,0.0006896402,0.00022534467],"domain_scores_gemma":[0.99415344,0.004204683,0.00073132105,0.00018471199,0.0005752371,0.00015061167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00355513,0.00036681697,0.00025507231,0.0016838297,0.00273658,0.0073550357,0.00056452566,0.0020351696,0.0020703657],"category_scores_gemma":[0.0124484105,0.00024475963,0.0002709422,0.0013269325,0.011562814,0.005283158,0.0032983955,0.0019311045,0.0003209411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001107955,0.000041559015,0.0017671679,0.00031550482,0.00002185287,0.0004705,0.11301926,0.0004316686,0.0026303444,0.840839,0.0028992624,0.03745298],"study_design_scores_gemma":[0.0000533594,0.00012824619,0.012282369,0.0007847537,0.00005409657,0.00077142136,0.07229183,0.0034717422,0.002293065,0.6986191,0.20915164,0.00009824465],"about_ca_topic_score_codex":0.0008165789,"about_ca_topic_score_gemma":0.0007620012,"teacher_disagreement_score":0.0073550357,"about_ca_system_score_codex":0.0025826993,"about_ca_system_score_gemma":0.0008806251,"threshold_uncertainty_score":0.01880157},"labels":[],"label_agreement":null},{"id":"W3168129243","doi":"10.6000/1929-4409.2021.10.97","title":"Hate Crime in the News: The Media’s Role in Agenda Setting","year":2021,"lang":"en","type":"article","venue":"International Journal of Criminology and Sociology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Legislation; Law enforcement; Criminology; Political science; Content analysis; Hate crime; Social media; Extant taxon; Enforcement; News media; Law; Sociology; Social science","score_opus":0.038583565118918556,"score_gpt":0.29824907080560487,"score_spread":0.25966550568668634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168129243","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6471343,0.013491538,0.010219401,0.07618423,0.0021005075,0.00067831273,0.0017687986,0.00015922757,0.24826369],"genre_scores_gemma":[0.974225,0.006363701,0.005029536,0.0066411905,0.0008002604,0.00048112846,0.00035923134,0.00015261219,0.005947351],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9867577,0.009492696,0.0006412322,0.00071528484,0.0015064052,0.0008866656],"domain_scores_gemma":[0.9236616,0.061767,0.0065130126,0.0020245714,0.004091623,0.0019420439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013866583,0.0005440045,0.0005367852,0.0045025586,0.0059826095,0.020248622,0.0013335852,0.0018211138,0.006647445],"category_scores_gemma":[0.046352,0.0005825732,0.0004555731,0.0047215656,0.008285699,0.0199087,0.0055910675,0.0035927473,0.0008096591],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018841894,0.00011112933,0.034166355,0.0022254132,0.000061173356,0.0016626748,0.8376525,0.00011372325,0.0009799496,0.04197637,0.011111302,0.06975103],"study_design_scores_gemma":[0.000024167153,0.00007866541,0.027767483,0.004512096,0.000084687774,0.00053321145,0.80440277,0.00039545968,0.00075985375,0.010077823,0.15128298,0.000080797996],"about_ca_topic_score_codex":0.007628556,"about_ca_topic_score_gemma":0.012016778,"teacher_disagreement_score":0.020248622,"about_ca_system_score_codex":0.0043138587,"about_ca_system_score_gemma":0.0044724373,"threshold_uncertainty_score":0.073334396},"labels":[],"label_agreement":null},{"id":"W3168420408","doi":"10.1109/access.2021.3088410","title":"Monitoring Cyber SentiHate Social Behavior During COVID-19 Pandemic in North America","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Pandemic; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Social media; Artificial intelligence; Data science; Big data; The Internet; Machine learning; Scale (ratio); Stability (learning theory); Internet privacy; Computer security; World Wide Web; Data mining","score_opus":0.05117454856832516,"score_gpt":0.33477491154380495,"score_spread":0.2836003629754798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168420408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923173,0.00016018019,0.0030115834,0.000388949,0.000050057555,0.000059201655,0.0011863384,0.00016567405,0.002660779],"genre_scores_gemma":[0.9943615,0.0001195458,0.0029625664,0.00008764527,0.0000261911,0.00004727229,0.0015929935,0.000008830291,0.0007933961],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997819,0.00006170859,0.000011194819,0.00005382801,0.000051583607,0.000039709146],"domain_scores_gemma":[0.9994473,0.00014956633,0.00009980793,0.000038604026,0.00018832114,0.00007638474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032381475,0.00031982045,0.00027220754,0.00065645354,0.0004730298,0.0003645282,0.0003433417,0.00037545216,0.0005091106],"category_scores_gemma":[0.00091774313,0.000098546356,0.00013571554,0.00049319887,0.0002542783,0.0005414666,0.00042326393,0.0004066353,0.00017577708],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032186735,0.00059326674,0.8041523,0.00030028017,0.00013561432,0.0016459402,0.0039685816,0.025787277,0.015125987,0.0012860134,0.02159865,0.12508413],"study_design_scores_gemma":[0.000011018524,0.00019241046,0.71361285,0.00006243003,0.000037486443,0.00026250078,0.006922772,0.26809287,0.0030056785,0.0009647103,0.006785919,0.00004944286],"about_ca_topic_score_codex":0.02502724,"about_ca_topic_score_gemma":0.056160636,"teacher_disagreement_score":0.02502724,"about_ca_system_score_codex":0.0005757342,"about_ca_system_score_gemma":0.00034308928,"threshold_uncertainty_score":0.049763143},"labels":[],"label_agreement":null},{"id":"W3168548811","doi":"10.29173/irie416","title":"Ethical and technical challenges of AI in tackling hate speech","year":2021,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Moderation; Process (computing); Computer science; Pipeline (software); Scale (ratio); Data science; World Wide Web; Machine learning","score_opus":0.02863628928553276,"score_gpt":0.32653914000579953,"score_spread":0.2979028507202668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168548811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03348161,0.0094369445,0.77304506,0.13000557,0.0015972002,0.0010230931,0.00020275083,0.0006924668,0.050515324],"genre_scores_gemma":[0.56636137,0.0053776363,0.40512717,0.013042379,0.0014802002,0.002246237,0.00026041613,0.00037248677,0.0057320734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.74314743,0.20616709,0.008093211,0.0084214555,0.0317632,0.002407585],"domain_scores_gemma":[0.37136042,0.51670307,0.020636052,0.04296164,0.044867314,0.003471543],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20181496,0.0012490787,0.001314898,0.00526613,0.0056067975,0.016368357,0.004663336,0.007040136,0.0027347773],"category_scores_gemma":[0.33300236,0.0010024351,0.0009874828,0.0030729426,0.022624286,0.019100916,0.008746298,0.010756072,0.0015145484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019487801,0.0004434606,0.02619235,0.00270622,0.00024811606,0.00092553464,0.03828342,0.008117932,0.006294073,0.4998343,0.017082771,0.3996769],"study_design_scores_gemma":[0.00006177039,0.00029484372,0.0073421076,0.0030954657,0.00010463221,0.0019834554,0.025081286,0.043477446,0.009453106,0.7812832,0.12753958,0.0002830583],"about_ca_topic_score_codex":0.0027522433,"about_ca_topic_score_gemma":0.0025506152,"teacher_disagreement_score":0.20181496,"about_ca_system_score_codex":0.0040603112,"about_ca_system_score_gemma":0.00792794,"threshold_uncertainty_score":0.98430425},"labels":[],"label_agreement":null},{"id":"W3168741991","doi":"10.2139/ssrn.3821753","title":"Review Helpfulness Score vs. Review Unhelpfulness Score: Two Sides of the Same Coin or Different Coins?","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Helpfulness; Standard score; Psychology; Mathematics; Statistics; Social psychology","score_opus":0.021317097041572943,"score_gpt":0.26454203308912044,"score_spread":0.2432249360475475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168741991","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91301525,0.043228924,0.004304995,0.011046799,0.001955843,0.0006493189,0.0090538645,0.00024839988,0.016496532],"genre_scores_gemma":[0.99277484,0.0016246092,0.0013987067,0.0009009216,0.0009802427,0.00018362881,0.0009446373,0.00004792849,0.0011444843],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.990774,0.0026549008,0.0022982946,0.0012370725,0.002589784,0.00044597065],"domain_scores_gemma":[0.89290315,0.06347481,0.026396189,0.0022694657,0.010172572,0.004783739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0156076,0.00048666808,0.0017456316,0.005942762,0.0005632763,0.0021661557,0.0006802477,0.0018891601,0.0053703953],"category_scores_gemma":[0.12500751,0.00027157372,0.0018585878,0.0037997712,0.00091905444,0.003508818,0.0012565395,0.0011807925,0.0017286572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0052814684,0.00016295238,0.8839483,0.0042456547,0.006229206,0.00057439104,0.0009890718,0.0002619961,0.0032361846,0.00087864976,0.018456519,0.07573554],"study_design_scores_gemma":[0.0006158618,0.0013058519,0.96322167,0.0011422498,0.0078107193,0.0035760675,0.0019863953,0.0022182253,0.0024060912,0.0033969576,0.012149883,0.00017008884],"about_ca_topic_score_codex":0.00069847534,"about_ca_topic_score_gemma":0.0027078616,"teacher_disagreement_score":0.0156076,"about_ca_system_score_codex":0.00051941175,"about_ca_system_score_gemma":0.00077254296,"threshold_uncertainty_score":0.08254182},"labels":[],"label_agreement":null},{"id":"W3168793415","doi":"10.1017/9781108348423.002","title":"Freedom of Expression and the Regulation of Hate Speech","year":2018,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Freedom of expression; Expression (computer science); Political science; Computer science; Psychology; Communication; Law; Human rights","score_opus":0.012946887282446244,"score_gpt":0.18168142326705566,"score_spread":0.1687345359846094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168793415","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05579376,0.07402603,0.016017862,0.008105929,0.00080564356,0.000041749485,0.00022706152,0.00012013644,0.8448619],"genre_scores_gemma":[0.8016944,0.029085523,0.0025138555,0.0010197212,0.00054631656,0.00007881481,0.00013944629,0.00006861617,0.16485329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992047,0.00031970214,0.000019428924,0.00009353059,0.0002608156,0.00010180495],"domain_scores_gemma":[0.998367,0.001018762,0.00020612162,0.00017345118,0.00011705867,0.000117559975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011465078,0.00027946502,0.0002546932,0.0006531604,0.0008773563,0.003828935,0.00040956415,0.0008697564,0.009039515],"category_scores_gemma":[0.0027375536,0.00017351122,0.0002730898,0.0005796979,0.004827515,0.0018091135,0.0011921279,0.0014087564,0.0017381725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017509484,0.000090458416,0.0041408488,0.00044025542,0.00003836875,0.00042987583,0.017138302,0.0008703716,0.007037002,0.63825315,0.028078288,0.30330792],"study_design_scores_gemma":[0.000032071614,0.0001755238,0.059612174,0.0016207659,0.000051226692,0.0014962748,0.010611613,0.001976054,0.009184646,0.390814,0.5243096,0.000116027986],"about_ca_topic_score_codex":0.0012879279,"about_ca_topic_score_gemma":0.0014745508,"teacher_disagreement_score":0.009039515,"about_ca_system_score_codex":0.0011317973,"about_ca_system_score_gemma":0.000634659,"threshold_uncertainty_score":0.030240238},"labels":[],"label_agreement":null},{"id":"W3170678379","doi":"10.33137/cjal-rcbu.v7.36630","title":"Speak Freely: Why Universities Must Defend Free Speech, by Keith E. Whittington","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Academic Librarianship","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Free speech; Epistemology; Philosophy; Linguistics; Communication; Computer science; Sociology; Political science; Law","score_opus":0.0149353015557152,"score_gpt":0.1979454485916457,"score_spread":0.1830101470359305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170678379","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078623014,0.03295281,0.00052336947,0.934894,0.019708395,0.000017264565,0.000024311019,0.00007149789,0.0110220155],"genre_scores_gemma":[0.04630772,0.05892142,0.0018914596,0.72048414,0.02093959,0.0001865232,0.00007001048,0.00034620587,0.15085293],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98919386,0.004906217,0.00053452846,0.0010320038,0.0034719033,0.00086151477],"domain_scores_gemma":[0.9576465,0.026284553,0.0014105926,0.0010421809,0.0062052114,0.007411043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017933344,0.0009626064,0.00073819817,0.0013856186,0.012770358,0.013693341,0.0022173526,0.0121382065,0.011262176],"category_scores_gemma":[0.066441685,0.0007672701,0.00040356905,0.0013595452,0.012020442,0.01587809,0.0051950356,0.018818496,0.0070481673],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015960644,0.000016432808,0.00028860907,0.000058462854,0.000004219152,0.00007123133,0.0038343563,0.00002068043,0.00007046823,0.006922141,0.9597986,0.028898938],"study_design_scores_gemma":[0.0000107667865,0.000020175794,0.0012867467,0.0004968369,0.00000618773,0.00013975224,0.01228207,0.00010738377,0.00011827534,0.0071957815,0.9782974,0.00003861107],"about_ca_topic_score_codex":0.032970186,"about_ca_topic_score_gemma":0.054001313,"teacher_disagreement_score":0.032970186,"about_ca_system_score_codex":0.004904882,"about_ca_system_score_gemma":0.01059114,"threshold_uncertainty_score":0.09484172},"labels":[],"label_agreement":null},{"id":"W3171122582","doi":"","title":"Terms of Silence: Weaknesses in Corporate and Law Enforcement Responses to Cyberviolence against Girls","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Redress; Silence; Deference; Enforcement; Political science; Social media; Law enforcement; Identity (music); Order (exchange); Public relations; Law; Sociology; Business","score_opus":0.015360506104702486,"score_gpt":0.25527149164168705,"score_spread":0.23991098553698456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171122582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79010844,0.0013150682,0.0089285895,0.05335447,0.0005155183,0.00015188978,0.00007986354,0.00021284803,0.14533333],"genre_scores_gemma":[0.9853953,0.00034611643,0.00057373673,0.004136096,0.0000856822,0.00006892924,0.000024351612,0.00008064819,0.009289307],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9827527,0.009416815,0.00078824104,0.0016140395,0.0038877008,0.0015405321],"domain_scores_gemma":[0.95490444,0.031223662,0.0045145904,0.0029701272,0.0043941704,0.0019929225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014777609,0.0003576047,0.00044969874,0.001688376,0.011174468,0.008043796,0.002537565,0.0036328493,0.0047191866],"category_scores_gemma":[0.057345066,0.0004779024,0.0003685661,0.0012804997,0.013279791,0.0056795324,0.0066676866,0.0050744778,0.0010298564],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005682358,0.00009927567,0.031092871,0.00017782273,0.00003159435,0.0012923907,0.81608313,0.000085386215,0.002077057,0.033665385,0.007510266,0.10782804],"study_design_scores_gemma":[0.000014438178,0.00015499651,0.028181909,0.00062049436,0.000043821245,0.0017842193,0.8551587,0.00058104435,0.0036825119,0.0064223288,0.103285246,0.00007035945],"about_ca_topic_score_codex":0.024122564,"about_ca_topic_score_gemma":0.030756148,"teacher_disagreement_score":0.024122564,"about_ca_system_score_codex":0.0043968325,"about_ca_system_score_gemma":0.0048270384,"threshold_uncertainty_score":0.07815242},"labels":[],"label_agreement":null},{"id":"W3171916522","doi":"10.18601/01210483.v41n111.03","title":"“Gromming”: el minotauro en internet. El derecho penal del enemigo frente al pederasta de la era digital","year":2021,"lang":"es","type":"article","venue":"Derecho Penal y Criminología","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Humanities; Persona; Art; Political science; Philosophy","score_opus":0.01604378904084651,"score_gpt":0.2832740299046378,"score_spread":0.26723024086379127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171916522","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4704325,0.015195517,0.006180263,0.020734113,0.0014065334,0.00027805616,0.00031797495,0.0002321689,0.48522288],"genre_scores_gemma":[0.8793189,0.012127108,0.003728038,0.0090673575,0.00061173615,0.00017569518,0.00022378127,0.0001262322,0.094621226],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99804866,0.00091603585,0.000059750306,0.0001875577,0.00055599766,0.00023202953],"domain_scores_gemma":[0.99819773,0.000668627,0.00042094034,0.00013118831,0.00025159167,0.0003300157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012370516,0.00044936917,0.00020026631,0.0013190315,0.00503939,0.006554797,0.000996049,0.0021396372,0.012624366],"category_scores_gemma":[0.0047536073,0.00032052127,0.00031466887,0.001476549,0.0053331866,0.009283881,0.0039981985,0.0020305049,0.001877327],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020522944,0.00041781864,0.047655724,0.0015424765,0.00005953634,0.0030431543,0.40481958,0.00016967872,0.003408004,0.1526193,0.069591485,0.31646797],"study_design_scores_gemma":[0.000016113823,0.00021532523,0.04010567,0.0013762992,0.00005367485,0.003046982,0.26068234,0.0002685809,0.0009904582,0.007604188,0.6855672,0.0000732031],"about_ca_topic_score_codex":0.007964624,"about_ca_topic_score_gemma":0.018119602,"teacher_disagreement_score":0.012624366,"about_ca_system_score_codex":0.0019059443,"about_ca_system_score_gemma":0.0020453823,"threshold_uncertainty_score":0.042232692},"labels":[],"label_agreement":null},{"id":"W317289987","doi":"","title":"The cartoon controversy : a note on freedom of expression, hate speech and blasphemy","year":2006,"lang":"en","type":"article","venue":"Comparative and International Law Journal of Southern Africa","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Blasphemy; Dignity; Freedom of religion; Freedom of expression; Islam; Expression (computer science); Law; Denial; Human rights; Political science; Multiculturalism; Freedom of the press; Sociology; Philosophy; Psychology; Computer science; Theology; Psychoanalysis","score_opus":0.017245770251611343,"score_gpt":0.2480359473883886,"score_spread":0.23079017713677727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W317289987","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27498555,0.04413407,0.022166181,0.16496779,0.00731408,0.00008344135,0.00007848119,0.0001308909,0.48613954],"genre_scores_gemma":[0.9662308,0.0040050657,0.0013907372,0.008987233,0.001576922,0.000030606276,0.000014757367,0.000030316616,0.017733684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.995414,0.002502629,0.000103733546,0.0003574699,0.0011862757,0.0004358695],"domain_scores_gemma":[0.9862031,0.011556449,0.00093556725,0.00043006157,0.00062082737,0.00025398255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003928012,0.00037002677,0.00032999192,0.0012662973,0.0064581977,0.010890703,0.00067961623,0.0045158463,0.0032118994],"category_scores_gemma":[0.013807904,0.00025056297,0.00024848204,0.0013566609,0.020244218,0.005790765,0.002814206,0.0042753518,0.000325672],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058689384,0.000023523813,0.0017326948,0.00013555118,0.0000079916,0.00077577226,0.09646309,0.00013890988,0.000541032,0.83442616,0.02061165,0.045085013],"study_design_scores_gemma":[0.00002486207,0.00009218951,0.009399686,0.0009319837,0.00003352926,0.0017624587,0.09699054,0.0016200284,0.0023513339,0.16686657,0.71983284,0.000094000345],"about_ca_topic_score_codex":0.00907863,"about_ca_topic_score_gemma":0.013198033,"teacher_disagreement_score":0.010890703,"about_ca_system_score_codex":0.0022213187,"about_ca_system_score_gemma":0.0019649456,"threshold_uncertainty_score":0.02077353},"labels":[],"label_agreement":null},{"id":"W3173790829","doi":"","title":"La lutte contre les contenus haineux sur les plateformes de médias sociaux : une analyse comparative d’approches de régulation","year":2021,"lang":"fr","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Art","score_opus":0.09736543075075113,"score_gpt":0.309653797795422,"score_spread":0.21228836704467088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173790829","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2192828,0.01435716,0.017189223,0.021592727,0.00042373032,0.00028474888,0.00031365422,0.00015491543,0.72640103],"genre_scores_gemma":[0.92278993,0.008465221,0.0046312036,0.0022910198,0.00020450447,0.00047448336,0.00018691902,0.00011632849,0.060840458],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9815606,0.007265491,0.0005976142,0.0016496038,0.007463687,0.0014631067],"domain_scores_gemma":[0.9616994,0.02404304,0.0041656336,0.0022559182,0.0069596833,0.0008762131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01249394,0.0005998116,0.00039964833,0.0045992043,0.005369399,0.01444088,0.0019379836,0.0027945612,0.011493783],"category_scores_gemma":[0.023824269,0.00058005657,0.0008605287,0.0049052727,0.011823318,0.004726489,0.0046422426,0.0029530749,0.0014047681],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015864948,0.00011235694,0.025754597,0.0009446449,0.00011212209,0.0002651687,0.122362755,0.0014171175,0.0029988994,0.6742366,0.007065517,0.16457161],"study_design_scores_gemma":[0.00005304699,0.00026121165,0.10495654,0.003675467,0.0003053574,0.0003315388,0.12479664,0.0020077429,0.005929067,0.046282467,0.7112446,0.00015636749],"about_ca_topic_score_codex":0.036813162,"about_ca_topic_score_gemma":0.037502162,"teacher_disagreement_score":0.036813162,"about_ca_system_score_codex":0.016472956,"about_ca_system_score_gemma":0.013122074,"threshold_uncertainty_score":0.11952025},"labels":[],"label_agreement":null},{"id":"W3175155664","doi":"10.1177/00033286211025955","title":"Are rights really so wrong? A response to Nigel Biggar’s <i>What’s Wrong with Rights</i>","year":2021,"lang":"en","type":"article","venue":"Anglican Theological Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Supreme court; Law; Virtue; Duty; Bill of rights; Reading (process); Fundamental rights; Human rights; Sociology; Fundamentalism; Political science; Philosophy; Politics","score_opus":0.015078770853505593,"score_gpt":0.25545159716697124,"score_spread":0.24037282631346565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175155664","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024621957,0.014796935,0.00014671516,0.9751721,0.007839943,0.0000047355857,0.0000153963,0.000008387892,0.0017694966],"genre_scores_gemma":[0.010232399,0.01571752,0.00038944045,0.9556807,0.012347615,0.00003101948,0.00002138831,0.000047633494,0.005532292],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9865022,0.007087445,0.00053533464,0.0016753102,0.0032692754,0.00093044736],"domain_scores_gemma":[0.9401499,0.04787904,0.0018039627,0.00073175196,0.0068583046,0.0025770864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01498183,0.0009017019,0.0015428212,0.0026645306,0.009934674,0.009645365,0.003381094,0.025936114,0.004375397],"category_scores_gemma":[0.043340646,0.00068500446,0.0006700398,0.0025977374,0.021080697,0.01367118,0.00403971,0.04491317,0.0014506488],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000102708855,0.000012926944,0.00019400803,0.00017619708,0.000009122657,0.00013728056,0.005187527,0.000031730982,0.000046062185,0.041924465,0.94169706,0.010573454],"study_design_scores_gemma":[0.000013925586,0.000022157476,0.0007682127,0.0017737692,0.000015534477,0.00029691673,0.012506165,0.00010896487,0.00016933172,0.03062361,0.95362914,0.00007222642],"about_ca_topic_score_codex":0.038819678,"about_ca_topic_score_gemma":0.06483894,"teacher_disagreement_score":0.038819678,"about_ca_system_score_codex":0.007961754,"about_ca_system_score_gemma":0.010434888,"threshold_uncertainty_score":0.079232454},"labels":[],"label_agreement":null},{"id":"W3175346340","doi":"10.15626/hn.20214604","title":"The #MeeToo Movement as an e-Discourse: Social and Legal Effects","year":2021,"lang":"en","type":"article","venue":"HumaNetten","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sexual misconduct; Social media; Legal consciousness; Personality psychology; Criminology; Social psychology; Social movement; Politics; Sexual assault; Consciousness; Sociology; Psychology; Gender studies; Media studies; Political science; Law; Poison control; Human factors and ergonomics; Personality","score_opus":0.009499825713052915,"score_gpt":0.2645117646392586,"score_spread":0.25501193892620566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175346340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6524113,0.0012479862,0.002009224,0.015806638,0.00025434842,0.00011142046,0.000073270894,0.000058057532,0.32802787],"genre_scores_gemma":[0.9950841,0.00020962357,0.00025746005,0.00072749495,0.000108306114,0.0000467547,0.000015032664,0.000016509488,0.0035347003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.98289526,0.012471738,0.0004216309,0.00082148396,0.0024436882,0.0009462096],"domain_scores_gemma":[0.9342152,0.05370635,0.0051946715,0.0023843553,0.002356761,0.002142564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010568011,0.00035161502,0.0003528311,0.0034202146,0.00967091,0.01352268,0.0009555111,0.0027176943,0.009378097],"category_scores_gemma":[0.041776173,0.0002800096,0.000339095,0.002200021,0.026441999,0.008390397,0.012509664,0.0030342762,0.00055227487],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030528518,0.0007052191,0.049505576,0.00040336634,0.00006137767,0.0014466997,0.33542752,0.0005035177,0.0020895582,0.5174225,0.0068249265,0.08530448],"study_design_scores_gemma":[0.00017312878,0.00072539767,0.07488183,0.0010610876,0.00008958816,0.0011757497,0.6505209,0.0016767284,0.0042277267,0.09168576,0.17365329,0.00012877585],"about_ca_topic_score_codex":0.004306134,"about_ca_topic_score_gemma":0.0051280744,"teacher_disagreement_score":0.01352268,"about_ca_system_score_codex":0.0032337906,"about_ca_system_score_gemma":0.002298242,"threshold_uncertainty_score":0.055889606},"labels":[],"label_agreement":null},{"id":"W3178576861","doi":"10.5539/ijel.v11n4p58","title":"Linguistic Harassment Against Arab LGBTs on Cyberspace","year":2021,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Harassment; Cyberspace; Resentment; Prejudice (legal term); Lesbian; Social psychology; Psychology; Sociology; Political science; Criminology; Gender studies; Law; Politics; Computer science","score_opus":0.011762976683374752,"score_gpt":0.26460893281446846,"score_spread":0.25284595613109373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178576861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99741995,0.00006073409,0.0001021745,0.00008486573,0.00000861478,0.000011944743,0.000089273846,0.0000040132545,0.0022184697],"genre_scores_gemma":[0.99753606,0.00020257987,0.00018268642,0.000077533026,0.000013210711,0.000025156172,0.00013083481,0.0000034113687,0.0018284025],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995944,0.00020614214,0.000024663053,0.000028623126,0.0000785184,0.000067634894],"domain_scores_gemma":[0.99900085,0.0002673398,0.0003821261,0.000066649474,0.00019051622,0.000092497634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004146253,0.0002119169,0.00013933296,0.0012276365,0.0016461358,0.0013079684,0.00018238956,0.00033876445,0.0030779233],"category_scores_gemma":[0.0021610907,0.000086246786,0.00010367305,0.00072324363,0.0007080201,0.0005251802,0.00096502516,0.0003369835,0.0010771491],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028299598,0.0001646135,0.56622076,0.00032639343,0.000028166349,0.0019162316,0.34511715,0.00008693687,0.012906516,0.0013824534,0.004394915,0.067172945],"study_design_scores_gemma":[0.000004482662,0.00015846235,0.41555133,0.00024603438,0.000022804013,0.001562031,0.56567174,0.0004839177,0.0034618047,0.00035212716,0.012457511,0.000027801449],"about_ca_topic_score_codex":0.005180612,"about_ca_topic_score_gemma":0.00971475,"teacher_disagreement_score":0.005180612,"about_ca_system_score_codex":0.00038622078,"about_ca_system_score_gemma":0.00027278197,"threshold_uncertainty_score":0.010300875},"labels":[],"label_agreement":null},{"id":"W3180118297","doi":"10.1093/arisup/akab004","title":"A Distortion or ‘Our’ Default?","year":2021,"lang":"en","type":"article","venue":"Aristotelian Society Supplementary Volume","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Democracy; Pessimism; Political science; Reading (process); Law; Law and economics; Sociology; Philosophy; Politics; Epistemology","score_opus":0.013044353563240506,"score_gpt":0.24801707979597576,"score_spread":0.23497272623273524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180118297","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01518982,0.010983302,0.02705858,0.37294996,0.00981593,0.000070381575,0.0016791597,0.0006058208,0.56164706],"genre_scores_gemma":[0.6335707,0.0055535315,0.008236234,0.13195546,0.006900706,0.00020881394,0.0011025714,0.0006079227,0.21186405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970885,0.0010630133,0.000094203126,0.0006858847,0.0007967108,0.00027164636],"domain_scores_gemma":[0.99618405,0.0018571325,0.0003797612,0.0005570763,0.00082865043,0.00019328756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028727697,0.00049588346,0.00045369964,0.0010680305,0.0035051117,0.007403165,0.0013511124,0.0039203633,0.033770744],"category_scores_gemma":[0.016028538,0.00026103025,0.00031932534,0.0009986089,0.01386093,0.009731935,0.002415266,0.0069931555,0.009462037],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032281485,0.000014998803,0.0012097235,0.000068213056,0.0000063546127,0.00011975144,0.007307865,0.00004979136,0.00013268631,0.8087982,0.14989012,0.032370046],"study_design_scores_gemma":[0.000012383398,0.000020980906,0.0030082983,0.00036071238,0.000010230165,0.00048522544,0.0077656913,0.000518267,0.0004863651,0.3930722,0.59422475,0.000034907185],"about_ca_topic_score_codex":0.0060986686,"about_ca_topic_score_gemma":0.0043111187,"teacher_disagreement_score":0.033770744,"about_ca_system_score_codex":0.0026183932,"about_ca_system_score_gemma":0.0010899538,"threshold_uncertainty_score":0.112974405},"labels":[],"label_agreement":null},{"id":"W3184836400","doi":"10.1108/jices-09-2020-0101","title":"Security and privacy of adolescents in social applications and networks: legislative aspects and legal practice of countering cyberbullying on example of developed and developing countries","year":2021,"lang":"en","type":"article","venue":"Journal of Information Communication and Ethics in Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislation; Developing country; Legislature; Originality; Cybercrime; State (computer science); Value (mathematics); Business; Public relations; Developed country; Political science; Internet privacy; Law; Economic growth; The Internet; Economics; Sociology; Computer science","score_opus":0.033471725200854896,"score_gpt":0.3151500351475365,"score_spread":0.2816783099466816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184836400","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98731273,0.0005040442,0.00061571714,0.0012834434,0.000018167848,0.000029401463,0.000038716807,0.000005300832,0.010192533],"genre_scores_gemma":[0.9986753,0.00031161532,0.00023526335,0.0001510731,0.0000034999453,0.000012440089,0.000010689485,0.0000029240903,0.00059721514],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971271,0.0018386493,0.0001713838,0.00017889585,0.0003503058,0.00033367524],"domain_scores_gemma":[0.99442345,0.0027424523,0.0014696392,0.00034025023,0.0005834434,0.0004407612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031286103,0.00012023836,0.00015680463,0.0008714354,0.0030000156,0.0028897694,0.00030603033,0.0005639408,0.0024080663],"category_scores_gemma":[0.0067165634,0.00017666022,0.00015926748,0.00088771404,0.0035481853,0.0018920952,0.0019884212,0.0012792691,0.00016583611],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088015,0.00020144643,0.31578425,0.00038537377,0.000022975899,0.0021465744,0.59284544,0.00018740931,0.0019275241,0.027053952,0.002266627,0.057090398],"study_design_scores_gemma":[0.000008069547,0.0001819891,0.1606068,0.00076373445,0.000030250689,0.0018346758,0.7748012,0.00043806853,0.0017186007,0.0018124322,0.05776811,0.000036092744],"about_ca_topic_score_codex":0.008075678,"about_ca_topic_score_gemma":0.011287925,"teacher_disagreement_score":0.008075678,"about_ca_system_score_codex":0.0013928727,"about_ca_system_score_gemma":0.0025248802,"threshold_uncertainty_score":0.016545832},"labels":[],"label_agreement":null},{"id":"W3188759137","doi":"10.18653/v1/2022.wassa-1.14","title":"Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Masking (illustration); Exploit; Set (abstract data type); Artificial intelligence; Shot (pellet); Meaning (existential); Natural language processing; Class (philosophy); Language model; Baseline (sea); Machine learning; Training set; Contrast (vision); Domain (mathematical analysis); Test set; Speech recognition; Psychology; Mathematics","score_opus":0.010628335239707944,"score_gpt":0.21890807235594187,"score_spread":0.20827973711623393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188759137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13117574,0.0013594306,0.84807223,0.0017722602,0.00041149947,0.0001950695,0.00065172283,0.00968725,0.006674848],"genre_scores_gemma":[0.74968475,0.00027574395,0.238276,0.0013239298,0.0002335373,0.00023631967,0.0021072077,0.0006327893,0.007229779],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985215,0.00059891626,0.00006996551,0.00043443666,0.00024801117,0.00012705341],"domain_scores_gemma":[0.99774796,0.0013245596,0.00013506532,0.00037430425,0.00030994546,0.00010812781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022479882,0.001778704,0.0010526062,0.0009929521,0.0006820403,0.001548424,0.0014464767,0.0020716141,0.002652184],"category_scores_gemma":[0.009672486,0.00049143814,0.0010955918,0.00054456794,0.0013615406,0.0033904212,0.0026667237,0.0030489203,0.0022195228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008903693,0.00061675074,0.005974106,0.00053761876,0.00025777498,0.00046813837,0.00107279,0.09863501,0.08290617,0.017810805,0.024295805,0.76653475],"study_design_scores_gemma":[0.00005666855,0.0002159323,0.0014080354,0.000050308743,0.00004962243,0.00018509978,0.00026696752,0.95356494,0.01445143,0.025774214,0.0039223596,0.00005454035],"about_ca_topic_score_codex":0.0025491077,"about_ca_topic_score_gemma":0.005578375,"teacher_disagreement_score":0.002652184,"about_ca_system_score_codex":0.00064613554,"about_ca_system_score_gemma":0.0012564348,"threshold_uncertainty_score":0.011888623},"labels":[],"label_agreement":null},{"id":"W3192799006","doi":"10.48550/arxiv.2108.03305","title":"Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Offensive; Transfer of learning; Computer science; Psychology; Linguistics; Artificial intelligence; Engineering; Philosophy","score_opus":0.016764716596043918,"score_gpt":0.16264363836496837,"score_spread":0.14587892176892445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192799006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31872976,0.0012035235,0.6597052,0.0010519717,0.00028869495,0.00014769656,0.00088200666,0.011120662,0.0068704863],"genre_scores_gemma":[0.89807415,0.00024181814,0.091947034,0.00022955747,0.000076484175,0.000090740024,0.0013597413,0.00015022134,0.007830272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995741,0.00010507933,0.000022288708,0.0001226114,0.000084037645,0.00009189381],"domain_scores_gemma":[0.9991259,0.00035443742,0.00008903051,0.00015080866,0.00022135054,0.000058648082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084912387,0.0012610969,0.00046765443,0.0008649098,0.00039590336,0.0007411364,0.0009681336,0.0010464375,0.002022293],"category_scores_gemma":[0.0024151637,0.00037728078,0.0005631582,0.0006019372,0.00045535472,0.0018614341,0.0013667685,0.0020434503,0.0015635183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048403346,0.0006024388,0.0069220862,0.00018410863,0.00017022411,0.00027769525,0.00026393402,0.122129366,0.035507564,0.0023497234,0.008440647,0.8226683],"study_design_scores_gemma":[0.000006666382,0.0000793535,0.0009842234,0.000012692164,0.000014864831,0.00003914685,0.000053105483,0.9832138,0.012876475,0.0020377722,0.0006703754,0.0000116266865],"about_ca_topic_score_codex":0.0042918255,"about_ca_topic_score_gemma":0.005241173,"teacher_disagreement_score":0.0042918255,"about_ca_system_score_codex":0.00078541925,"about_ca_system_score_gemma":0.0006245316,"threshold_uncertainty_score":0.008533716},"labels":[],"label_agreement":null},{"id":"W3193907701","doi":"10.1109/ro-man50785.2021.9515450","title":"Can Robots Be Bullied? A Crowdsourced Feasibility Study for Using Social Robots in Anti-Bullying Interventions","year":2021,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Waterloo","funders":"","keywords":"Robot; Applied psychology; Psychology; Context (archaeology); Psychological intervention; Perception; Human–robot interaction; Social robot; Social psychology; Computer science; Artificial intelligence; Mobile robot","score_opus":0.1434275318357561,"score_gpt":0.3664603690591795,"score_spread":0.2230328372234234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193907701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986568,0.00009389964,0.00342088,0.0011287887,0.000052171043,0.005650754,0.00015511064,0.0000382526,0.0028920535],"genre_scores_gemma":[0.9778047,0.00019295812,0.008241536,0.000738793,0.00005065445,0.011850271,0.00008636265,0.000013887911,0.001020913],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9877844,0.0088601755,0.00051462115,0.00077950925,0.0010824902,0.0009788413],"domain_scores_gemma":[0.9724619,0.017293649,0.002557431,0.0017426418,0.003051093,0.0028932663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018090706,0.00061758107,0.00052735646,0.001240471,0.0037617274,0.0017578279,0.0013521919,0.0023177797,0.0040569245],"category_scores_gemma":[0.029274462,0.00066389854,0.0008736315,0.00039972394,0.0023425138,0.0025460154,0.0025156923,0.0017434966,0.0007984587],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007010037,0.14321917,0.1676238,0.0057699024,0.0005353334,0.00688838,0.46078077,0.0023339419,0.019435748,0.005546287,0.0066831545,0.17417349],"study_design_scores_gemma":[0.0066894824,0.124902435,0.21907164,0.0021589713,0.0005909638,0.0017543518,0.5832852,0.0067132246,0.008743283,0.006355481,0.039245393,0.0004895264],"about_ca_topic_score_codex":0.004086864,"about_ca_topic_score_gemma":0.006471925,"teacher_disagreement_score":0.018090706,"about_ca_system_score_codex":0.0015043139,"about_ca_system_score_gemma":0.003832966,"threshold_uncertainty_score":0.09567398},"labels":[],"label_agreement":null},{"id":"W3194697967","doi":"","title":"Internet voting : in the Quebec context : a study","year":2020,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Voting; The Internet; Internet privacy; Political science; Computer science; Business; World Wide Web; History; Politics; Law","score_opus":0.03482241765155038,"score_gpt":0.23466195960918135,"score_spread":0.19983954195763098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194697967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98667544,0.00061035604,0.00018766156,0.0014898629,0.000029006096,0.00007676507,0.0006680972,0.000007012042,0.010255769],"genre_scores_gemma":[0.9902451,0.00042740614,0.00012149106,0.00048709963,0.000020036077,0.000028761291,0.00030776442,0.000010645019,0.008351714],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99859816,0.0003797876,0.0000361533,0.0001413348,0.00034761662,0.0004968736],"domain_scores_gemma":[0.9929529,0.001419061,0.0010670545,0.00026178488,0.0029136573,0.0013854813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013053854,0.00029688067,0.00047107917,0.0015202702,0.007491127,0.0033535664,0.0013822182,0.0014356184,0.0075316597],"category_scores_gemma":[0.0051568435,0.00026943034,0.00035472435,0.0046065515,0.0019052286,0.0014171137,0.0011420192,0.0023593698,0.0006736625],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027236805,0.0011406632,0.9009147,0.00010040963,0.000083216786,0.0007923464,0.045443557,0.00029362732,0.00052169035,0.0032776091,0.010322744,0.0368371],"study_design_scores_gemma":[0.000027735472,0.00011596125,0.9375349,0.00007473467,0.000040357954,0.00015375986,0.05040378,0.0008659594,0.00012711728,0.0001464324,0.010479299,0.00002984053],"about_ca_topic_score_codex":0.99180603,"about_ca_topic_score_gemma":0.9974917,"teacher_disagreement_score":0.034717385,"about_ca_system_score_codex":0.034717385,"about_ca_system_score_gemma":0.02355325,"threshold_uncertainty_score":0.2518934},"labels":[],"label_agreement":null},{"id":"W3200592652","doi":"10.5210/spir.v2021i0.12176","title":"NOT FAR ENOUGH: HOW WORKPLACE HARASSMENT POLICIES FAIL TO PROTECT SCHOLARS FROM ONLINE ABUSE","year":2021,"lang":"en","type":"article","venue":"AoIR Selected Papers of Internet Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Harassment; Public relations; Framing (construction); Institution; Work (physics); Visibility; Political science; Sociology; Business; Law and economics; Law; Engineering","score_opus":0.0366512202601975,"score_gpt":0.315474532367428,"score_spread":0.2788233121072305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200592652","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"incentives","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"incentives","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7127871,0.0060528475,0.007904191,0.121189535,0.0003947893,0.00023365924,0.00013264532,0.000116163406,0.15118915],"genre_scores_gemma":[0.9921617,0.0014158417,0.000675924,0.0035584331,0.000046422756,0.000041603205,0.000024288123,0.000041946267,0.002033927],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9641481,0.017294243,0.0008987545,0.0023477913,0.0059155673,0.009395528],"domain_scores_gemma":[0.9371893,0.0343862,0.010082302,0.0052259425,0.007802896,0.0053134304],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.029303197,0.00062581233,0.0009823481,0.0069980444,0.025104385,0.032398507,0.0050056083,0.0076662046,0.005083427],"category_scores_gemma":[0.09470795,0.0007719086,0.0009121873,0.007717245,0.038085423,0.01650364,0.011365658,0.007664061,0.00085043156],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073587624,0.00028084926,0.08728624,0.0002857537,0.000086960325,0.000835287,0.47910705,0.000995421,0.00023857359,0.35613322,0.010290538,0.06438656],"study_design_scores_gemma":[0.000029181945,0.00007715935,0.048903752,0.0016942105,0.0001059181,0.00032994227,0.7921289,0.0015372316,0.00046392047,0.101215854,0.05342829,0.00008557043],"about_ca_topic_score_codex":0.2800476,"about_ca_topic_score_gemma":0.2534809,"teacher_disagreement_score":0.9706968,"about_ca_system_score_codex":0.045125198,"about_ca_system_score_gemma":0.04859778,"threshold_uncertainty_score":0.55683506},"labels":[],"label_agreement":null},{"id":"W3200895625","doi":"10.22329/il.v41i3.6854","title":"Illocutionary Performance and Objective Assessment in the Speech Act of Arguing","year":2021,"lang":"en","type":"article","venue":"Informal Logic","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Argumentation theory; Speech act; Set (abstract data type); Action (physics); Epistemology; Linguistics; Position (finance); Psychology; Computer science; Philosophy; Business","score_opus":0.012676637355366014,"score_gpt":0.24691328910885463,"score_spread":0.23423665175348862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200895625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6043512,0.0006274897,0.20444126,0.0046283617,0.00014583938,0.00025427697,0.000104929924,0.00013764224,0.18530896],"genre_scores_gemma":[0.9887533,0.00005734844,0.009411855,0.00009076519,0.000041205727,0.00006734459,0.000024438745,0.000013863718,0.001539692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.96340567,0.021041155,0.0025751144,0.002282896,0.00935949,0.0013357613],"domain_scores_gemma":[0.90080297,0.061097328,0.019188924,0.008806014,0.007894375,0.002210468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024412876,0.000619621,0.0005125754,0.0033382964,0.0019363093,0.0062872735,0.0012073439,0.002945342,0.0028699846],"category_scores_gemma":[0.09461698,0.00035768567,0.00048319413,0.0010674029,0.020797458,0.009024381,0.004404142,0.0028803933,0.0003304711],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014825613,0.000127748,0.022071293,0.00014011252,0.000035923487,0.00044911966,0.021439735,0.0021768792,0.0021555955,0.9278448,0.0004088587,0.023001682],"study_design_scores_gemma":[0.00005487032,0.00038389934,0.0465729,0.00038668013,0.000059160702,0.0010394139,0.018931787,0.022602698,0.0069982745,0.8913301,0.011486164,0.00015412027],"about_ca_topic_score_codex":0.0010103562,"about_ca_topic_score_gemma":0.0010016661,"teacher_disagreement_score":0.024412876,"about_ca_system_score_codex":0.0019922024,"about_ca_system_score_gemma":0.0016972537,"threshold_uncertainty_score":0.12910914},"labels":[],"label_agreement":null},{"id":"W3201238034","doi":"10.5210/spir.v2021i0.12254","title":"DISCOURSES OF VICTIMHOOD AND IDENTITY POLITICS ON SOCIAL MEDIA: UNDERSTANDING AFFECTIVE POLARIZATION DURING THE US ELECTION","year":2021,"lang":"en","type":"article","venue":"AoIR Selected Papers of Internet Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Social media; Politics; Blame; Sociology; Social psychology; Grounded theory; Identity politics; Duty; Identity (music); Media studies; Qualitative research; Psychology; Political science; Social science; Law; Aesthetics","score_opus":0.029174531493799255,"score_gpt":0.3163301661909795,"score_spread":0.2871556346971802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201238034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97071093,0.00063756027,0.001688014,0.002981766,0.00010792893,0.00003986685,0.000067796005,0.000015313506,0.023750888],"genre_scores_gemma":[0.9969682,0.00039281504,0.0002957411,0.00031008775,0.00002796515,0.000040398867,0.000028527405,0.000024571156,0.0019117859],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99374604,0.004748661,0.00010893251,0.00024963572,0.00040352953,0.0007432028],"domain_scores_gemma":[0.9929917,0.005604001,0.00039738035,0.0002510308,0.0003659558,0.00038987983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004716195,0.0004411179,0.0004224799,0.0022257788,0.010792801,0.008134775,0.00080396753,0.0015239784,0.002415973],"category_scores_gemma":[0.009472496,0.00040048012,0.00025343217,0.0017366309,0.012739831,0.00683202,0.008152678,0.0028179514,0.00025153955],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020961123,0.000018656921,0.0024184675,0.000025429177,0.0000029361356,0.00020963974,0.9889186,0.000022310489,0.00047292133,0.0035644702,0.00052329904,0.0038022608],"study_design_scores_gemma":[0.0000017054391,0.000009536491,0.0031676367,0.000070423885,0.0000028870013,0.00009196471,0.98490673,0.00006340607,0.0002833042,0.0008160328,0.010577807,0.000008570962],"about_ca_topic_score_codex":0.019030664,"about_ca_topic_score_gemma":0.030537112,"teacher_disagreement_score":0.019030664,"about_ca_system_score_codex":0.0028817907,"about_ca_system_score_gemma":0.0018196062,"threshold_uncertainty_score":0.03783977},"labels":[],"label_agreement":null},{"id":"W3201648943","doi":"10.3233/faia210016","title":"Cyber Racism Detection Using Bidirectional Gated Recurrent Units and Word Embeddings1","year":2021,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Racism; Word (group theory); Unconscious mind; Word2vec; Aggression; Punitive damages; Insult; Psychology; Action (physics); Value (mathematics); Representation (politics); Social psychology; Ethnic group; Computer science; Computer security; Sociology; Embedding; Artificial intelligence; Linguistics; Political science; Law; Gender studies; Politics","score_opus":0.04690592237393865,"score_gpt":0.2716675547044273,"score_spread":0.22476163233048868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201648943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27745372,0.0025162483,0.70729756,0.0009311744,0.00054761075,0.00017143498,0.0015419425,0.004108338,0.005431904],"genre_scores_gemma":[0.85056674,0.0007715257,0.13529278,0.00025960032,0.00015298872,0.00015467906,0.003198248,0.0002103603,0.009393085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961394,0.00012016536,0.00002777212,0.00010898389,0.0000650319,0.00006410187],"domain_scores_gemma":[0.99933416,0.00036079562,0.00007171416,0.000068238725,0.00013884861,0.000026201034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043137136,0.0012589751,0.0005687585,0.00084948156,0.00024447535,0.0007761303,0.0008095383,0.0007274834,0.0019856791],"category_scores_gemma":[0.0016987215,0.00028671793,0.0008553532,0.0007617754,0.00034380914,0.001013419,0.00074441516,0.0010218151,0.0011139256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004846953,0.00034866878,0.007146561,0.00035793905,0.00026278847,0.00057577225,0.00035877532,0.12821737,0.038138106,0.0042240014,0.011070573,0.80881476],"study_design_scores_gemma":[0.000004909031,0.000071557195,0.0018094631,0.000022041073,0.000025295312,0.00006846129,0.00004244174,0.98846275,0.006296691,0.0020303328,0.0011517135,0.000014290503],"about_ca_topic_score_codex":0.0044394266,"about_ca_topic_score_gemma":0.005955847,"teacher_disagreement_score":0.0044394266,"about_ca_system_score_codex":0.0004537286,"about_ca_system_score_gemma":0.00035651712,"threshold_uncertainty_score":0.00882715},"labels":[],"label_agreement":null},{"id":"W3202703754","doi":"10.32996/ijls.2021.1.2.2","title":"Analyzing Canadian PM Justin Trudeau’s Speech about Terrorist Attack on a Muslim Family in Ontario’s London: A Critical Perspective","year":2021,"lang":"en","type":"article","venue":"International Journal of Linguistics Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Islamophobia; Ideology; Critical discourse analysis; Hatred; Power (physics); Sociology; Politics; Media studies; Terrorism; Gender studies; Political science; Law","score_opus":0.05769391337723892,"score_gpt":0.3577178000372025,"score_spread":0.3000238866599636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202703754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85710144,0.0036517968,0.000990322,0.029440777,0.0006584531,0.0001835163,0.0010572848,0.000049287995,0.10686712],"genre_scores_gemma":[0.952542,0.0020721033,0.0005381297,0.003091771,0.000106329506,0.00007720041,0.0002597394,0.000111223315,0.041201606],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99739325,0.0007523489,0.00007050244,0.00031063028,0.000827512,0.00064566656],"domain_scores_gemma":[0.99193865,0.0034503934,0.00058484706,0.000205586,0.0026238973,0.0011965273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025216965,0.0005936366,0.00046263976,0.0022396073,0.025856273,0.0071480223,0.0014802078,0.002600229,0.0059251855],"category_scores_gemma":[0.008340088,0.00044572906,0.00024264543,0.0035929962,0.01192254,0.0016706715,0.003631122,0.003203823,0.0006490409],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000379617,0.000009472457,0.0028761716,0.00010139439,0.0000049523737,0.0019655365,0.9764638,0.000055733974,0.0011646007,0.003960058,0.009597091,0.0037631704],"study_design_scores_gemma":[0.000002118126,0.000009223491,0.0074988287,0.00017116635,0.0000051217926,0.00017527324,0.87425655,0.000053347354,0.00034128258,0.00015349429,0.11730808,0.000025497862],"about_ca_topic_score_codex":0.96535134,"about_ca_topic_score_gemma":0.9879189,"teacher_disagreement_score":0.1220557,"about_ca_system_score_codex":0.1220557,"about_ca_system_score_gemma":0.060759384,"threshold_uncertainty_score":0.88558024},"labels":[],"label_agreement":null},{"id":"W3203762216","doi":"10.1093/llc/fqab076","title":"A free market in extreme speech: Scientific racism and bloodsports on YouTube","year":2021,"lang":"en","type":"article","venue":"Digital Scholarship in the Humanities","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Arts and Humanities Research Council","keywords":"Radicalization; Sociology; Racism; Politics; Extreme right; Media studies; Political science; Law; Gender studies","score_opus":0.07123324672103878,"score_gpt":0.2304017534108769,"score_spread":0.15916850668983812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203762216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6594696,0.0033437645,0.0065192753,0.05225025,0.0006472689,0.000071245224,0.00044390743,0.0000842369,0.27717045],"genre_scores_gemma":[0.99267817,0.00036945194,0.0003715486,0.001733651,0.00014970989,0.000027662347,0.00005637852,0.000045725392,0.004567758],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9940579,0.004057806,0.00014996159,0.00051334297,0.00080377853,0.00041721915],"domain_scores_gemma":[0.98319477,0.011819202,0.0022687577,0.00083613634,0.0010824237,0.0007987235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005499373,0.00029959035,0.000271874,0.0021885869,0.008130171,0.011292849,0.0007773912,0.0029615487,0.008743082],"category_scores_gemma":[0.01886944,0.00028038226,0.00018873751,0.0017159579,0.016217913,0.011668074,0.0059719994,0.00316416,0.0008087499],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016506635,0.000056881992,0.018804593,0.00021026228,0.000026716762,0.001514271,0.66477454,0.0002072638,0.0013086316,0.26157647,0.0187648,0.032590512],"study_design_scores_gemma":[0.000033064778,0.00007558786,0.029685615,0.0009621914,0.000030517489,0.00088984164,0.6188855,0.001718609,0.0012862032,0.091434844,0.2548667,0.00013117981],"about_ca_topic_score_codex":0.009811262,"about_ca_topic_score_gemma":0.0129698245,"teacher_disagreement_score":0.011292849,"about_ca_system_score_codex":0.0044839764,"about_ca_system_score_gemma":0.0014675565,"threshold_uncertainty_score":0.032533705},"labels":[],"label_agreement":null},{"id":"W3203781812","doi":"10.5210/fm.v26i7.10891","title":"Understanding cancel culture: Normative and unequal sanctioning","year":2021,"lang":"en","type":"article","venue":"First Monday","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Normative; Phenomenon; Sanctions; Sociology; Social phenomenon; Law and economics; Epistemology; Political science; Social psychology; Psychology; Law; Social science; Philosophy","score_opus":0.05100119400643345,"score_gpt":0.2240802656838286,"score_spread":0.17307907167739517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203781812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8421117,0.0008913827,0.016365241,0.010720521,0.0001354443,0.00004819734,0.000052144474,0.000036684934,0.12963875],"genre_scores_gemma":[0.99814034,0.00013389482,0.0005089614,0.0002855061,0.000016880182,0.000010995064,0.000008139845,0.0000072300477,0.00088804873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99612576,0.002000493,0.00013631805,0.00037531843,0.00087172486,0.0004903822],"domain_scores_gemma":[0.9905082,0.004598967,0.0024260704,0.00070323097,0.0010227658,0.00074076076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039603566,0.0002773844,0.000255385,0.0012474486,0.004174149,0.005172295,0.00066533696,0.0011580724,0.002367563],"category_scores_gemma":[0.013146055,0.00020231145,0.00017448625,0.0005353719,0.016682755,0.0056897607,0.00464938,0.0020301205,0.00018319952],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013689435,0.00020046231,0.13687566,0.00018126657,0.00003508018,0.0008553775,0.36984107,0.0007061807,0.0028522229,0.4317583,0.0024350064,0.054122414],"study_design_scores_gemma":[0.000020021114,0.00013236463,0.09037205,0.00046347853,0.00004904437,0.0011668005,0.64416736,0.003954481,0.0030526854,0.21469048,0.041822013,0.00010930246],"about_ca_topic_score_codex":0.006989242,"about_ca_topic_score_gemma":0.00949731,"teacher_disagreement_score":0.006989242,"about_ca_system_score_codex":0.002700985,"about_ca_system_score_gemma":0.0020272946,"threshold_uncertainty_score":0.020944595},"labels":[],"label_agreement":null},{"id":"W3204600076","doi":"10.33972/jhs.188","title":"Making Hate Visible: Online Hate Incident Reporting Tools","year":2021,"lang":"en","type":"article","venue":"Journal of Hate Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Hate crime; Government (linguistics); Law enforcement; Political science; Social media; Equity (law); Economic Justice; Public relations; Criminology; Law; Sociology","score_opus":0.11142229285346363,"score_gpt":0.36446369556170155,"score_spread":0.25304140270823794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204600076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19046398,0.00198063,0.37730473,0.011410461,0.0025170466,0.0068199057,0.03381735,0.21297483,0.16271102],"genre_scores_gemma":[0.38953006,0.0019514983,0.53347343,0.0024281172,0.001243324,0.004582432,0.019897908,0.0053791236,0.041514166],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99375737,0.0027000422,0.0006399065,0.000656685,0.0018268382,0.00041909743],"domain_scores_gemma":[0.940922,0.034155987,0.0069802185,0.0078321295,0.0077111344,0.0023984732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00876962,0.0012076538,0.000522831,0.0066174306,0.0010465423,0.0046055,0.002082172,0.0012982616,0.012663012],"category_scores_gemma":[0.04140928,0.0006217246,0.0005367929,0.0027306862,0.0005140675,0.008473913,0.0047235135,0.0014432473,0.007065006],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006062271,0.0010603414,0.024067393,0.0012926457,0.0000958683,0.0008856456,0.0135849435,0.0010323585,0.005395416,0.007161234,0.19378346,0.75103444],"study_design_scores_gemma":[0.00030344038,0.0007491451,0.056803588,0.002461767,0.00024503825,0.0017023517,0.016548097,0.03501224,0.023249969,0.026016116,0.83591485,0.0009933793],"about_ca_topic_score_codex":0.0026385062,"about_ca_topic_score_gemma":0.0041577974,"teacher_disagreement_score":0.012663012,"about_ca_system_score_codex":0.0006946867,"about_ca_system_score_gemma":0.0015389462,"threshold_uncertainty_score":0.04637873},"labels":[],"label_agreement":null},{"id":"W3205918618","doi":"10.29173/mlj761","title":"When Friends Become Adversaries: Litigation in the Age of Facebook","year":2009,"lang":"en","type":"article","venue":"Manitoba Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Internet privacy; Political science; Psychology; Computer security; Advertising; Law and economics; Business; Sociology; Computer science","score_opus":0.015184651642178372,"score_gpt":0.22617309703577543,"score_spread":0.21098844539359707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205918618","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13177271,0.024701858,0.0010170973,0.5360967,0.0029909038,0.00010358989,0.00047072596,0.00011585707,0.30273065],"genre_scores_gemma":[0.7068503,0.010499804,0.00042227822,0.085292056,0.0009091985,0.000073191826,0.000115343304,0.000040251223,0.19579758],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777454,0.0003976231,0.00007891118,0.00019325712,0.0005669264,0.0009888439],"domain_scores_gemma":[0.9962739,0.0012863075,0.00040726966,0.00010247608,0.00059802196,0.0013321175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022800865,0.00033282314,0.00023404877,0.0009731812,0.03924493,0.010952896,0.0014347443,0.016899617,0.011890481],"category_scores_gemma":[0.009534348,0.0005931253,0.00028287506,0.0009381459,0.006478048,0.004414493,0.0043254173,0.009085265,0.0014787694],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038355454,0.00005374115,0.019639632,0.00009735191,0.0000260908,0.005031324,0.11143647,0.000077558514,0.00034238375,0.067020625,0.7465819,0.049654588],"study_design_scores_gemma":[0.000021235895,0.000044558285,0.039166052,0.0005964863,0.00006811856,0.0019428292,0.2224528,0.00022274531,0.00079122983,0.007398733,0.72721136,0.00008371779],"about_ca_topic_score_codex":0.56045276,"about_ca_topic_score_gemma":0.90831214,"teacher_disagreement_score":0.56045276,"about_ca_system_score_codex":0.017223526,"about_ca_system_score_gemma":0.023644606,"threshold_uncertainty_score":0.8842716},"labels":[],"label_agreement":null},{"id":"W3208025439","doi":"10.5815/ijitcs.2021.05.04","title":"Data Mining for Cyberbullying and Harassment Detection in Arabic Texts","year":2021,"lang":"en","type":"article","venue":"International Journal of Information Technology and Computer Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Arabic; Computer science; Globe; Scarcity; Harassment; Social media; Artificial intelligence; Natural language processing; World Wide Web; Internet privacy; Data science; Linguistics; Psychology; Social psychology","score_opus":0.01283397843629926,"score_gpt":0.26586329670706227,"score_spread":0.253029318270763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208025439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9007644,0.0024940574,0.067026295,0.0026189997,0.00041915782,0.00075310457,0.01621198,0.0022973223,0.0074145384],"genre_scores_gemma":[0.88449717,0.00081341946,0.0891349,0.00023051808,0.00012037984,0.00064833864,0.020268708,0.000048685368,0.0042379503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911445,0.00021089394,0.00016270157,0.000237865,0.00017877624,0.00009528015],"domain_scores_gemma":[0.99725205,0.001471651,0.00023852936,0.00021889352,0.0007019401,0.00011691333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010485346,0.0009282792,0.000573263,0.0031439506,0.0009707142,0.00095465896,0.00074084237,0.0011274301,0.0022538644],"category_scores_gemma":[0.005680041,0.00021760189,0.0008311135,0.0018470971,0.0003889993,0.0012820929,0.00080315664,0.0011479306,0.0022168283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010738824,0.0018583008,0.11406893,0.0015216302,0.00020553698,0.0021865019,0.001954948,0.028621325,0.039310053,0.001995625,0.024925696,0.78227764],"study_design_scores_gemma":[0.00009342703,0.0007287299,0.11209148,0.00041193797,0.00023858999,0.0016251024,0.0061449003,0.77252203,0.06303252,0.0058246152,0.037163664,0.00012300067],"about_ca_topic_score_codex":0.0044800276,"about_ca_topic_score_gemma":0.0057557016,"teacher_disagreement_score":0.0044800276,"about_ca_system_score_codex":0.0005467463,"about_ca_system_score_gemma":0.0008266297,"threshold_uncertainty_score":0.008907914},"labels":[],"label_agreement":null},{"id":"W3209074625","doi":"10.1080/08839514.2021.1988443","title":"Semi-Supervised Self-Training of Hate and Offensive Speech from Social Media","year":2021,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Offensive; Leverage (statistics); Social media; Machine learning; Support vector machine; Arabic; Training set; Supervised learning; Labeled data; Semi-supervised learning; Natural language processing; Speech recognition; Pattern recognition (psychology); World Wide Web","score_opus":0.045377666617984376,"score_gpt":0.24799838572498212,"score_spread":0.20262071910699775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209074625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75929606,0.0005947878,0.22350886,0.0004719724,0.0003188443,0.00048003305,0.0012760732,0.006179556,0.00787375],"genre_scores_gemma":[0.92294383,0.00008365404,0.06908265,0.00016799504,0.00008136199,0.00020074491,0.0030423105,0.000118697324,0.0042787963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867034,0.00045698453,0.00008465344,0.00042864901,0.00021213888,0.0001472083],"domain_scores_gemma":[0.9946695,0.0024509374,0.0004538132,0.00080078194,0.0013721463,0.0002528222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023747536,0.0012119377,0.0009404859,0.0010291076,0.0006407858,0.00080415304,0.001200205,0.0010502283,0.0013798947],"category_scores_gemma":[0.0056703947,0.0002773828,0.00066730747,0.0003953722,0.0006087312,0.0015326974,0.0011469536,0.0015286898,0.0018837875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010088313,0.002390378,0.045568835,0.00036598498,0.000277099,0.00021492403,0.00067901734,0.067971304,0.042032596,0.0010913974,0.013580264,0.8248194],"study_design_scores_gemma":[0.000025988107,0.00034477556,0.010608271,0.000031515025,0.000040392257,0.00012330973,0.0002729218,0.94985443,0.035630945,0.0011376678,0.001893297,0.000036492336],"about_ca_topic_score_codex":0.001948482,"about_ca_topic_score_gemma":0.0043270355,"teacher_disagreement_score":0.0023747536,"about_ca_system_score_codex":0.00046350865,"about_ca_system_score_gemma":0.0007691383,"threshold_uncertainty_score":0.012559056},"labels":[],"label_agreement":null},{"id":"W3211110162","doi":"10.4013/fem.2021.232.09","title":"Deplatforming, demotion and folk theories of Big Tech persecution","year":2021,"lang":"en","type":"article","venue":"Fronteiras - estudos midiáticos","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Moderation; Narrative; Demotion; Social psychology; History; Sociology; Psychology; Literature; Political science; Law; Art","score_opus":0.01195787799721924,"score_gpt":0.21687224228758176,"score_spread":0.20491436429036253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211110162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7632687,0.0024106416,0.015348719,0.012295233,0.00018789919,0.00016020086,0.00014882306,0.00004712139,0.20613256],"genre_scores_gemma":[0.996944,0.00027968458,0.0005729713,0.00025179525,0.000033151075,0.000039639504,0.000020794774,0.000015585025,0.0018423089],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99387413,0.0038555039,0.00016804131,0.00077679515,0.0007639771,0.0005615278],"domain_scores_gemma":[0.9698953,0.020509215,0.0048505743,0.0026549164,0.0011138634,0.00097607914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008739041,0.00051485305,0.0002600441,0.002974058,0.0049313265,0.0074160914,0.0008856357,0.001008947,0.007786767],"category_scores_gemma":[0.027276061,0.00030312943,0.00023368085,0.0019943917,0.025227612,0.0072556227,0.0062194937,0.0024572988,0.00040111606],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014315765,0.00012962826,0.08190792,0.0003542088,0.00005391038,0.00053066225,0.5091784,0.00042227443,0.00089192827,0.36660108,0.0017872272,0.03799958],"study_design_scores_gemma":[0.000037542446,0.00021020387,0.13250697,0.00077155035,0.000049259794,0.00057064625,0.59925485,0.00212681,0.001610585,0.18957804,0.07321795,0.00006555648],"about_ca_topic_score_codex":0.004261918,"about_ca_topic_score_gemma":0.006275811,"teacher_disagreement_score":0.008739041,"about_ca_system_score_codex":0.0038965659,"about_ca_system_score_gemma":0.0022672617,"threshold_uncertainty_score":0.046217024},"labels":[],"label_agreement":null},{"id":"W3211421893","doi":"10.1007/s10611-021-09978-7","title":"A continuum of hate: delimiting the field of hate studies","year":2021,"lang":"en","type":"article","venue":"Crime Law and Social Change","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"University of Limerick; Irish Research eLibrary","keywords":"Field (mathematics); Hate crime; Discipline; Scope (computer science); Sociology; Criminology; Epistemology; Interdisciplinarity; Social science; Philosophy; Computer science","score_opus":0.06512065924479371,"score_gpt":0.31419792912382455,"score_spread":0.24907726987903084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211421893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.448358,0.032641973,0.07204604,0.17164053,0.0010710994,0.0003021878,0.00015919926,0.00012452973,0.27365646],"genre_scores_gemma":[0.99237,0.0019681433,0.003292853,0.0014666227,0.0001772451,0.00007756732,0.00001585366,0.000015003734,0.0006166682],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98709875,0.009059033,0.0004973605,0.0009133397,0.0014956837,0.00093574205],"domain_scores_gemma":[0.9471213,0.040039033,0.0034035286,0.0033406727,0.003293956,0.0028015291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019021213,0.00037944314,0.0009775949,0.009352536,0.008818459,0.017613126,0.0021568062,0.005185301,0.0039569554],"category_scores_gemma":[0.029098084,0.000451979,0.00043845415,0.0044021937,0.09583381,0.02406869,0.014239872,0.0065572197,0.0002496906],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031033283,0.000043409542,0.003855343,0.000112658505,0.000004566439,0.00014119186,0.046489596,0.00011182007,0.00019117839,0.9359952,0.0006829003,0.012341163],"study_design_scores_gemma":[0.000017880946,0.00006910085,0.0077878824,0.0015856999,0.000010140089,0.00035508876,0.16077337,0.0010607151,0.00029320345,0.7986997,0.02931142,0.000035792753],"about_ca_topic_score_codex":0.0022286759,"about_ca_topic_score_gemma":0.0025764816,"teacher_disagreement_score":0.019021213,"about_ca_system_score_codex":0.0055931625,"about_ca_system_score_gemma":0.005219925,"threshold_uncertainty_score":0.100595},"labels":[],"label_agreement":null},{"id":"W3213398320","doi":"10.21810/strm.v13i1.307","title":"Humanitarian Communication Through the Lens of Feminist Ethics of Care","year":2021,"lang":"en","type":"article","venue":"Stream Interdisciplinary Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Information ethics; Engineering ethics; Applied ethics; Nursing ethics; Feminist ethics; Value (mathematics); Meta-ethics; Sociology; Military medical ethics; Field (mathematics); Humanitarian aid; Bioethics; Public relations; Political science; Law; Engineering; Computer science","score_opus":0.032759477624251306,"score_gpt":0.3289004887269881,"score_spread":0.29614101110273683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213398320","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01296354,0.0665464,0.05979545,0.44013348,0.004558086,0.00014626785,0.00015739993,0.00008447729,0.41561496],"genre_scores_gemma":[0.80757606,0.047827758,0.026933463,0.05589533,0.0064812778,0.0006274313,0.00009860131,0.00017716174,0.054382905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97941065,0.017548908,0.00030857412,0.00065835856,0.0013735865,0.00069986],"domain_scores_gemma":[0.9815832,0.015832726,0.0010340433,0.00039448167,0.0007257701,0.00042986125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012113759,0.0009181288,0.00058299914,0.0029040289,0.00779571,0.011965737,0.001625446,0.0074301944,0.0050940732],"category_scores_gemma":[0.012441138,0.00038155352,0.0005877785,0.0020768277,0.053537734,0.011998648,0.006897022,0.009964486,0.0007864337],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006767735,0.0000063997213,0.00011105544,0.00016685075,0.0000036931378,0.0002203556,0.04881037,0.00020809376,0.000065998516,0.9360391,0.007677645,0.006683708],"study_design_scores_gemma":[0.0000064592296,0.000018684412,0.00025271077,0.001676765,0.000004622511,0.00055184215,0.037905965,0.00033644293,0.00017918852,0.51472265,0.44432417,0.000020510208],"about_ca_topic_score_codex":0.004878183,"about_ca_topic_score_gemma":0.005392232,"teacher_disagreement_score":0.012113759,"about_ca_system_score_codex":0.010734281,"about_ca_system_score_gemma":0.0069713444,"threshold_uncertainty_score":0.077883005},"labels":[],"label_agreement":null},{"id":"W3215058976","doi":"10.22230/cjc.2021v46n4a4049","title":"Tweeting #RemoveKebab: A Critical Study of the Far Right’s Islamophobic Hate Hashtag on Twitter","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Islamophobia; Dehumanization; Far right; Social media; Genocide; Political science; Media studies; Sociology; Law","score_opus":0.02182102623204967,"score_gpt":0.2546424458983132,"score_spread":0.23282141966626352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215058976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96591747,0.00043244186,0.0011737872,0.009891233,0.00022014459,0.00007150028,0.00010811345,0.000021570952,0.022163685],"genre_scores_gemma":[0.9916045,0.0003419588,0.00037209762,0.0021601964,0.00007727411,0.000054326418,0.00004225913,0.000054597018,0.005292754],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9961671,0.0023940427,0.000095211144,0.0003963017,0.00050067756,0.00044665928],"domain_scores_gemma":[0.9781253,0.016773196,0.0018001174,0.00078635756,0.0015926984,0.00092238985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055551752,0.00059649535,0.00038717868,0.0022066578,0.016547782,0.0063716224,0.0009542756,0.0027050711,0.004284158],"category_scores_gemma":[0.015336914,0.0005571983,0.00019735636,0.001477039,0.014985152,0.006857218,0.005069429,0.0048116455,0.00074388605],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024695213,0.000013053237,0.0021946363,0.00003681868,0.0000021219678,0.00041403485,0.9912901,0.0000061283704,0.0010905614,0.0020718966,0.0009722683,0.0018836339],"study_design_scores_gemma":[0.0000018644264,0.000011151824,0.0027871002,0.000058052734,0.000002276262,0.00013368188,0.984355,0.000043995875,0.00037888056,0.000326229,0.011891616,0.000010158277],"about_ca_topic_score_codex":0.020921407,"about_ca_topic_score_gemma":0.05206679,"teacher_disagreement_score":0.020921407,"about_ca_system_score_codex":0.0044098683,"about_ca_system_score_gemma":0.0022067134,"threshold_uncertainty_score":0.041599274},"labels":[],"label_agreement":null},{"id":"W3215212169","doi":"10.22230/cjc.2021v46n4a4055","title":"Telegramming Hate: Far-Right Themes on Dark Social Media","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Theme (computing); Conservatism; Social media; White (mutation); Political science; Media studies; Advertising; Sociology; Internet privacy; Psychology; Computer science; Law; World Wide Web; Business","score_opus":0.017635328913974072,"score_gpt":0.22837634132950801,"score_spread":0.21074101241553395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215212169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9835481,0.000414178,0.0010913193,0.0010235237,0.00005679108,0.000030208606,0.000060715414,0.000013247136,0.013761923],"genre_scores_gemma":[0.9982602,0.00016727581,0.00017648345,0.00016624844,0.000028765078,0.000016320799,0.000018847031,0.00001036522,0.0011554817],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9977811,0.0011480495,0.000095972675,0.00024639632,0.0004391517,0.0002893942],"domain_scores_gemma":[0.9907098,0.0063492125,0.0013521193,0.0004712261,0.00060215755,0.00051547255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003501365,0.00037885795,0.00034256995,0.0021002886,0.0059954966,0.004465066,0.00038776657,0.00089889683,0.0042740605],"category_scores_gemma":[0.008498564,0.00023138706,0.00015732131,0.0014383785,0.007500401,0.0052947276,0.0056650983,0.0016599377,0.00023845144],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110050045,0.000029250516,0.014993964,0.00021206046,0.000010690842,0.00048693924,0.9595671,0.000025695572,0.0028021804,0.008034624,0.00083380827,0.012893577],"study_design_scores_gemma":[0.0000039001206,0.00004394542,0.020387702,0.0002473122,0.00001236167,0.0002933904,0.9590877,0.000110885565,0.00080715027,0.0011652597,0.017820502,0.000019807712],"about_ca_topic_score_codex":0.004487584,"about_ca_topic_score_gemma":0.006989623,"teacher_disagreement_score":0.0059954966,"about_ca_system_score_codex":0.001920173,"about_ca_system_score_gemma":0.0010451868,"threshold_uncertainty_score":0.018517256},"labels":[],"label_agreement":null},{"id":"W3215779345","doi":"10.32920/14645577.v1","title":"The Efficacy of Facebook as a Tool in Criminal Investigations","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Crowdsourcing; Context (archaeology); Criminal justice; Upload; Globe; Psychology; Media studies; Internet privacy; Sociology; Criminology; Computer science; World Wide Web; History","score_opus":0.02649786554716767,"score_gpt":0.2683834848162736,"score_spread":0.24188561926910593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215779345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8622102,0.0022334924,0.0016472996,0.010077119,0.00029177786,0.00031372593,0.000056442852,0.00007232313,0.12309759],"genre_scores_gemma":[0.9955265,0.00083664356,0.00072167907,0.0005776276,0.000090415946,0.00018759919,0.000016288914,0.000020442121,0.0020228105],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9174136,0.07032643,0.0013520226,0.0018393294,0.007744774,0.0013237925],"domain_scores_gemma":[0.71026856,0.24612747,0.02201283,0.009341209,0.008591999,0.0036579536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037649155,0.00043019318,0.00037704257,0.0033015967,0.008345946,0.010624089,0.0016091664,0.0019100879,0.0036007364],"category_scores_gemma":[0.1629636,0.0005437253,0.0003847464,0.0013595853,0.01261347,0.0073248693,0.0070665046,0.0024523127,0.00095576415],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033943405,0.0008307507,0.08941238,0.0009878479,0.00011526665,0.00061934517,0.6754365,0.00020667602,0.001000189,0.0292936,0.010271137,0.19148684],"study_design_scores_gemma":[0.00009323809,0.0014029203,0.15826198,0.0040893205,0.00020279836,0.0008758086,0.6815243,0.0019190905,0.002696816,0.009102515,0.13962317,0.00020806065],"about_ca_topic_score_codex":0.0035207658,"about_ca_topic_score_gemma":0.004374976,"teacher_disagreement_score":0.037649155,"about_ca_system_score_codex":0.0032477477,"about_ca_system_score_gemma":0.0027109215,"threshold_uncertainty_score":0.19911015},"labels":[],"label_agreement":null},{"id":"W3217762930","doi":"10.22230/cjc.2021v46n4a3807","title":"The Representation of Syrian Refugees in Canadian Online News Media: A Focus on the Topos of Burdening","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Topos theory; Representation (politics); Depiction; Refugee; Syrian refugees; News media; Focus (optics); Political science; Public discourse; Sociology; Media studies; Linguistics; Law; Art; Literature; Philosophy","score_opus":0.02119143668530193,"score_gpt":0.26456151987537097,"score_spread":0.24337008319006903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217762930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96498644,0.0010491513,0.00011910037,0.001207906,0.000050890205,0.000031094532,0.00063224207,0.000012656898,0.03191055],"genre_scores_gemma":[0.99545324,0.001312392,0.00011974338,0.00013914422,0.000024494004,0.000010233636,0.00022801972,0.000008602001,0.0027041174],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99802643,0.00026834666,0.00007404574,0.00010103551,0.0010288536,0.0005013029],"domain_scores_gemma":[0.99445325,0.0014473437,0.0015178376,0.00023215896,0.001688713,0.0006606395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015709163,0.00046510328,0.00031335047,0.00973467,0.008607365,0.0066978587,0.00059166044,0.000497405,0.0051722634],"category_scores_gemma":[0.0070244162,0.00018512845,0.00018644537,0.0096399,0.004481708,0.001397772,0.0033275732,0.0008421527,0.0003271798],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002615512,0.000037762155,0.26066655,0.00064441294,0.00006944688,0.0019975293,0.65896386,0.00010602643,0.004504544,0.0068224766,0.0077361083,0.058189772],"study_design_scores_gemma":[0.000004019571,0.000025729734,0.28566656,0.0003577796,0.000058233218,0.00059973286,0.66655684,0.00011401794,0.00094826246,0.00022148022,0.045398135,0.000049245788],"about_ca_topic_score_codex":0.84557015,"about_ca_topic_score_gemma":0.9318801,"teacher_disagreement_score":0.15442985,"about_ca_system_score_codex":0.012907939,"about_ca_system_score_gemma":0.014957529,"threshold_uncertainty_score":0.3106786},"labels":[],"label_agreement":null},{"id":"W323249253","doi":"","title":"Social Media, Free Speech and Parliamentary Service","year":2012,"lang":"en","type":"article","venue":"Canadian parliamentary review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Administration (probate law); Statement (logic); Service (business); Political science; Public relations; Ethical code; Social media; Code (set theory); Set (abstract data type); Public administration; Law; Business; Computer science; Marketing","score_opus":0.027373953490748726,"score_gpt":0.24682562253728338,"score_spread":0.21945166904653465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W323249253","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008499654,0.45161676,0.0013156276,0.12633343,0.0055544134,0.00025811154,0.0017706313,0.00010557929,0.40454578],"genre_scores_gemma":[0.33734736,0.46389467,0.0029374987,0.05229692,0.005499997,0.00034552664,0.0017035743,0.00014149571,0.13583295],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9720435,0.004557077,0.0009402881,0.0010630819,0.018659975,0.0027360904],"domain_scores_gemma":[0.9486092,0.015418273,0.0036220453,0.0013983885,0.027637228,0.0033148648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0136987,0.00043040834,0.0006321694,0.008313423,0.006045874,0.006197767,0.0016985622,0.0025453714,0.0091684135],"category_scores_gemma":[0.03192929,0.00040116388,0.00037513574,0.008827407,0.007355349,0.0027099962,0.0019684806,0.002723823,0.0011139626],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088233624,0.000057366768,0.003882872,0.0042573265,0.00007695478,0.0002633048,0.0032815456,0.0003023191,0.0005052487,0.21796967,0.35564575,0.41366938],"study_design_scores_gemma":[0.000008456425,0.000025536267,0.017538013,0.0018505048,0.000034128705,0.0000698496,0.0013436753,0.000048266254,0.0002415056,0.0026012638,0.97620744,0.00003142474],"about_ca_topic_score_codex":0.90539384,"about_ca_topic_score_gemma":0.9460808,"teacher_disagreement_score":0.96382517,"about_ca_system_score_codex":0.036174823,"about_ca_system_score_gemma":0.10908478,"threshold_uncertainty_score":0.26246792},"labels":[],"label_agreement":null},{"id":"W32961228","doi":"10.3233/978-1-60750-028-5-65","title":"The Impact of Privacy on Learners in the Context of a Web-Based Test","year":2009,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Test (biology); Context (archaeology); Computer science; Internet privacy; World Wide Web; Psychology; History; Geology","score_opus":0.02893076151581186,"score_gpt":0.28147474708855963,"score_spread":0.25254398557274776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W32961228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9079765,0.0017689768,0.009435439,0.0032314467,0.00010776976,0.000068869966,0.00010075688,0.00014077006,0.07716932],"genre_scores_gemma":[0.98780406,0.00073780835,0.0024812922,0.00032147916,0.00004272488,0.0000359807,0.00004582714,0.000027221271,0.008503586],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99685246,0.0020342798,0.000076107564,0.00014303127,0.00076509896,0.00012909863],"domain_scores_gemma":[0.98105925,0.016788576,0.0006832225,0.00061201595,0.0004767013,0.0003802753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028827211,0.0002161513,0.00021608536,0.00021606476,0.00077488576,0.002901215,0.0006998772,0.0008756104,0.005046398],"category_scores_gemma":[0.014648877,0.00010233087,0.00021113313,0.00025943512,0.0014877702,0.0019839166,0.0015105932,0.0013922971,0.0005770524],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018758338,0.0028690132,0.12033125,0.0009822707,0.00011110456,0.0033876302,0.08324017,0.0056073377,0.033378974,0.038413562,0.014128201,0.69567466],"study_design_scores_gemma":[0.00018735563,0.007804862,0.48715848,0.0015966942,0.0004716991,0.010033046,0.09424978,0.028169544,0.13248883,0.069570936,0.16792698,0.00034175374],"about_ca_topic_score_codex":0.00036387565,"about_ca_topic_score_gemma":0.0004924212,"teacher_disagreement_score":0.005046398,"about_ca_system_score_codex":0.00053489883,"about_ca_system_score_gemma":0.00040095285,"threshold_uncertainty_score":0.016881883},"labels":[],"label_agreement":null},{"id":"W349712876","doi":"","title":"The Regulation of Hate Speech Under the Canadian Human Rights Act","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Repeal; Human rights; Censorship; Political science; Tribunal; Commission; Law; Criminal code; Freedom of expression; Criminal law","score_opus":0.006081372817754707,"score_gpt":0.22288100276542366,"score_spread":0.21679962994766896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W349712876","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030208856,0.005699347,0.015529198,0.0711061,0.0020545179,0.0005403314,0.0013096133,0.0003343776,0.87321764],"genre_scores_gemma":[0.71115094,0.007577608,0.024592204,0.0483284,0.00081734813,0.0006075621,0.00095980824,0.00017655284,0.20578963],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9655664,0.0031100078,0.0009896529,0.0014517423,0.023470663,0.0054115704],"domain_scores_gemma":[0.96616834,0.008426836,0.0015693327,0.0025276963,0.019202612,0.0021051313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010295422,0.000630833,0.0005430127,0.0033207422,0.013023095,0.008525016,0.002763904,0.004796241,0.00602051],"category_scores_gemma":[0.028915513,0.00067548995,0.00073131826,0.002589431,0.01079132,0.0025344652,0.0030628094,0.006863021,0.0013393271],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035097615,0.00004891809,0.0041758628,0.00018489966,0.000017656166,0.00030251965,0.010382026,0.00069721567,0.0018348824,0.80870736,0.10967158,0.06394203],"study_design_scores_gemma":[0.00003350808,0.0000476438,0.030357381,0.0007260466,0.00008153137,0.00024110408,0.0047810846,0.0017771629,0.0027098875,0.04593167,0.9130089,0.00030410357],"about_ca_topic_score_codex":0.96432376,"about_ca_topic_score_gemma":0.97301143,"teacher_disagreement_score":0.048044838,"about_ca_system_score_codex":0.048044838,"about_ca_system_score_gemma":0.1576282,"threshold_uncertainty_score":0.34859133},"labels":[],"label_agreement":null},{"id":"W410245981","doi":"","title":"A Report on Canada's Conspiracy Law","year":2002,"lang":"en","type":"article","venue":"Loyola consumer law review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Law; Political science; Business","score_opus":0.020663012582655387,"score_gpt":0.23679014198534407,"score_spread":0.2161271294026887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W410245981","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060724735,0.021024538,0.002121462,0.32561964,0.012478712,0.0020527265,0.045087818,0.0013117217,0.5295786],"genre_scores_gemma":[0.103827655,0.012870072,0.0029899697,0.13564546,0.0011225359,0.00071368,0.010377494,0.00037517556,0.73207796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.980004,0.0004913327,0.0004566253,0.00063457666,0.014173181,0.004240225],"domain_scores_gemma":[0.9732463,0.0028924313,0.0005394734,0.0005103461,0.01931083,0.0035006732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005379779,0.00093973696,0.0009251843,0.0070739635,0.0133103235,0.011266482,0.0036926495,0.01892824,0.020853939],"category_scores_gemma":[0.014960839,0.0012529703,0.0019644098,0.006594115,0.0021600812,0.0019323564,0.002256256,0.008977883,0.0026874794],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005900811,0.000110569956,0.004453314,0.00017854509,0.0000437936,0.0003338962,0.00050227856,0.00037386667,0.0004952114,0.02102369,0.9575133,0.014912626],"study_design_scores_gemma":[0.00006521823,0.000050231767,0.036520083,0.00026979586,0.00010191709,0.000088382585,0.001437484,0.00044593163,0.00082099246,0.0014997897,0.9585793,0.0001208329],"about_ca_topic_score_codex":0.9933293,"about_ca_topic_score_gemma":0.99627906,"teacher_disagreement_score":0.07988232,"about_ca_system_score_codex":0.07988232,"about_ca_system_score_gemma":0.3723963,"threshold_uncertainty_score":0.5795895},"labels":[],"label_agreement":null},{"id":"W414380516","doi":"","title":"Hate speech : hate speech laws in Australia, hate speech laws in Canada, hate speech laws in India, allport's scale, harassment, hate crime, hate group, hate mail, anti-LGBT slogans, antisemitism, antilocution","year":2009,"lang":"en","type":"book","venue":"Alphascript Pub. eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Harassment; Hate crime; Law; Antisemitism; Political science; Philosophy; Theology; Judaism","score_opus":0.015789492344363477,"score_gpt":0.2375981545914986,"score_spread":0.22180866224713514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W414380516","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046872143,0.02464463,0.005530412,0.004944584,0.0024182745,0.00018302744,0.07298021,0.0077266167,0.876885],"genre_scores_gemma":[0.016975833,0.012962503,0.0040649846,0.0012999029,0.0005823419,0.00010899731,0.04936756,0.0021784145,0.91245943],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997002,0.000024544466,0.000018522453,0.000031807187,0.00019109574,0.000033831653],"domain_scores_gemma":[0.99852824,0.00046005083,0.00015399056,0.000056463286,0.00060092995,0.00020031074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029228764,0.0011489425,0.0004329052,0.00313338,0.0007814178,0.0017997734,0.0007593929,0.00092728826,0.08716523],"category_scores_gemma":[0.0018289941,0.00048120227,0.00026038222,0.0057094437,0.000409677,0.002189342,0.00060814695,0.0011680056,0.069753736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012774187,0.00001373363,0.00075422507,0.00017586777,0.0000027990711,0.000025215486,0.0002425566,0.000100611775,0.00014654544,0.0012170648,0.9062444,0.09106424],"study_design_scores_gemma":[0.000008501636,0.000027021028,0.039784525,0.00059874495,0.000009920687,0.00041794052,0.00059564755,0.00072328094,0.00053552154,0.0025210413,0.95474833,0.000029533958],"about_ca_topic_score_codex":0.071722075,"about_ca_topic_score_gemma":0.15599523,"teacher_disagreement_score":0.9282779,"about_ca_system_score_codex":0.00080088223,"about_ca_system_score_gemma":0.0020882832,"threshold_uncertainty_score":0.29159677},"labels":[],"label_agreement":null},{"id":"W4200012584","doi":"10.18280/ria.350603","title":"Hate Speech in the Arab Electronic Press and Social Networks","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arabic; Computer science; Social media; Presentation (obstetrics); Voice activity detection; Natural language processing; Artificial intelligence; Recall; Speech recognition; Linguistics; World Wide Web; Speech processing","score_opus":0.02316421436916392,"score_gpt":0.24773587273713013,"score_spread":0.22457165836796622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200012584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97197086,0.0016993772,0.006792409,0.00048296043,0.00013192339,0.00008729631,0.0019284824,0.0004023926,0.016504178],"genre_scores_gemma":[0.99033207,0.0007258394,0.003500393,0.000058528294,0.00010924029,0.000027671313,0.0012466897,0.000017162547,0.0039823954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987224,0.000515073,0.00009553186,0.00019059812,0.00039136343,0.000085109394],"domain_scores_gemma":[0.9951871,0.002622546,0.0010021905,0.0003036583,0.0007273999,0.00015709162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010031497,0.0004137316,0.0002454563,0.0047542294,0.00059907685,0.0018427164,0.00023735111,0.00058107893,0.0016724062],"category_scores_gemma":[0.0059631485,0.00014055434,0.00022123638,0.0019252672,0.0004675172,0.0016811882,0.0007388579,0.00041377006,0.0012774452],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067968434,0.00048836495,0.5335965,0.0008309527,0.00022092607,0.00222522,0.01064537,0.0044942927,0.021841329,0.0030068303,0.01113557,0.41083494],"study_design_scores_gemma":[0.000014351323,0.00035548382,0.800752,0.00034403338,0.00015689776,0.004343143,0.014657152,0.096617006,0.039172012,0.003185476,0.040270295,0.00013217409],"about_ca_topic_score_codex":0.002936536,"about_ca_topic_score_gemma":0.0029856113,"teacher_disagreement_score":0.0047542294,"about_ca_system_score_codex":0.0004180295,"about_ca_system_score_gemma":0.00020287106,"threshold_uncertainty_score":0.005838871},"labels":[],"label_agreement":null},{"id":"W4200153559","doi":"10.5539/ijel.v12n1p120","title":"Profanity and Play: Solidarity in the Discourse of Cyberbullying","year":2021,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Solidarity; Aggression; Phenomenon; Social psychology; Psychology; Online forum; Media studies; Sociology; Internet privacy; Political science; Law; Epistemology; Computer science","score_opus":0.013949089980279395,"score_gpt":0.2829329900549912,"score_spread":0.2689839000747118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200153559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9047118,0.0030182675,0.011523159,0.008949998,0.0002092509,0.000066650806,0.00004741921,0.000058479563,0.07141499],"genre_scores_gemma":[0.99779177,0.00028735533,0.0004084501,0.00017118023,0.000040612253,0.000015720496,0.000008881988,0.000016546868,0.0012594919],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98723537,0.010429564,0.000249598,0.00049814,0.0009991587,0.0005882042],"domain_scores_gemma":[0.9829092,0.013531243,0.0014785829,0.0006462697,0.0006851619,0.0007495874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060678837,0.00071812765,0.0005384573,0.0034024701,0.012159272,0.010070046,0.000935049,0.0026678627,0.0023326543],"category_scores_gemma":[0.012998816,0.0004280028,0.000308244,0.0017803488,0.034024067,0.009853945,0.008805489,0.003248087,0.0003029327],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031849926,0.000011371148,0.0013800855,0.000052740663,0.0000045012957,0.0002518493,0.97108984,0.000021733875,0.0007094119,0.023198448,0.00022248483,0.003025694],"study_design_scores_gemma":[0.000006539271,0.000052331503,0.004823138,0.0003000594,0.000013060898,0.00074297213,0.9546146,0.0003446187,0.0008699957,0.010205268,0.027997905,0.000029515208],"about_ca_topic_score_codex":0.003911467,"about_ca_topic_score_gemma":0.003541529,"teacher_disagreement_score":0.012159272,"about_ca_system_score_codex":0.0026790178,"about_ca_system_score_gemma":0.0014982946,"threshold_uncertainty_score":0.032090425},"labels":[],"label_agreement":null},{"id":"W4200325561","doi":"10.1109/istas52410.2021.9629201","title":"Protecting marginalized communities by mitigating discrimination in toxic language detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Generalizability theory; Computer science; Language identification; Language model; Classifier (UML); Artificial intelligence; Machine learning; Identification (biology); Natural language processing; Natural language; Psychology","score_opus":0.013600078452696042,"score_gpt":0.24121802012857338,"score_spread":0.22761794167587734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200325561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5548346,0.00053858804,0.4328517,0.002392182,0.00013701635,0.00032119793,0.00036104044,0.0019211276,0.00664252],"genre_scores_gemma":[0.9604148,0.00008294117,0.036864206,0.00045267612,0.000063271495,0.00008404043,0.00028216367,0.00006743095,0.0016884045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671197,0.0015695716,0.0001125982,0.0006643107,0.0005857598,0.0003557064],"domain_scores_gemma":[0.9872953,0.0059715644,0.0018359661,0.002290696,0.0018542148,0.0007523047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005816308,0.0012520822,0.0011405449,0.0013544771,0.0017109555,0.0017051258,0.0015284038,0.001632384,0.0012499737],"category_scores_gemma":[0.023723733,0.00043383593,0.00066238426,0.00060753745,0.0016042412,0.0043210513,0.0042857653,0.0021781393,0.00092640164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013708518,0.0020738605,0.13222492,0.0005947073,0.00023854047,0.00071925484,0.004494314,0.18093379,0.04330657,0.021539493,0.0147094745,0.5977942],"study_design_scores_gemma":[0.00003883819,0.00038748255,0.008526596,0.00006646767,0.000069361864,0.0002737988,0.0010075157,0.9453866,0.015985684,0.02440318,0.0037986513,0.000055779023],"about_ca_topic_score_codex":0.0046811467,"about_ca_topic_score_gemma":0.007254329,"teacher_disagreement_score":0.005816308,"about_ca_system_score_codex":0.0008183374,"about_ca_system_score_gemma":0.001956043,"threshold_uncertainty_score":0.03075993},"labels":[],"label_agreement":null},{"id":"W4200402279","doi":"10.3138/9781487529314-fm","title":"Frontmatter","year":2021,"lang":"en","type":"book-chapter","venue":"University of Toronto Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"ECW Press (Canada); University of Toronto","funders":"","keywords":"Computer science","score_opus":0.015104466559718243,"score_gpt":0.18470094606440343,"score_spread":0.16959647950468518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200402279","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017968223,0.0004123951,0.0004666622,0.00072347536,0.0008045552,0.000025493644,0.0006684905,0.0011835515,0.99553573],"genre_scores_gemma":[0.0004283367,0.00013175323,0.000093587725,0.00027007464,0.00006096901,0.000006612574,0.00020120281,0.00020181296,0.9986058],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964476,0.000027115519,0.000014163338,0.00008701378,0.00017364947,0.000053353077],"domain_scores_gemma":[0.9993599,0.000100919766,0.000029615825,0.000103002436,0.00023441153,0.00017211602],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00028105566,0.0008332542,0.000582985,0.0013589192,0.0016217884,0.005330668,0.0011176011,0.002668383,0.9249053],"category_scores_gemma":[0.0015549377,0.00046239814,0.00044218707,0.0010326476,0.00045066082,0.0035072446,0.0025020097,0.0016757167,0.89228475],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022620112,0.000023106963,0.00010675466,0.000090792484,0.0000015477009,0.000065219865,0.00008156387,0.000026698415,0.00031266196,0.0065403734,0.875927,0.11680177],"study_design_scores_gemma":[0.0000022770462,0.000004373386,0.00010165621,0.00002775283,5.978602e-7,0.000045086144,0.000031407348,0.000025268084,0.000067563844,0.0004902889,0.9992017,0.0000021046628],"about_ca_topic_score_codex":0.0021991576,"about_ca_topic_score_gemma":0.0066540446,"teacher_disagreement_score":0.0750947,"about_ca_system_score_codex":0.000886385,"about_ca_system_score_gemma":0.00067295926,"threshold_uncertainty_score":0.10711342},"labels":[],"label_agreement":null},{"id":"W4205332525","doi":"10.18778/1733-8077.10.2.03","title":"“The Machines Don’t Lie”: A Study of the Social Production of Mechanization in the Determination of Voter Intent","year":2014,"lang":"en","type":"article","venue":"Qualitative Sociology Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Voting; Production (economics); Consistency (knowledge bases); Action (physics); Computer science; Contingent vote; Sociology; Law; Political science; Economics; Artificial intelligence; Group voting ticket; Politics; Microeconomics","score_opus":0.038854846007855534,"score_gpt":0.3710089362909556,"score_spread":0.33215409028310006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205332525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94049215,0.006613844,0.0027105266,0.009889079,0.000080637175,0.0001495553,0.000052905096,0.000007386615,0.040003896],"genre_scores_gemma":[0.99419963,0.0022442832,0.00056778523,0.00050425297,0.000015739013,0.0000751873,0.000018896148,0.000010547283,0.0023637516],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98279047,0.014503964,0.00025323962,0.00062524393,0.001117366,0.0007096614],"domain_scores_gemma":[0.95465165,0.035702333,0.004965836,0.0010313515,0.003092531,0.0005563995],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.019664427,0.00028054163,0.00044431028,0.0028763807,0.0064626783,0.0033104757,0.0014449299,0.0011940798,0.002333812],"category_scores_gemma":[0.03062797,0.0005014605,0.00027022476,0.0030466034,0.023750396,0.0041321507,0.0037055449,0.0018032866,0.00025496606],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032685854,0.00005795505,0.014573171,0.00034714548,0.000019319777,0.0002886799,0.9225447,0.00005946281,0.00028231798,0.04152087,0.0011769877,0.01909661],"study_design_scores_gemma":[0.000011806297,0.00006345257,0.025265155,0.00044628116,0.000016225766,0.00011018483,0.9477247,0.00013611502,0.00027574084,0.004720869,0.021212587,0.000016863054],"about_ca_topic_score_codex":0.046812203,"about_ca_topic_score_gemma":0.086265,"teacher_disagreement_score":0.9935373,"about_ca_system_score_codex":0.010228597,"about_ca_system_score_gemma":0.008860917,"threshold_uncertainty_score":0.103996634},"labels":[],"label_agreement":null},{"id":"W4205675155","doi":"10.1109/bigdata52589.2021.9672023","title":"MetaHate: A Meta-Model for Hate Speech Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Benchmark (surveying); Computer science; Baseline (sea); Task (project management); Language model; F1 score; Artificial intelligence; Macro; Voice activity detection; Machine learning; Sentiment analysis; Natural language processing; Speech recognition; Speech processing","score_opus":0.44815077082744353,"score_gpt":0.36512516252513644,"score_spread":0.0830256083023071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205675155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06496102,0.0057606753,0.8800891,0.0022163014,0.00088550703,0.00039458086,0.011205034,0.0278318,0.006655937],"genre_scores_gemma":[0.6087816,0.0019304801,0.33643776,0.001982168,0.0007384315,0.0009848763,0.024425903,0.002481354,0.022237498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991259,0.0002519705,0.0000507207,0.0003458292,0.00013624091,0.0000893068],"domain_scores_gemma":[0.997282,0.0016918983,0.00011745676,0.00034272624,0.00044928532,0.000116639545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021029585,0.0026258868,0.0015825154,0.0028159423,0.0008196298,0.0019140216,0.0028240976,0.0019345704,0.0040429397],"category_scores_gemma":[0.0051653096,0.0010454576,0.002685844,0.0012553391,0.0005717812,0.0038422772,0.0017123513,0.0031534785,0.004198514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013679411,0.0009881494,0.022168253,0.00093018747,0.0023215192,0.0006863209,0.00057577924,0.3436225,0.018263206,0.0054730643,0.04830279,0.55530035],"study_design_scores_gemma":[0.000027823095,0.000114750306,0.0012189401,0.000051473322,0.00017298809,0.0001285455,0.0000589582,0.9819217,0.0044722375,0.0070477948,0.004729848,0.00005490113],"about_ca_topic_score_codex":0.009127414,"about_ca_topic_score_gemma":0.021356776,"teacher_disagreement_score":0.009127414,"about_ca_system_score_codex":0.00119997,"about_ca_system_score_gemma":0.0015052961,"threshold_uncertainty_score":0.018148541},"labels":[],"label_agreement":null},{"id":"W4206166506","doi":"10.1109/smc52423.2021.9659134","title":"Semi-Supervised Self-Learning for Arabic Hate Speech Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Arabic; Classifier (UML); Voice activity detection; Artificial intelligence; Speech recognition; Supervised learning; Natural language processing; Social media; Training set; Machine learning; Speech processing; Linguistics; World Wide Web; Artificial neural network","score_opus":0.03250911837304178,"score_gpt":0.26497872461590927,"score_spread":0.2324696062428675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206166506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16875342,0.0006782772,0.81491107,0.0004035235,0.00023417662,0.00040190335,0.0008841384,0.008476582,0.0052569467],"genre_scores_gemma":[0.7528174,0.00017881037,0.23451035,0.00037670528,0.00020184818,0.0004847215,0.0038121988,0.00035008168,0.007268005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977738,0.0008810786,0.00014271445,0.0006669455,0.00038328578,0.00015217684],"domain_scores_gemma":[0.990682,0.0045883995,0.0007357771,0.0013869689,0.002359435,0.0002473552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027926138,0.0012421245,0.0012099163,0.0012593757,0.0008838262,0.00089825084,0.0017690766,0.0012109431,0.0021873459],"category_scores_gemma":[0.008185772,0.0003867331,0.00072476227,0.00066093483,0.0009962223,0.0017260006,0.0014922076,0.0019063842,0.0033571927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007778465,0.0011571049,0.010295131,0.0003651255,0.00019197828,0.000257871,0.0005820925,0.053543486,0.04555986,0.0019032657,0.014296286,0.8710701],"study_design_scores_gemma":[0.000026784355,0.00019448949,0.0029709397,0.000023845752,0.000030014167,0.00016632809,0.00016840348,0.9606679,0.030014893,0.0027651666,0.0029289185,0.000042321673],"about_ca_topic_score_codex":0.0015544224,"about_ca_topic_score_gemma":0.0036965527,"teacher_disagreement_score":0.0027926138,"about_ca_system_score_codex":0.00051814393,"about_ca_system_score_gemma":0.000802157,"threshold_uncertainty_score":0.0147688985},"labels":[],"label_agreement":null},{"id":"W4206818289","doi":"10.13056/acamh.14503","title":"‘Social Media Use and Cyberbullying: an international analysis’ – Professor William Pickett","year":2021,"lang":"en","type":"report","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Queen (butterfly); Social media; Certificate; Media studies; Sociology; Political science; Library science; Computer science; Law; Biology; Ecology","score_opus":0.07485778760173695,"score_gpt":0.3239682364356507,"score_spread":0.24911044883391376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206818289","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014696041,0.17967702,0.0012720157,0.72339284,0.013272146,0.000065212254,0.00093258114,0.00006292879,0.06662914],"genre_scores_gemma":[0.19191131,0.4010932,0.003556377,0.065406725,0.037874978,0.00027083108,0.00189797,0.0006049898,0.29738364],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9981828,0.00065260235,0.00007033628,0.0002736835,0.0006013202,0.00021939242],"domain_scores_gemma":[0.99613523,0.0016952562,0.00035659262,0.00010807644,0.0010447807,0.00066014065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004570316,0.00082423823,0.0005878401,0.006192573,0.0044142082,0.005520658,0.0006492408,0.002090577,0.00840629],"category_scores_gemma":[0.005837024,0.0007537823,0.00037855326,0.009924557,0.0032245293,0.0060149943,0.002547812,0.0064060153,0.0016168247],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004724566,0.000040600487,0.005852076,0.0001365217,0.00002169282,0.000084905645,0.0044333576,0.000066525354,0.00011884254,0.020976735,0.9073656,0.060855817],"study_design_scores_gemma":[0.000007322666,0.00003941678,0.030329837,0.00073015684,0.000021847582,0.00019347758,0.0076038577,0.000113228445,0.00034544928,0.004339877,0.9562312,0.000044381763],"about_ca_topic_score_codex":0.07233006,"about_ca_topic_score_gemma":0.10922553,"teacher_disagreement_score":0.07233006,"about_ca_system_score_codex":0.0025479102,"about_ca_system_score_gemma":0.0043819556,"threshold_uncertainty_score":0.14381808},"labels":[],"label_agreement":null},{"id":"W4210347196","doi":"10.1109/icmla52953.2021.00156","title":"Graph Convolutional Networks for Categorizing Online Harassment on Twitter","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Categorization; Convolutional neural network; Computer science; Machine learning; Embedding; Artificial intelligence; Graph; Text categorization; Task (project management); Representation (politics); Theoretical computer science","score_opus":0.04054491041714028,"score_gpt":0.3149590693513241,"score_spread":0.27441415893418386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210347196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7623699,0.0028635089,0.21460345,0.0023889875,0.00031739878,0.00022884042,0.0056846775,0.0036601932,0.007883148],"genre_scores_gemma":[0.96451557,0.000416804,0.026707336,0.00019367663,0.00005404289,0.00006260822,0.0050496743,0.00004558056,0.0029546907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996704,0.0000756858,0.000018479133,0.0000976375,0.00005210274,0.00008570528],"domain_scores_gemma":[0.9994518,0.0002098841,0.00009474266,0.00007331661,0.00012969832,0.000040467854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005834514,0.0011819797,0.00039338687,0.0018550097,0.00047220086,0.0006118754,0.000757211,0.0008870355,0.0015642234],"category_scores_gemma":[0.0022950452,0.00021306971,0.00066287746,0.0015269768,0.000398037,0.0013933099,0.0006053218,0.0010099636,0.000566919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001161478,0.00066157413,0.050664943,0.00030409097,0.00040479877,0.0004071217,0.00034154343,0.41723537,0.010337607,0.009470085,0.02279928,0.48621213],"study_design_scores_gemma":[0.000008846763,0.000041203093,0.0047357134,0.000019192308,0.00003755268,0.00003512838,0.000078196295,0.9872617,0.0018730618,0.00493773,0.0009597935,0.000011738726],"about_ca_topic_score_codex":0.030142775,"about_ca_topic_score_gemma":0.034559675,"teacher_disagreement_score":0.030142775,"about_ca_system_score_codex":0.0016021141,"about_ca_system_score_gemma":0.0006939201,"threshold_uncertainty_score":0.059934676},"labels":[],"label_agreement":null},{"id":"W4210474564","doi":"10.2307/3505240","title":"10.2307/3505240","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hate crime; Political philosophy; Legislation; Political science; Philosophy of law; Law; Criminology; Politics; Law and economics; Sociology; Comparative law","score_opus":0.004017916171813146,"score_gpt":0.16207892609926497,"score_spread":0.1580610099274518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210474564","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035043526,0.00037885507,0.00056180084,0.0003188614,0.0004639459,0.00006341193,0.0012102464,0.001011995,0.9956404],"genre_scores_gemma":[0.0010064924,0.0002927274,0.00041634386,0.00037031923,0.00009790759,0.000029605357,0.0012503132,0.0003589719,0.9961773],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945635,0.000042652042,0.000042718882,0.00015333685,0.00021663902,0.000088232824],"domain_scores_gemma":[0.9978987,0.00043601193,0.0000822507,0.00036774424,0.0005382517,0.00067701313],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00082321273,0.0017363217,0.0011456689,0.002341294,0.0015812357,0.004619185,0.0018118384,0.0026776576,0.98487955],"category_scores_gemma":[0.0019654965,0.000788946,0.00076399604,0.0023165739,0.0015577229,0.0052422397,0.0038745422,0.0015920943,0.98637277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016999026,0.00014651912,0.00046330199,0.00036307325,0.00001485204,0.00020504487,0.00007510747,0.00016237858,0.001796464,0.0046674265,0.59494865,0.39698717],"study_design_scores_gemma":[0.000023140343,0.000034558674,0.00070151646,0.0001521452,0.000005706966,0.00021222048,0.00010946826,0.00012789595,0.00026067471,0.0009301084,0.9974275,0.00001491873],"about_ca_topic_score_codex":0.0037429107,"about_ca_topic_score_gemma":0.004001937,"teacher_disagreement_score":0.015120447,"about_ca_system_score_codex":0.0010136705,"about_ca_system_score_gemma":0.0011718716,"threshold_uncertainty_score":0.021567464},"labels":[],"label_agreement":null},{"id":"W4210529051","doi":"10.21810/jicw.v4i3.4160","title":"Dehumanization, Hate Speech, and Violence in Right-Wing Extremist Environments","year":2022,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dehumanization; Agency (philosophy); Presentation (obstetrics); Criminology; Political science; Sociology; Social psychology; Media studies; Psychology; Law; Social science","score_opus":0.015453145801558663,"score_gpt":0.23135111226329322,"score_spread":0.21589796646173456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210529051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98164314,0.0025745092,0.00019131693,0.00473855,0.00009602026,0.000023861841,0.00009138406,0.0000046911796,0.010636339],"genre_scores_gemma":[0.99593747,0.001689701,0.000098861194,0.0004880893,0.00008154575,0.0000247161,0.00006179187,0.0000032923479,0.0016144827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973858,0.0016376532,0.00007624079,0.00014890567,0.0002962453,0.00045513755],"domain_scores_gemma":[0.995027,0.0021413718,0.0013470295,0.00014067943,0.0004376805,0.0009061875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023106576,0.000312429,0.00031916253,0.0013562649,0.0025401553,0.0026907937,0.0002589098,0.0010094907,0.002507515],"category_scores_gemma":[0.0069728065,0.00029634053,0.00028824236,0.00074164016,0.001872781,0.0012607697,0.0025102333,0.0023560398,0.00022627942],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048141283,0.0006328695,0.7664996,0.00024831455,0.00017352929,0.0023975873,0.13234924,0.00023753935,0.00073215953,0.0025426853,0.010660233,0.08304484],"study_design_scores_gemma":[0.000006775277,0.00022963382,0.86298096,0.00021233,0.00002617325,0.0011347672,0.12714452,0.0002421871,0.00031386176,0.00093087956,0.006737584,0.000040257913],"about_ca_topic_score_codex":0.009544629,"about_ca_topic_score_gemma":0.018896984,"teacher_disagreement_score":0.009544629,"about_ca_system_score_codex":0.0012501593,"about_ca_system_score_gemma":0.00044582933,"threshold_uncertainty_score":0.018978179},"labels":[],"label_agreement":null},{"id":"W4210798472","doi":"10.1016/j.osnem.2021.100194","title":"Selecting and combining complementary feature representations and classifiers for hate speech detection","year":2022,"lang":"en","type":"article","venue":"Online Social Networks and Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Heuristics; Sarcasm; Artificial intelligence; Feature selection; Machine learning; Feature extraction; Task (project management); Selection (genetic algorithm); Speech recognition; Natural language processing","score_opus":0.0194614751732331,"score_gpt":0.2694540405599481,"score_spread":0.249992565386715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210798472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27046463,0.0024992712,0.71557033,0.0006948172,0.0005951515,0.0004167679,0.0009771311,0.0033650782,0.0054168133],"genre_scores_gemma":[0.7983771,0.00074882363,0.19385178,0.00028848628,0.00033820546,0.0002514768,0.0017887572,0.00013322172,0.004222097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829835,0.00030380665,0.000094077215,0.0004563473,0.00044425903,0.0004031084],"domain_scores_gemma":[0.9976271,0.0009842872,0.00012990627,0.0002097476,0.0009039066,0.00014510445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025120906,0.0018043298,0.0021969373,0.0040526073,0.0007458005,0.0019189479,0.0010746876,0.0018167581,0.0018114141],"category_scores_gemma":[0.00516578,0.00044055207,0.0016593459,0.0023332126,0.0004588403,0.0020716055,0.0016247767,0.0013674124,0.0016494358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076530053,0.0010384955,0.014872788,0.00016752254,0.00035708735,0.00022326337,0.00011294202,0.010455566,0.061022386,0.0009553825,0.005757495,0.90427166],"study_design_scores_gemma":[0.00008191239,0.0007598845,0.028583188,0.00007637579,0.00087368797,0.0004856442,0.00049516134,0.8985497,0.06075902,0.005040651,0.0041690324,0.0001257093],"about_ca_topic_score_codex":0.00339231,"about_ca_topic_score_gemma":0.004617471,"teacher_disagreement_score":0.0040526073,"about_ca_system_score_codex":0.0004878076,"about_ca_system_score_gemma":0.0010979851,"threshold_uncertainty_score":0.0132853985},"labels":[],"label_agreement":null},{"id":"W4212990424","doi":"10.22230/cjc.2022v47n1a4157","title":"Platform Multiverse: Discontent and Disconnection among Alt-Rights","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Disconnection; Moderation; Sociology; Social psychology; Political science; Psychology; Law","score_opus":0.013625749278327055,"score_gpt":0.20154309215838861,"score_spread":0.18791734288006157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212990424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9614587,0.00040457977,0.004516363,0.0019804568,0.000057616333,0.000032786087,0.000033599044,0.000032146432,0.0314836],"genre_scores_gemma":[0.9977884,0.00009342991,0.00042758018,0.000119472585,0.000016843973,0.000012616587,0.000014418464,0.000011386468,0.0015156982],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9955656,0.0021866458,0.00022292983,0.0006318879,0.0009283359,0.00046460572],"domain_scores_gemma":[0.9823721,0.009114972,0.003983073,0.0022512488,0.0011305527,0.0011481012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005847845,0.0003025214,0.00026747212,0.0020986262,0.006997156,0.005070327,0.00088821707,0.0011250933,0.0065468373],"category_scores_gemma":[0.022374833,0.00024634696,0.00019674539,0.0010927035,0.010854447,0.009214001,0.009992627,0.002068093,0.0005792378],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100287514,0.000044605396,0.04380212,0.00012367095,0.0000170542,0.0006248248,0.884184,0.00005632899,0.0025203829,0.025748378,0.0007521818,0.0420263],"study_design_scores_gemma":[0.000008266172,0.00007944759,0.044180512,0.00032804688,0.000024934674,0.0009714839,0.8915446,0.0005781631,0.0013844442,0.020110251,0.0407384,0.0000514681],"about_ca_topic_score_codex":0.0050259903,"about_ca_topic_score_gemma":0.004958275,"teacher_disagreement_score":0.006997156,"about_ca_system_score_codex":0.0015683358,"about_ca_system_score_gemma":0.0015450065,"threshold_uncertainty_score":0.030926704},"labels":[],"label_agreement":null},{"id":"W4213014074","doi":"10.21203/rs.3.rs-1356281/v1","title":"Dbias: Detecting biases and ensuring Fairness in news articles","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Internet privacy; Business","score_opus":0.11496495797838957,"score_gpt":0.37661430346953023,"score_spread":0.26164934549114066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213014074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23991545,0.0014858387,0.7193474,0.0037652033,0.00088553695,0.0009065481,0.0040142084,0.01724058,0.012439251],"genre_scores_gemma":[0.7502842,0.00020994771,0.24125993,0.00055743725,0.00037292243,0.00049940933,0.0017339528,0.00071857765,0.0043636737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9576596,0.02166269,0.0029530174,0.0069974186,0.008754943,0.0019722984],"domain_scores_gemma":[0.8005377,0.14090203,0.011550995,0.028497266,0.013844765,0.004667273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049185928,0.0011632384,0.0021579508,0.0043007825,0.0029016177,0.007235161,0.00284935,0.003176372,0.0052276934],"category_scores_gemma":[0.20099325,0.0012037422,0.0006910909,0.0025072442,0.0031880157,0.00703678,0.0070239203,0.0026962827,0.0022795505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006049699,0.0010159016,0.1477626,0.0014429126,0.00081930717,0.00037907448,0.004683031,0.025111562,0.023229674,0.08369745,0.038138404,0.6676703],"study_design_scores_gemma":[0.0008358016,0.0007936738,0.027475392,0.0002670529,0.00037260447,0.0004500537,0.002524603,0.61551416,0.06719083,0.25549713,0.028857816,0.000220895],"about_ca_topic_score_codex":0.0030481436,"about_ca_topic_score_gemma":0.0031633934,"teacher_disagreement_score":0.049185928,"about_ca_system_score_codex":0.0019504321,"about_ca_system_score_gemma":0.004793063,"threshold_uncertainty_score":0.2601232},"labels":[],"label_agreement":null},{"id":"W4213077840","doi":"10.22230/cjc.2022v47n1a3931","title":"Archetypes and Homophilic Avatars: New Approaches to Studying Far-Right Facebook Practice","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Performative utterance; Homophily; Archetype; Politics; Avatar; Advertising; Sociology; Performativity; Internet privacy; Social psychology; Aesthetics; Psychology; Political science; Computer science; Business; Law; Art; Human–computer interaction; Gender studies","score_opus":0.08915508511764923,"score_gpt":0.239864887570359,"score_spread":0.15070980245270976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213077840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84195006,0.0017463077,0.050913766,0.004867041,0.0001397188,0.00037981608,0.00012602046,0.0000530961,0.09982416],"genre_scores_gemma":[0.98987794,0.00035834027,0.006289241,0.0002535716,0.000018715262,0.0003185713,0.000029756857,0.000021129568,0.002832701],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9930674,0.0056815427,0.00009688535,0.0004950485,0.00037843568,0.00028077222],"domain_scores_gemma":[0.9819338,0.014022491,0.0015160004,0.0011950758,0.0005598014,0.00077296107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007794563,0.0006202213,0.00047045024,0.004595039,0.0063283225,0.007709343,0.0014328988,0.0016689221,0.0047288067],"category_scores_gemma":[0.010149585,0.0004111124,0.00040620624,0.0022341933,0.025992297,0.010316647,0.0069642877,0.0030660464,0.00035342242],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049099668,0.000111994814,0.009987407,0.00020956535,0.000014329853,0.00026229993,0.8547137,0.0001510697,0.00083671475,0.118805185,0.00046579205,0.014392974],"study_design_scores_gemma":[0.000015653062,0.00005654059,0.00886675,0.0003358862,0.000017344813,0.00034480516,0.8982138,0.0016558463,0.00061959424,0.07101484,0.01882746,0.00003148255],"about_ca_topic_score_codex":0.0041705435,"about_ca_topic_score_gemma":0.009624162,"teacher_disagreement_score":0.007794563,"about_ca_system_score_codex":0.0045713726,"about_ca_system_score_gemma":0.0023810829,"threshold_uncertainty_score":0.041222036},"labels":[],"label_agreement":null},{"id":"W4213296124","doi":"10.21810/jicw.v4i3.4191","title":"Predicting the Escalation to Violence of Online Hate Groups","year":2022,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Presentation (obstetrics); Influencer marketing; Session (web analytics); Psychology; Gun violence; Period (music); Hate crime; Criminology; Social psychology; Computer security; Suicide prevention; Poison control; Computer science; Advertising; Business","score_opus":0.022033286552093634,"score_gpt":0.2619118643308156,"score_spread":0.23987857777872196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213296124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99334455,0.00013524422,0.0019530753,0.00035275822,0.000028183485,0.000056885423,0.0002977015,0.000046174362,0.0037855306],"genre_scores_gemma":[0.99696594,0.00010269965,0.0011219797,0.000043220953,0.000014361421,0.000017634466,0.0004653455,0.000007922565,0.0012608063],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883574,0.0004410151,0.00006641317,0.000144319,0.0002977522,0.00021469942],"domain_scores_gemma":[0.99358505,0.0027415396,0.0014543749,0.0003543001,0.00090254593,0.00096221355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017062277,0.00038822522,0.00028341523,0.001727582,0.0006611613,0.0013459764,0.00034656734,0.00061391847,0.002328458],"category_scores_gemma":[0.013896445,0.00027073896,0.0004134828,0.00049104786,0.0005417603,0.001096434,0.001489494,0.0016262634,0.0011320469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031401223,0.0003067399,0.93498856,0.000047607387,0.00009172906,0.0002172224,0.0019362393,0.004195708,0.0012460551,0.0008082218,0.0035419622,0.052305903],"study_design_scores_gemma":[0.000008912949,0.00039054867,0.94296634,0.00005790545,0.00004286964,0.00039445877,0.005129684,0.04342545,0.0032785002,0.0009014642,0.0033506795,0.000053087548],"about_ca_topic_score_codex":0.006346182,"about_ca_topic_score_gemma":0.009764068,"teacher_disagreement_score":0.006346182,"about_ca_system_score_codex":0.0005795098,"about_ca_system_score_gemma":0.0002191464,"threshold_uncertainty_score":0.012618542},"labels":[],"label_agreement":null},{"id":"W4213330595","doi":"10.22230/cjc.2022v47n1a3947","title":"Political Cartoon Representations of Freedom of Information in Canada","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Secrecy; Transparency (behavior); Politics; Newspaper; Freedom of information; Freedom of the press; Censorship; Government (linguistics); Doors; Political science; Media studies; Sociology; Law; Computer science; Linguistics","score_opus":0.01056044709731025,"score_gpt":0.21779334531794906,"score_spread":0.2072328982206388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213330595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6802646,0.0022127603,0.003407028,0.007150546,0.00026161782,0.000057892466,0.0016642015,0.00011554408,0.30486587],"genre_scores_gemma":[0.99053043,0.0005665326,0.00045180472,0.0001258351,0.000013704461,0.000007784,0.00017853451,0.000020907031,0.008104423],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99891543,0.00025205076,0.000021640917,0.00007705251,0.00046305233,0.00027071548],"domain_scores_gemma":[0.9976586,0.00079689425,0.00042088516,0.00016945464,0.00064101536,0.00031311312],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00089019065,0.0003261241,0.00020164224,0.0034525718,0.009266528,0.00730736,0.00053263124,0.0005435179,0.010256334],"category_scores_gemma":[0.0039345566,0.00018163306,0.00021041928,0.006272253,0.0070911855,0.0018216028,0.0021869682,0.0011194673,0.00031146867],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031269056,0.00003305376,0.03024915,0.0003337107,0.000033669512,0.0019306638,0.6397925,0.001620876,0.0028992554,0.21795632,0.038280357,0.06655774],"study_design_scores_gemma":[0.000016063072,0.00002353513,0.057646595,0.0004359686,0.000039562376,0.0005260621,0.40374896,0.0030717812,0.001010216,0.009712713,0.52363485,0.00013363156],"about_ca_topic_score_codex":0.95638096,"about_ca_topic_score_gemma":0.97335476,"teacher_disagreement_score":0.99073344,"about_ca_system_score_codex":0.039362404,"about_ca_system_score_gemma":0.023680506,"threshold_uncertainty_score":0.28559554},"labels":[],"label_agreement":null},{"id":"W4213358103","doi":"10.22230/cjc.2022v47n1a4037","title":"Sustainable Hate: How Gab Built a Durable “Platform for the People”","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Expansive; Popularity; Key (lock); Haven; Racism; Political science; Advertising; Sociology; Public relations; Business; Computer science; Computer security; Law","score_opus":0.01653948675724491,"score_gpt":0.22304436870082858,"score_spread":0.20650488194358368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213358103","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5489723,0.0036796967,0.022097128,0.028498638,0.001015265,0.00022079865,0.00023203362,0.00058622134,0.39469793],"genre_scores_gemma":[0.95476115,0.00091687776,0.0041468297,0.0014609235,0.000032773867,0.000028107797,0.000049808274,0.00016058201,0.038442988],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9977551,0.0012256748,0.000038775714,0.00020213971,0.00038315065,0.00039525775],"domain_scores_gemma":[0.99731654,0.0010569659,0.00032127026,0.00033877546,0.0005167963,0.00044966998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041946825,0.00068068894,0.00025157628,0.0018739589,0.010965207,0.0120813055,0.0008222833,0.0023875886,0.008259372],"category_scores_gemma":[0.007081204,0.00039837824,0.0003292588,0.000968864,0.009597842,0.008443946,0.00541256,0.0027164035,0.0019850933],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013967701,0.00007742087,0.016935106,0.00042385343,0.00004167733,0.0027918306,0.57642287,0.00072182214,0.0028325685,0.27949902,0.033800524,0.08631367],"study_design_scores_gemma":[0.000011423528,0.000086632994,0.0047866567,0.00067919045,0.000046038138,0.0013266281,0.31323615,0.0010771092,0.0020904157,0.025119588,0.6514648,0.00007531509],"about_ca_topic_score_codex":0.024359517,"about_ca_topic_score_gemma":0.04439186,"teacher_disagreement_score":0.024359517,"about_ca_system_score_codex":0.005694157,"about_ca_system_score_gemma":0.0037293737,"threshold_uncertainty_score":0.04843545},"labels":[],"label_agreement":null},{"id":"W4214492748","doi":"10.1155/2022/8467349","title":"An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding","year":2022,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":29,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Taif University","keywords":"Computer science; Word (group theory); Artificial intelligence; Word embedding; Binary classification; Natural language processing; Embedding; Encoder; Context (archaeology); Social media; Machine learning; Speech recognition; Support vector machine; World Wide Web; Linguistics","score_opus":0.1078338663258403,"score_gpt":0.35528519929207064,"score_spread":0.24745133296623034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214492748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5185571,0.001558919,0.4620189,0.0010037267,0.0005268281,0.00042924276,0.0029914263,0.007422236,0.0054916246],"genre_scores_gemma":[0.83274627,0.00071221654,0.15323141,0.00015627964,0.00012637796,0.00025492115,0.0034535595,0.00011172808,0.009207226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996872,0.00007411621,0.000025848196,0.000091929636,0.00008230325,0.00003862408],"domain_scores_gemma":[0.9992937,0.00024751297,0.00010170746,0.00005506468,0.00027253237,0.000029416742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042650738,0.0011102199,0.00040235426,0.0014578938,0.00024144104,0.00053914264,0.00047135222,0.0007788675,0.0013566343],"category_scores_gemma":[0.0014733676,0.00018179437,0.0005585273,0.000792709,0.0002190647,0.0015736514,0.00059031503,0.0007579644,0.001323405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056727754,0.00042520527,0.009782809,0.000424756,0.00011635157,0.0006335147,0.00029692717,0.028547335,0.0917899,0.0010112324,0.0067307926,0.8596739],"study_design_scores_gemma":[0.000023394161,0.0005393825,0.009422257,0.00006383619,0.000071253955,0.00031323644,0.0004290707,0.90602946,0.07442268,0.0041541737,0.004487124,0.000044161396],"about_ca_topic_score_codex":0.0016381354,"about_ca_topic_score_gemma":0.0025142326,"teacher_disagreement_score":0.0016381354,"about_ca_system_score_codex":0.00048277565,"about_ca_system_score_gemma":0.0004339497,"threshold_uncertainty_score":0.0045383573},"labels":[],"label_agreement":null},{"id":"W4214810516","doi":"10.1504/ijeg.2021.121241","title":"Sentiment analysis of political discussion on Twitter in Nigeria's 2019 presidential election","year":2021,"lang":"en","type":"article","venue":"International Journal of Electronic Governance","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Presidential election; Social media; Politics; Presidential system; Sentiment analysis; Political science; General election; Representation (politics); Ideology; Political communication; Public relations; Lexicon; Media studies; Sociology; Computer science; Law; Artificial intelligence","score_opus":0.004501113774446315,"score_gpt":0.25688550649457087,"score_spread":0.25238439272012453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214810516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934388,0.00014284071,0.00033293373,0.00023343215,0.00006601849,0.000026256459,0.00097044965,0.000017848133,0.0047714165],"genre_scores_gemma":[0.99534565,0.00017865765,0.0005156779,0.00005885922,0.00007501691,0.000055186178,0.0011635852,0.000010223985,0.0025970847],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99966276,0.000095574134,0.000032694174,0.000043163764,0.00009435235,0.00007145465],"domain_scores_gemma":[0.9992772,0.00029462512,0.00014748033,0.000026252212,0.00019827978,0.00005628797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040636776,0.00015743567,0.00023214724,0.0010009658,0.00061621726,0.0008055243,0.00010241961,0.0002542223,0.0014067884],"category_scores_gemma":[0.0013714214,0.000070845155,0.00017452952,0.0009148015,0.00021901877,0.00062670826,0.0003927482,0.00027247344,0.0005961393],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019431032,0.00041817222,0.7301082,0.0010509532,0.00012780416,0.002415558,0.033029683,0.0013297487,0.046975464,0.0026724346,0.021002453,0.15892641],"study_design_scores_gemma":[0.000012016399,0.00020861864,0.92370707,0.00011770935,0.0000688242,0.0003664526,0.03351829,0.008071411,0.008357732,0.0005194511,0.025005367,0.000046970745],"about_ca_topic_score_codex":0.0018093392,"about_ca_topic_score_gemma":0.0040226923,"teacher_disagreement_score":0.0018093392,"about_ca_system_score_codex":0.0002742823,"about_ca_system_score_gemma":0.00018971614,"threshold_uncertainty_score":0.004706204},"labels":[],"label_agreement":null},{"id":"W4214816497","doi":"10.25071/2564-2855.11","title":"Content moderation as language policy","year":2022,"lang":"en","type":"article","venue":"Working papers in Applied Linguistics and Linguistics at York","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Offensive; Moderation; Normative; Computer science; Process (computing); De facto; Government (linguistics); Language policy; Content (measure theory); Public relations; Psychology; Political science; Linguistics; Social psychology; Law; Engineering; Operations research; Pedagogy","score_opus":0.01779615739909172,"score_gpt":0.23465904972831095,"score_spread":0.21686289232921924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214816497","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07670872,0.0023648536,0.30497733,0.09157572,0.0011293782,0.00094440056,0.00040454496,0.0014410046,0.5204542],"genre_scores_gemma":[0.9230037,0.00055371446,0.03768301,0.01037095,0.0005032051,0.00097911,0.00016359855,0.000821322,0.025921458],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.90554243,0.05872275,0.0034387559,0.012257942,0.015277558,0.004760606],"domain_scores_gemma":[0.77002066,0.14940478,0.014705356,0.036277376,0.02445291,0.0051389392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07058287,0.00084037555,0.00068337045,0.0033195773,0.009613169,0.01883144,0.0027162742,0.007021829,0.010397176],"category_scores_gemma":[0.17744836,0.0010204605,0.0007051223,0.0026468353,0.043278765,0.022233361,0.011963713,0.008249464,0.0028449327],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055785633,0.000042558997,0.0042821616,0.00010804843,0.000019522986,0.00013734544,0.024115626,0.0005007462,0.0018519511,0.93051785,0.0063985544,0.031969883],"study_design_scores_gemma":[0.0000706109,0.00007695473,0.004394717,0.00061350013,0.00005499959,0.00022906861,0.010326492,0.0033327427,0.0058415555,0.7000158,0.2749152,0.00012842135],"about_ca_topic_score_codex":0.011324725,"about_ca_topic_score_gemma":0.007037426,"teacher_disagreement_score":0.07058287,"about_ca_system_score_codex":0.014851065,"about_ca_system_score_gemma":0.016161226,"threshold_uncertainty_score":0.3732823},"labels":[],"label_agreement":null},{"id":"W4220920946","doi":"10.22329/il.v42i1.7222","title":"Douglas Walton’s Contributions in Education","year":2022,"lang":"en","type":"article","venue":"Informal Logic","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Argumentation theory; Field (mathematics); Epistemology; Sociology; Informal logic; Education theory; Law; Political science; Philosophy; Higher education","score_opus":0.0073829251218354005,"score_gpt":0.2404133217296493,"score_spread":0.2330303966078139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220920946","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075087063,0.3781556,0.018744553,0.38370654,0.024233708,0.000051242427,0.0001477214,0.0001235696,0.18732834],"genre_scores_gemma":[0.35321283,0.3421293,0.015220815,0.10712427,0.01730166,0.00011854175,0.00016059978,0.00021849517,0.16451344],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99463,0.0023621083,0.00023931712,0.00088962686,0.0016041507,0.00027491772],"domain_scores_gemma":[0.98528415,0.010968893,0.0005398965,0.00038395036,0.0018251751,0.0009978983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033933537,0.0006590375,0.000623235,0.0032807556,0.0029581615,0.0062522795,0.00066664256,0.0038705359,0.008673136],"category_scores_gemma":[0.01406444,0.00029407963,0.0003162243,0.0028810222,0.007124339,0.0055513754,0.002629507,0.0053753844,0.0022728064],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043877975,0.000079590085,0.002436237,0.00083092006,0.000033425647,0.0002999828,0.008729324,0.0005015119,0.0002165668,0.5624258,0.2257792,0.19862351],"study_design_scores_gemma":[0.000005805786,0.000019566349,0.001086604,0.001461227,0.000011404964,0.00043658138,0.001534013,0.00026797195,0.00021388766,0.11880874,0.87612075,0.000033419103],"about_ca_topic_score_codex":0.00557411,"about_ca_topic_score_gemma":0.0059533855,"teacher_disagreement_score":0.008673136,"about_ca_system_score_codex":0.0037755193,"about_ca_system_score_gemma":0.005217499,"threshold_uncertainty_score":0.029014528},"labels":[],"label_agreement":null},{"id":"W4221097964","doi":"10.5430/wjel.v12n1p419","title":"Perspectives on Covid19 Safety Protocols among Non-Native English Speaking Teachers and Students","year":2022,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Snowball sampling; Distancing; Descriptive statistics; Psychology; Medical education; Comprehension; Coronavirus disease 2019 (COVID-19); Mathematics education; Computer science; Medicine; Statistics","score_opus":0.00740782312892793,"score_gpt":0.2696555917611591,"score_spread":0.2622477686322312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221097964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99353987,0.0003913202,0.00054499484,0.0015790837,0.000030366364,0.000047892467,0.00002418703,0.0000065041477,0.0038357188],"genre_scores_gemma":[0.9971655,0.00066858117,0.0002680464,0.000582634,0.00002081491,0.000039703402,0.000020758296,0.000005482249,0.0012284055],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9884261,0.006801533,0.0006818805,0.0004446659,0.0022839059,0.0013619758],"domain_scores_gemma":[0.9786968,0.0098318,0.0045166924,0.00065126736,0.0028986724,0.003404875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010265079,0.0003062679,0.0005190886,0.0010789436,0.002956331,0.004226734,0.000616521,0.0013140555,0.0019703582],"category_scores_gemma":[0.021955686,0.00044765923,0.00043064475,0.0005393948,0.0034746246,0.002608894,0.0027952658,0.0026381365,0.00050077506],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006801815,0.00017413142,0.08678649,0.00023167193,0.000012463941,0.0012405799,0.8935329,0.000057553643,0.002457344,0.0010950454,0.00068689045,0.013657048],"study_design_scores_gemma":[0.0000053565864,0.00043435633,0.029674493,0.00026358615,0.000013396876,0.00064271135,0.9590278,0.00008385506,0.0005468246,0.00024402176,0.009031499,0.000032081094],"about_ca_topic_score_codex":0.0049175844,"about_ca_topic_score_gemma":0.00569609,"teacher_disagreement_score":0.010265079,"about_ca_system_score_codex":0.0020374204,"about_ca_system_score_gemma":0.0043412587,"threshold_uncertainty_score":0.054287612},"labels":[],"label_agreement":null},{"id":"W4223975859","doi":"10.1002/cl2.1228","title":"PROTOCOL: Mapping the scientific knowledge and approaches to defining and measuring hate crime, hate speech, and hate incidents","year":2022,"lang":"en","type":"article","venue":"Campbell Systematic Reviews","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Hate crime; Underpinning; Love and hate; Psychology; Criminology; Engineering","score_opus":0.20556971487346654,"score_gpt":0.287234409854773,"score_spread":0.08166469498130646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223975859","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00070143613,0.0034496605,0.0038020322,0.0031923074,0.0015133872,0.9621814,0.022059076,0.00031457914,0.0027862352],"genre_scores_gemma":[0.00049782847,0.0012407001,0.0044990974,0.0006160866,0.000051843952,0.99078184,0.0012797321,0.000030601324,0.0010023045],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9321392,0.027403213,0.023061452,0.0045709135,0.009614239,0.0032108817],"domain_scores_gemma":[0.8160959,0.06927748,0.022003718,0.016389666,0.07172707,0.004506133],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.13348386,0.004505862,0.011274336,0.019518001,0.0061013084,0.012035151,0.0054184925,0.01156483,0.12323093],"category_scores_gemma":[0.23124154,0.0052514654,0.011567906,0.01883024,0.0067418474,0.011805141,0.010715936,0.011316021,0.02496342],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004458339,0.000331272,0.0010332993,0.77041245,0.0013677181,0.0003647469,0.0030953367,0.0009764545,0.0014165896,0.013315753,0.12685749,0.07637056],"study_design_scores_gemma":[0.009896035,0.0009487296,0.004902699,0.61126125,0.003064548,0.0002572493,0.003721871,0.000642212,0.00169801,0.014488496,0.3487543,0.0003646665],"about_ca_topic_score_codex":0.011865984,"about_ca_topic_score_gemma":0.016354168,"teacher_disagreement_score":0.8665161,"about_ca_system_score_codex":0.019320387,"about_ca_system_score_gemma":0.107199565,"threshold_uncertainty_score":0.7059386},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"systematic_review","genre":"protocol","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"systematic_review","genre":"protocol","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4224109040","doi":"10.29173/psur294","title":"Cancelling, Counter-speech and Censorship","year":2022,"lang":"en","type":"article","venue":"Political Science Undergraduate Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Censorship; Civil liberties; Social media; Freedom of expression; Expression (computer science); Action (physics); Political science; Public relations; First amendment; Internet privacy; Sociology; Law; Computer science; Politics; Human rights","score_opus":0.018426285206844903,"score_gpt":0.27698398185304335,"score_spread":0.25855769664619843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224109040","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02912556,0.88121325,0.003134218,0.02577749,0.00256719,0.000081744554,0.00034651745,0.00007534173,0.05767878],"genre_scores_gemma":[0.37113032,0.60261196,0.0015214968,0.008764407,0.0024032902,0.0000694842,0.0004177617,0.00005091986,0.013030359],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9923034,0.0030951274,0.0005868897,0.0010179428,0.0025040351,0.0004927475],"domain_scores_gemma":[0.9584094,0.026872126,0.0071029784,0.001788724,0.004901653,0.0009250813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009518821,0.0004394944,0.0007525238,0.0040161978,0.00146727,0.0047999574,0.0010659056,0.0020708493,0.004660146],"category_scores_gemma":[0.042378645,0.0002605958,0.0005153489,0.0027314252,0.0049542678,0.0050496575,0.001673792,0.0026121333,0.0010514611],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010300903,0.000046887282,0.008482146,0.00857476,0.00022220032,0.00024508906,0.0039238124,0.00027077735,0.000505939,0.070551574,0.03463381,0.87244],"study_design_scores_gemma":[0.000032348948,0.00020230166,0.038378485,0.023335274,0.00035893772,0.001107212,0.007278979,0.00047996792,0.0028684488,0.02903653,0.8968475,0.00007405079],"about_ca_topic_score_codex":0.007571055,"about_ca_topic_score_gemma":0.015213527,"teacher_disagreement_score":0.009518821,"about_ca_system_score_codex":0.0020591875,"about_ca_system_score_gemma":0.0033335325,"threshold_uncertainty_score":0.05034089},"labels":[],"label_agreement":null},{"id":"W4224223031","doi":"10.2196/29183","title":"Types of Racism and Twitter Users’ Responses Amid the COVID-19 Outbreak: Content Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Instituto de Salud Carlos III; European Commission","keywords":"Racism; Prejudice (legal term); Microblogging; Social media; Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Psychology; Sociology; Social psychology; Political science; History; Gender studies; Medicine; Law","score_opus":0.14221831928748954,"score_gpt":0.398517904294675,"score_spread":0.2562995850071854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224223031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919606,0.00009604493,0.00057273306,0.0004370524,0.000031011623,0.00034745835,0.0039714877,0.00003218379,0.0025513228],"genre_scores_gemma":[0.9885872,0.00028596303,0.0028964875,0.0003051743,0.000092328795,0.0015081996,0.00436303,0.000047397603,0.0019143062],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980969,0.000665341,0.00021890143,0.00026839785,0.0004488671,0.00030157593],"domain_scores_gemma":[0.989778,0.005970804,0.0018553938,0.00028739378,0.0017829326,0.00032543845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023242536,0.0002687715,0.0003573077,0.004066285,0.0011787327,0.0019664508,0.0003799463,0.0005808339,0.001918474],"category_scores_gemma":[0.012168921,0.00016546981,0.00035620233,0.0040773363,0.0007848458,0.001910005,0.0017827521,0.00058882,0.0005556435],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007092764,0.00020995554,0.576254,0.0020917042,0.00012152685,0.0012457825,0.32764983,0.00053784915,0.009815342,0.0016266782,0.011683023,0.068055056],"study_design_scores_gemma":[0.0000121704625,0.00011840531,0.7598196,0.00032928592,0.000060318613,0.00027527474,0.21889022,0.00257912,0.002261215,0.00055102643,0.015021217,0.00008203487],"about_ca_topic_score_codex":0.0046987375,"about_ca_topic_score_gemma":0.0055234656,"teacher_disagreement_score":0.0046987375,"about_ca_system_score_codex":0.0013279326,"about_ca_system_score_gemma":0.0007952554,"threshold_uncertainty_score":0.012291968},"labels":[],"label_agreement":null},{"id":"W4225817241","doi":"10.1007/s10489-022-03944-z","title":"Reward modeling for mitigating toxicity in transformer-based language models","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Language model; Transformer; Artificial intelligence; Unintended consequences; Detoxification (alternative medicine); Machine learning; Natural language processing; Engineering","score_opus":0.028401121121705696,"score_gpt":0.25610199946126266,"score_spread":0.22770087833955696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225817241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02246505,0.00022463792,0.9742332,0.0006062101,0.00007160091,0.000043735,0.00014759057,0.00077854807,0.0014293938],"genre_scores_gemma":[0.91742444,0.00028405373,0.07699329,0.00035732033,0.00007344876,0.00011563215,0.00033512476,0.00025080732,0.0041657877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759173,0.0012062486,0.00014746738,0.00035623708,0.00042901968,0.00026923465],"domain_scores_gemma":[0.98489237,0.011927486,0.00054629135,0.0007922179,0.0015273495,0.00031425996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005114217,0.0010081221,0.0014715623,0.0008586331,0.0007639938,0.0016927706,0.002224866,0.0016818342,0.0040738294],"category_scores_gemma":[0.031154646,0.0007214847,0.00090102514,0.0007252586,0.001070543,0.0042391005,0.0022124841,0.003452685,0.0008536653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003022191,0.00010523619,0.0015429469,0.000115415554,0.00007583899,0.0001330932,0.00017752757,0.86891323,0.0018036468,0.07376969,0.0020768938,0.050984208],"study_design_scores_gemma":[0.000006673387,0.000019644727,0.00003832023,0.0000049554865,0.000013498726,0.000012517459,0.000006801135,0.9813059,0.0003696943,0.018011471,0.00020623705,0.0000044393923],"about_ca_topic_score_codex":0.008108677,"about_ca_topic_score_gemma":0.010208168,"teacher_disagreement_score":0.008108677,"about_ca_system_score_codex":0.0015784116,"about_ca_system_score_gemma":0.0025331092,"threshold_uncertainty_score":0.02704686},"labels":[],"label_agreement":null},{"id":"W4229440380","doi":"10.18653/v1/2022.naacl-main.192","title":"Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Computational linguistics; Voice activity detection; Artificial intelligence; Linguistics; Speech recognition; Natural language processing; Speech processing; Philosophy","score_opus":0.0191876154053738,"score_gpt":0.2586639038828571,"score_spread":0.23947628847748328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229440380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16167302,0.0024407415,0.8040208,0.012424541,0.00021982432,0.00036493837,0.00072121486,0.0012077892,0.016927099],"genre_scores_gemma":[0.78630954,0.00047387343,0.20897178,0.00061062357,0.00021212017,0.00020404416,0.0007893885,0.00034831878,0.0020802347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9862694,0.009157169,0.0011967737,0.0013723655,0.0016211235,0.0003831696],"domain_scores_gemma":[0.6903337,0.28274167,0.0063537615,0.008294853,0.011484995,0.00079102954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020925779,0.0008222798,0.00065005996,0.002553368,0.0025763004,0.004355538,0.0020433266,0.005321208,0.0062466175],"category_scores_gemma":[0.13661745,0.0008672267,0.000789384,0.0017273633,0.0036193961,0.01278131,0.0022582803,0.0036033976,0.0012612845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013436804,0.00034090955,0.0649723,0.0016932341,0.00019249665,0.0068687256,0.028483389,0.015467201,0.019816158,0.4807053,0.017535513,0.36258113],"study_design_scores_gemma":[0.00018075453,0.00033035956,0.013776092,0.0010514617,0.0003487722,0.00792482,0.00797301,0.313296,0.027922347,0.5771393,0.049884256,0.00017271322],"about_ca_topic_score_codex":0.0023195005,"about_ca_topic_score_gemma":0.0028447488,"teacher_disagreement_score":0.020925779,"about_ca_system_score_codex":0.0019102277,"about_ca_system_score_gemma":0.0016084028,"threshold_uncertainty_score":0.11066741},"labels":[],"label_agreement":null},{"id":"W4229449951","doi":"10.33423/jabe.v24i2.5143","title":"The Strangulation and Resuscitation of Representative Democracy in the United States","year":2022,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Democracy; Autocracy; Politics; Presidential system; Resuscitation; Government (linguistics); Secret ballot; Shock (circulatory); Ballot; Law; Political science; Medicine; Voting; Internal medicine; Emergency medicine","score_opus":0.011339465050211045,"score_gpt":0.21859872535656077,"score_spread":0.20725926030634972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229449951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96684086,0.0001913339,0.003943679,0.0033278824,0.00004142355,0.000027625578,0.00014601239,0.00003578631,0.02544546],"genre_scores_gemma":[0.99881697,0.000059521783,0.00028890008,0.0001502631,0.000009803384,0.000008169659,0.00005084084,0.0000019876672,0.000613709],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979448,0.0010937249,0.000088959016,0.00016796023,0.00037551107,0.00032895274],"domain_scores_gemma":[0.9931299,0.002222517,0.0027247772,0.00041857126,0.0008278958,0.0006763078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002389311,0.00011953176,0.00020001917,0.00085917435,0.00082643435,0.0013517624,0.00019397534,0.0003219702,0.0022751323],"category_scores_gemma":[0.013610071,0.00014486114,0.00017310883,0.0007808555,0.0014721355,0.0013238784,0.0018008171,0.001141626,0.00024665275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031147903,0.00023206681,0.7527809,0.000108149194,0.00008788359,0.0005886493,0.005561894,0.018879764,0.0012733621,0.11322747,0.009904247,0.097044066],"study_design_scores_gemma":[0.000059642713,0.00043982876,0.8091052,0.00023704524,0.00007141049,0.00029435064,0.012203259,0.054338392,0.0041729193,0.082970865,0.03600574,0.00010129556],"about_ca_topic_score_codex":0.00915179,"about_ca_topic_score_gemma":0.013454446,"teacher_disagreement_score":0.00915179,"about_ca_system_score_codex":0.001467866,"about_ca_system_score_gemma":0.0017031442,"threshold_uncertainty_score":0.01819706},"labels":[],"label_agreement":null},{"id":"W4229747476","doi":"10.4018/9781616928056.ch008","title":"Female and Male Hacker Conferences Attendees","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Hacker; Internet privacy; Computer science; Computer security","score_opus":0.027767044976745727,"score_gpt":0.22802852149673347,"score_spread":0.20026147651998774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229747476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8713932,0.006135546,0.0006779562,0.0029358137,0.000581155,0.0002831338,0.00807054,0.00016212369,0.10976043],"genre_scores_gemma":[0.7669172,0.007088374,0.0003809751,0.0021150236,0.00036667837,0.00037761795,0.0017369335,0.00011421224,0.22090292],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99932826,0.00007251397,0.00003320837,0.00011624269,0.00023077926,0.00021906222],"domain_scores_gemma":[0.99844414,0.00058865093,0.0002780582,0.000037626134,0.00016944756,0.00048212553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004832849,0.00045242906,0.0004892022,0.0035305326,0.0012911065,0.0022787352,0.00034270337,0.00058548053,0.094851464],"category_scores_gemma":[0.0027673142,0.0002726886,0.00036430568,0.0016753849,0.00044864436,0.0018112814,0.0011419464,0.00070144905,0.017401027],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010799372,0.0015724298,0.3900418,0.0010975465,0.00012007475,0.0036447989,0.10506935,0.00022261274,0.00537833,0.0055262973,0.11615969,0.3700871],"study_design_scores_gemma":[0.0000340174,0.00052226917,0.785702,0.00042276518,0.000068755195,0.004126545,0.10675898,0.000277131,0.00097509846,0.00081703364,0.100221924,0.00007345874],"about_ca_topic_score_codex":0.004110109,"about_ca_topic_score_gemma":0.009514757,"teacher_disagreement_score":0.094851464,"about_ca_system_score_codex":0.00047526273,"about_ca_system_score_gemma":0.00028903518,"threshold_uncertainty_score":0.31730974},"labels":[],"label_agreement":null},{"id":"W4229760390","doi":"10.1201/b10718-26","title":"Cyber Bullying: Legal Obligations and","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cyber bullying; Computer security; Political science; Criminology; Psychology; Computer science; World Wide Web","score_opus":0.020439790251601985,"score_gpt":0.20491644137266077,"score_spread":0.1844766511210588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229760390","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022356482,0.02430761,0.0028741073,0.026662847,0.00042290398,0.000036877667,0.00008490284,0.00005201671,0.9232023],"genre_scores_gemma":[0.5598678,0.0320309,0.0020754733,0.010778679,0.00060591317,0.00014433223,0.0002008385,0.00007163917,0.3942244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986588,0.00033428022,0.00006415072,0.00014689959,0.0005095527,0.00028638923],"domain_scores_gemma":[0.9986738,0.00060773955,0.00014031993,0.00010260036,0.00028947505,0.00018605475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094637653,0.0002828182,0.0001931638,0.00086449704,0.004030793,0.0050977794,0.00067667617,0.0028545123,0.012086638],"category_scores_gemma":[0.0027861425,0.00028252226,0.00018935616,0.0007274813,0.007841448,0.0065394696,0.0034917286,0.0039624246,0.0013723287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037480443,0.000038375652,0.00091797195,0.000049444876,0.000001113037,0.0002881676,0.01875518,0.00007429234,0.00009226601,0.8968558,0.034118235,0.04880545],"study_design_scores_gemma":[0.000004759518,0.000021673699,0.0057157134,0.0012208484,0.0000055475803,0.0011276522,0.019078532,0.00026512722,0.0002509231,0.17694119,0.7953401,0.000027890528],"about_ca_topic_score_codex":0.03745977,"about_ca_topic_score_gemma":0.040819056,"teacher_disagreement_score":0.03745977,"about_ca_system_score_codex":0.0038599847,"about_ca_system_score_gemma":0.005319161,"threshold_uncertainty_score":0.074483454},"labels":[],"label_agreement":null},{"id":"W4230298489","doi":"10.1007/978-1-4939-7131-2_100029","title":"Anonymity","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.014656371545556619,"score_gpt":0.2080143336088703,"score_spread":0.19335796206331368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230298489","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010784984,0.0006953693,0.01956525,0.0031341882,0.0007124619,0.00006309243,0.0006255358,0.00051014614,0.9736154],"genre_scores_gemma":[0.062152695,0.0017211165,0.0059004403,0.0019802062,0.0008257113,0.00019078277,0.0008492121,0.0004473335,0.9259324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99778384,0.00052502437,0.000087605134,0.00052577973,0.00077976467,0.00029788713],"domain_scores_gemma":[0.99803466,0.00052984717,0.00010273202,0.0006967626,0.0005088604,0.00012718429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001310421,0.0008355734,0.0005668873,0.0013776958,0.002968196,0.0062353266,0.0012028035,0.0020422505,0.14432858],"category_scores_gemma":[0.00571779,0.00035761629,0.0005400332,0.0012294691,0.0026050543,0.008282205,0.0042551225,0.003217163,0.08702484],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026861408,0.000016846205,0.00007303094,0.000057146994,0.0000035061132,0.00003222213,0.00027581464,0.00016328579,0.00023232526,0.82955617,0.11070169,0.058861226],"study_design_scores_gemma":[0.000005145179,0.000010800481,0.000081565544,0.000051501498,0.000005352702,0.00017618039,0.00017288567,0.00025450528,0.0006415596,0.3015316,0.69705665,0.000012159357],"about_ca_topic_score_codex":0.00092456397,"about_ca_topic_score_gemma":0.00080761284,"teacher_disagreement_score":0.14432858,"about_ca_system_score_codex":0.0015595047,"about_ca_system_score_gemma":0.0016473354,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4230630424","doi":"10.1017/cbo9781139095891","title":"Sexting and Cyberbullying","year":2014,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Political science; Value (mathematics); Criminology; Public relations; Human rights; Sociology; Psychology; Law","score_opus":0.0113809655414549,"score_gpt":0.17839775965680194,"score_spread":0.16701679411534703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230630424","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028234948,0.15984118,0.0020891023,0.009527283,0.0038512098,0.000058385947,0.00009757881,0.00017550388,0.8215363],"genre_scores_gemma":[0.01903147,0.08930516,0.0013581967,0.005158628,0.0013615745,0.00006749143,0.0001281818,0.0000917844,0.88349736],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996525,0.000056699737,0.0000108434415,0.000044523662,0.00020172435,0.000033696026],"domain_scores_gemma":[0.99965847,0.00020266522,0.000024664729,0.000021085558,0.000053935542,0.000039184604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002048082,0.00060598383,0.000334161,0.0013276853,0.0015935457,0.0034638958,0.00037182844,0.0016552844,0.024271997],"category_scores_gemma":[0.0010246504,0.00033206597,0.00023952487,0.0013432096,0.0028337569,0.0033521054,0.0014494737,0.0018526925,0.007075467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019298617,0.00007152904,0.0013390875,0.00040400092,0.000012083817,0.0002499494,0.0038787897,0.00025045092,0.00047344292,0.13199899,0.48676342,0.37453905],"study_design_scores_gemma":[0.0000029025075,0.000016030353,0.0017297996,0.00059813773,0.000004606046,0.00045176252,0.0010636329,0.00009546066,0.00017600201,0.01699576,0.9788574,0.0000084856265],"about_ca_topic_score_codex":0.004541923,"about_ca_topic_score_gemma":0.012299581,"teacher_disagreement_score":0.024271997,"about_ca_system_score_codex":0.0010777202,"about_ca_system_score_gemma":0.0013277308,"threshold_uncertainty_score":0.08119792},"labels":[],"label_agreement":null},{"id":"W4231141316","doi":"10.1007/978-1-4614-5690-2_132","title":"Hate Crime","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Criminology; Political science; Psychology","score_opus":0.01562805188228731,"score_gpt":0.20601937851435076,"score_spread":0.19039132663206346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231141316","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009318672,0.005039667,0.0015862362,0.003254553,0.0011959622,0.000030714145,0.00005836163,0.00005619337,0.98784643],"genre_scores_gemma":[0.011348172,0.0040093986,0.00062859675,0.002581645,0.00038858873,0.00003310255,0.00006650496,0.00004921684,0.9808946],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952734,0.00010215804,0.000012625215,0.000071451104,0.00019406796,0.000092325376],"domain_scores_gemma":[0.99972004,0.00006964462,0.00002663852,0.00004804294,0.00009113679,0.000044446824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004315992,0.0010688998,0.00036351502,0.0012838152,0.003533629,0.0035179215,0.0006723057,0.002928558,0.048241597],"category_scores_gemma":[0.001427748,0.00039253788,0.00029354714,0.0005562649,0.0030515941,0.0036952943,0.0020307282,0.003372334,0.027825676],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060657394,0.000041082498,0.0004390876,0.0000983973,0.000005380716,0.000092817325,0.0027486717,0.00010816604,0.00017142376,0.27843925,0.5378509,0.17999879],"study_design_scores_gemma":[0.0000011131154,0.0000095689575,0.0006695817,0.00020460816,0.0000028478014,0.00022071386,0.0008976945,0.00008310963,0.0001654023,0.020662121,0.97707695,0.0000062968957],"about_ca_topic_score_codex":0.008004975,"about_ca_topic_score_gemma":0.026167523,"teacher_disagreement_score":0.048241597,"about_ca_system_score_codex":0.0016859455,"about_ca_system_score_gemma":0.0015074945,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4234393863","doi":"10.7551/mitpress/12491.001.0001","title":"Hate Speech","year":2021,"lang":"en","type":"book","venue":"The MIT Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Offensive; Prejudice (legal term); Identity (music); Sexual orientation; Racism; Political science; Sociology; Law; Gender studies","score_opus":0.02463665874272713,"score_gpt":0.2269695491818167,"score_spread":0.20233289043908956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234393863","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01745396,0.017635878,0.0027408658,0.028936004,0.0057485793,0.00008599094,0.0004484371,0.00014440737,0.92680585],"genre_scores_gemma":[0.26536593,0.016655238,0.0015030085,0.030220859,0.003038904,0.00013074595,0.00051894726,0.00020354748,0.68236274],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957308,0.0012945734,0.00024125575,0.0005127186,0.0016630556,0.0005576013],"domain_scores_gemma":[0.9925889,0.0031414113,0.0009251216,0.001007601,0.0017741214,0.0005627851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025841442,0.00061891926,0.00039251277,0.0018436271,0.008316746,0.009060045,0.0009871908,0.0037090443,0.034503788],"category_scores_gemma":[0.011670251,0.0002906293,0.00036545048,0.0015201545,0.007332948,0.010003818,0.004513007,0.0036837202,0.010796309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055342745,0.000052259278,0.0026710145,0.00043912377,0.000013801292,0.0007887086,0.05225273,0.00006396904,0.00082454877,0.40222108,0.41616806,0.12444942],"study_design_scores_gemma":[0.000001924774,0.000019404337,0.0018158687,0.000464127,0.000005664665,0.00066648173,0.014879191,0.00004007935,0.000513253,0.006849051,0.97472805,0.000016875976],"about_ca_topic_score_codex":0.0066650817,"about_ca_topic_score_gemma":0.010863339,"teacher_disagreement_score":0.034503788,"about_ca_system_score_codex":0.0036899874,"about_ca_system_score_gemma":0.0027106549,"threshold_uncertainty_score":0.11542666},"labels":[],"label_agreement":null},{"id":"W4237407877","doi":"10.1007/978-3-319-32103-5","title":"Handbook of Global Media Ethics","year":2021,"lang":"en","type":"book","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Engineering ethics; Political science; Sociology; Environmental ethics; Philosophy; Engineering","score_opus":0.02581629378562496,"score_gpt":0.2578628530136541,"score_spread":0.2320465592280291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237407877","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025250315,0.032827396,0.0028960195,0.006558284,0.0013036928,0.00004829003,0.00021223676,0.00017257524,0.95572895],"genre_scores_gemma":[0.007317276,0.022419645,0.0025617816,0.0030582568,0.0008398083,0.00012901121,0.00025010097,0.00025840028,0.9631657],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924576,0.0002457409,0.00003501919,0.00006826308,0.0003315818,0.00007358684],"domain_scores_gemma":[0.99913114,0.0004499871,0.000034684555,0.0001051,0.00021318837,0.00006597733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087506074,0.0009581384,0.0005663616,0.002002931,0.0017309107,0.005375081,0.0008937308,0.0026285294,0.064511284],"category_scores_gemma":[0.0023772144,0.00051625085,0.0002195649,0.0019468071,0.002655991,0.004532257,0.00175407,0.0032322234,0.03408182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007656898,0.000020277821,0.000066472756,0.00012325805,0.0000024405165,0.000040586383,0.00089060684,0.00012147274,0.00011286959,0.33538568,0.57070833,0.092520334],"study_design_scores_gemma":[0.0000011516327,0.000002641743,0.000078342724,0.00016594918,8.160319e-7,0.00004605461,0.00015825711,0.00004007641,0.000032345975,0.02663061,0.972841,0.0000025694203],"about_ca_topic_score_codex":0.0044489773,"about_ca_topic_score_gemma":0.009964862,"teacher_disagreement_score":0.064511284,"about_ca_system_score_codex":0.0021104126,"about_ca_system_score_gemma":0.002722105,"threshold_uncertainty_score":0.21581173},"labels":[],"label_agreement":null},{"id":"W4238403565","doi":"10.24908/iqurcp.8829","title":"21. Online Hate: Discrimination at the Click of a Button","year":2016,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Racialization; Race (biology); Racism; Sociology; Psychology; Media studies; Gender studies","score_opus":0.08804301279679666,"score_gpt":0.3527795912675513,"score_spread":0.26473657847075466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238403565","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04237374,0.00031601364,0.004944327,0.006103304,0.0033675265,0.00067226135,0.020802964,0.019081367,0.9023386],"genre_scores_gemma":[0.12900619,0.00047737066,0.0068108924,0.0036193256,0.0008822885,0.0005078333,0.009702597,0.002193842,0.8467996],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99972516,0.00003356262,0.000014448821,0.000029154482,0.000111496425,0.000086137385],"domain_scores_gemma":[0.99856156,0.00037278887,0.00009843015,0.00017887595,0.00043014548,0.0003581074],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040179383,0.0005242727,0.00030640722,0.0009530322,0.0013728759,0.001987991,0.00037434287,0.0014479541,0.4079983],"category_scores_gemma":[0.0027820545,0.0002906653,0.00020561925,0.0004179665,0.00034147664,0.0018945598,0.0011016772,0.0007398546,0.25955617],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020139891,0.00020373562,0.0060668443,0.00013263743,0.000004311471,0.00032869127,0.0005723896,0.000023146466,0.0016179857,0.0014993347,0.8933079,0.09604167],"study_design_scores_gemma":[0.000045798297,0.00021027714,0.047044333,0.00022269099,0.000016498996,0.00069215946,0.001621495,0.00077159214,0.006711517,0.0016583708,0.94093716,0.0000681119],"about_ca_topic_score_codex":0.0023462933,"about_ca_topic_score_gemma":0.008353405,"teacher_disagreement_score":0.4079983,"about_ca_system_score_codex":0.0004209958,"about_ca_system_score_gemma":0.00033557642,"threshold_uncertainty_score":0.8444186},"labels":[],"label_agreement":null},{"id":"W4238820198","doi":"10.4018/978-1-7998-4555-3.ch003","title":"Delete, Delete, Hang-Up","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in public policy and administration (APPA) book series","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tragedy (event); Distress; Hang; Criminology; Psychology; Political science; Law; Social psychology; Engineering; Psychiatry; Psychotherapist","score_opus":0.017562544833462763,"score_gpt":0.25695577072030473,"score_spread":0.23939322588684198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238820198","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056172833,0.0026305239,0.048576064,0.00349723,0.0062456476,0.0004094392,0.005247556,0.024056004,0.9037204],"genre_scores_gemma":[0.021356609,0.002684364,0.016324557,0.0034788777,0.0010904325,0.00022573385,0.00381957,0.011731804,0.9392882],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954,0.00008333987,0.000026272417,0.00007276073,0.00021972989,0.000057989277],"domain_scores_gemma":[0.9984016,0.00047385207,0.00008354503,0.00050784956,0.00042096208,0.00011224665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058178295,0.0011558507,0.0006037905,0.001411459,0.0020234461,0.0029191845,0.0014101658,0.0011742517,0.2337966],"category_scores_gemma":[0.005255859,0.0004788593,0.00058450055,0.0015573329,0.0012238391,0.0076258495,0.0026497305,0.0013876905,0.24965498],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112646514,0.000051386567,0.00046488916,0.0002869611,0.000007874976,0.00035526068,0.0014616067,0.00017573845,0.0014785957,0.019148512,0.7364481,0.24000834],"study_design_scores_gemma":[0.000004941999,0.000015909243,0.00025415348,0.000069538306,0.0000044399017,0.00020764164,0.00038008578,0.00019628709,0.00084780017,0.00347006,0.99453753,0.000011509773],"about_ca_topic_score_codex":0.0027251977,"about_ca_topic_score_gemma":0.0047321515,"teacher_disagreement_score":0.2337966,"about_ca_system_score_codex":0.00052497454,"about_ca_system_score_gemma":0.0005540929,"threshold_uncertainty_score":0.7821275},"labels":[],"label_agreement":null},{"id":"W4240020752","doi":"10.32920/ryerson.14647572","title":"Technology Producers use of Language and Discourse to Shape and Reinstate Anti-Black Global Realities: An Analysis of Amazon’s Facial Recognition Technology Communications and Responses to Racial Bias in Rekognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"","keywords":"Amazon rainforest; Law enforcement; Enforcement; Face (sociological concept); Race (biology); Sociology; Racism; Neutrality; Political science; Law; Gender studies; Social science","score_opus":0.060278546560245456,"score_gpt":0.33594512923532605,"score_spread":0.2756665826750806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240020752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96949434,0.00029345567,0.0006904866,0.0022376066,0.000019596735,0.000037575686,0.00006473775,0.000012760535,0.027149353],"genre_scores_gemma":[0.9969048,0.00013202381,0.00023790638,0.00031084582,0.000010765029,0.000019395597,0.000029482948,0.000022953855,0.0023317216],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99822956,0.0008840327,0.00006633109,0.00022955652,0.0003199172,0.00027067485],"domain_scores_gemma":[0.99351776,0.004391591,0.00090080895,0.00036427536,0.00056907744,0.00025648274],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0028752582,0.00025047245,0.00022510684,0.0013316714,0.0051606316,0.0053145136,0.00058588997,0.0010805108,0.004090923],"category_scores_gemma":[0.007998355,0.0002265957,0.00014792632,0.0013175983,0.0060903737,0.00456736,0.00317231,0.0016116473,0.0003333169],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048271613,0.000030754138,0.010255409,0.00007760178,0.0000046750833,0.00039277982,0.97038424,0.0000141805585,0.0022733642,0.008635764,0.000758989,0.007123952],"study_design_scores_gemma":[0.000008014013,0.000041807336,0.042147066,0.00015968173,0.000015591539,0.00019647539,0.9105111,0.0003794653,0.0013322918,0.0022316007,0.042953815,0.000023060435],"about_ca_topic_score_codex":0.018728811,"about_ca_topic_score_gemma":0.027773289,"teacher_disagreement_score":0.9948394,"about_ca_system_score_codex":0.004750307,"about_ca_system_score_gemma":0.0016589715,"threshold_uncertainty_score":0.03723961},"labels":[],"label_agreement":null},{"id":"W4240034828","doi":"10.32920/ryerson.14647572.v1","title":"Technology Producers use of Language and Discourse to Shape and Reinstate Anti-Black Global Realities: An Analysis of Amazon’s Facial Recognition Technology Communications and Responses to Racial Bias in Rekognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"","keywords":"Amazon rainforest; Law enforcement; Enforcement; Face (sociological concept); Race (biology); Sociology; Facial recognition system; Neutrality; Political science; Psychology; Law; Gender studies; Cognitive psychology; Social science","score_opus":0.060278546560245456,"score_gpt":0.33594512923532605,"score_spread":0.2756665826750806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240034828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96949434,0.00029345567,0.0006904866,0.0022376066,0.000019596735,0.000037575686,0.00006473775,0.000012760535,0.027149353],"genre_scores_gemma":[0.9969048,0.00013202381,0.00023790638,0.00031084582,0.000010765029,0.000019395597,0.000029482948,0.000022953855,0.0023317216],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99822956,0.0008840327,0.00006633109,0.00022955652,0.0003199172,0.00027067485],"domain_scores_gemma":[0.99351776,0.004391591,0.00090080895,0.00036427536,0.00056907744,0.00025648274],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0028752582,0.00025047245,0.00022510684,0.0013316714,0.0051606316,0.0053145136,0.00058588997,0.0010805108,0.004090923],"category_scores_gemma":[0.007998355,0.0002265957,0.00014792632,0.0013175983,0.0060903737,0.00456736,0.00317231,0.0016116473,0.0003333169],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048271613,0.000030754138,0.010255409,0.00007760178,0.0000046750833,0.00039277982,0.97038424,0.0000141805585,0.0022733642,0.008635764,0.000758989,0.007123952],"study_design_scores_gemma":[0.000008014013,0.000041807336,0.042147066,0.00015968173,0.000015591539,0.00019647539,0.9105111,0.0003794653,0.0013322918,0.0022316007,0.042953815,0.000023060435],"about_ca_topic_score_codex":0.018728811,"about_ca_topic_score_gemma":0.027773289,"teacher_disagreement_score":0.9948394,"about_ca_system_score_codex":0.004750307,"about_ca_system_score_gemma":0.0016589715,"threshold_uncertainty_score":0.03723961},"labels":[],"label_agreement":null},{"id":"W4241614355","doi":"10.22215/etd/2004-06120","title":"Hate expectations: a narrative of the conceptualisation of criminal hatred in Canada","year":2004,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Narrative; Criminology; Political science; Sociology; Psychology; Law; Philosophy; Linguistics","score_opus":0.010562601197539793,"score_gpt":0.22670876179529295,"score_spread":0.21614616059775316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241614355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72370285,0.0025199086,0.0024708933,0.09097307,0.00048669998,0.00013998964,0.0005417919,0.000049645194,0.1791152],"genre_scores_gemma":[0.98555875,0.00059109327,0.00019593742,0.002748425,0.000021853739,0.000023967488,0.000057666122,0.000039838626,0.010762422],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9953441,0.0011928207,0.00010087059,0.00042271466,0.0010761305,0.0018633932],"domain_scores_gemma":[0.9909519,0.0031370665,0.00061054813,0.00025051946,0.0028139676,0.0022359837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031122349,0.00066871545,0.0006557179,0.002433259,0.058885727,0.01782806,0.0040622302,0.007572796,0.0063720723],"category_scores_gemma":[0.011985478,0.0007469933,0.00048286904,0.00394382,0.03760834,0.0049201287,0.007823462,0.016992597,0.0002438233],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039062626,0.000037590104,0.00571296,0.00003785549,0.00001089734,0.00089689577,0.8505012,0.00036334703,0.00023195626,0.12878941,0.009437911,0.0039408784],"study_design_scores_gemma":[0.000007061386,0.000010665568,0.005617322,0.00012877448,0.000013610243,0.00011575616,0.9187753,0.00046312422,0.00018917544,0.0069537894,0.06764927,0.00007613812],"about_ca_topic_score_codex":0.99668926,"about_ca_topic_score_gemma":0.9970222,"teacher_disagreement_score":0.21067981,"about_ca_system_score_codex":0.21067981,"about_ca_system_score_gemma":0.21129787,"threshold_uncertainty_score":0.91549957},"labels":[],"label_agreement":null},{"id":"W4242009327","doi":"10.22329/il.v30i3.3034","title":"Argumentative Injustice","year":2010,"lang":"en","type":"article","venue":"Informal Logic","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Testimonial; Injustice; Argumentative; Credibility; Identity (music); Argument (complex analysis); Epistemology; Social psychology; Argumentation theory; Sociology; Psychology; Aesthetics; Philosophy; Advertising","score_opus":0.00834268729701935,"score_gpt":0.24001654643667752,"score_spread":0.23167385913965818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242009327","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25079924,0.007669619,0.16857237,0.053429216,0.0015276594,0.00032948243,0.00012199307,0.00038298147,0.5171674],"genre_scores_gemma":[0.9815091,0.0007498804,0.0060308306,0.003580648,0.0002854536,0.0000858278,0.000031738335,0.000045003224,0.0076814066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9778412,0.01300932,0.0010616439,0.0015455566,0.0053984,0.001143885],"domain_scores_gemma":[0.92353004,0.050393686,0.011276597,0.0091284225,0.0043345536,0.001336655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012064022,0.00070648035,0.0005349643,0.0029517806,0.0038986874,0.005959173,0.0018168731,0.0041881404,0.0045224386],"category_scores_gemma":[0.06711571,0.00032729396,0.000514452,0.001095571,0.017642109,0.007114123,0.0071925856,0.007918214,0.0008956565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067689565,0.00012086434,0.009599068,0.00032718867,0.0000657893,0.0025919338,0.06838038,0.00044608317,0.0018389827,0.86202586,0.0067419917,0.047794186],"study_design_scores_gemma":[0.00003640768,0.00013670647,0.011711425,0.0015092947,0.000091152506,0.0148607725,0.046806455,0.0028333801,0.0062501067,0.6745824,0.24105997,0.0001219275],"about_ca_topic_score_codex":0.00063860137,"about_ca_topic_score_gemma":0.00058344775,"teacher_disagreement_score":0.012064022,"about_ca_system_score_codex":0.0015864454,"about_ca_system_score_gemma":0.0009197429,"threshold_uncertainty_score":0.06380141},"labels":[],"label_agreement":null},{"id":"W4242207812","doi":"10.26686/wgtn.17007385","title":"Public nudity and the right to freedom of expression: Balancing competing interests","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Wellington City Council; Auckland Council","keywords":"Freedom of expression; Scope (computer science); Expression (computer science); Supreme court; Test (biology); Political science; Law; Human rights; Balancing test; Sociology; Law and economics; Computer science","score_opus":0.012832233074653407,"score_gpt":0.23201504713566143,"score_spread":0.21918281406100804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242207812","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0708212,0.0014775319,0.05111277,0.041177176,0.00018014447,0.00019524943,0.00005072377,0.000027838953,0.83495736],"genre_scores_gemma":[0.9796601,0.0003905425,0.004212639,0.002461255,0.00009540001,0.00018662763,0.000018545974,0.000018122784,0.012956639],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9653197,0.01899245,0.001083555,0.00304338,0.0074923644,0.0040685856],"domain_scores_gemma":[0.96392184,0.02643465,0.0022665344,0.00376271,0.0023731757,0.0012410058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027993478,0.00057177,0.0010545724,0.0014324997,0.008618077,0.016673623,0.0026185876,0.008489341,0.007988331],"category_scores_gemma":[0.046747133,0.00050838466,0.0007336847,0.0012178818,0.07848932,0.014706087,0.010420015,0.009667591,0.0006505173],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007957903,0.000008854058,0.00031299837,0.0000118253965,0.0000024513506,0.0000541848,0.0022845713,0.0001151897,0.000053734067,0.9938426,0.00029660072,0.0030089938],"study_design_scores_gemma":[0.000026681664,0.000038405367,0.001396967,0.00026288294,0.000015132196,0.00015796465,0.0069383057,0.0010268154,0.0005454221,0.9567967,0.03275064,0.000043937296],"about_ca_topic_score_codex":0.020132504,"about_ca_topic_score_gemma":0.024848133,"teacher_disagreement_score":0.027993478,"about_ca_system_score_codex":0.013606045,"about_ca_system_score_gemma":0.014305482,"threshold_uncertainty_score":0.14804548},"labels":[],"label_agreement":null},{"id":"W4242463716","doi":"10.1163/2210-7975_hrd-0129-20190004","title":"Hate Crimes and Hate Expression in Alberta and Canada","year":2020,"lang":"en","type":"dataset","venue":"Human Rights Documents online","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hate crime; Criminology; Expression (computer science); Political science; Law; Psychology; Computer science","score_opus":0.010164072309280651,"score_gpt":0.2512410969839097,"score_spread":0.24107702467462902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242463716","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012684753,0.0004513787,0.00008572792,0.00030776687,0.00004197859,0.000038668248,0.98367184,0.00018574395,0.0025321124],"genre_scores_gemma":[0.01701612,0.00038555032,0.00033407638,0.00010582208,0.000015315589,0.000065439766,0.97664136,0.000043476288,0.005392941],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991726,0.000055527264,0.000048163987,0.00015530293,0.0002825382,0.00028587578],"domain_scores_gemma":[0.9971426,0.0002535896,0.00017989382,0.00024398266,0.0016832227,0.00049675535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006697828,0.0014583152,0.0009516154,0.0051461374,0.0020636888,0.0020499523,0.0029056063,0.0016186953,0.010893636],"category_scores_gemma":[0.002831202,0.0005620776,0.0011412221,0.010490254,0.00090016914,0.0006791212,0.0012409325,0.0015444792,0.0076162852],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026900307,0.000089676025,0.031357285,0.000365871,0.00008020825,0.0001504988,0.00023465998,0.0017926433,0.00017738997,0.0010402655,0.95373297,0.010709468],"study_design_scores_gemma":[0.00030608234,0.000045993245,0.40339363,0.0007657311,0.0001597364,0.00027927817,0.0038765029,0.0072321044,0.00093418907,0.0011787029,0.5816666,0.00016139337],"about_ca_topic_score_codex":0.986072,"about_ca_topic_score_gemma":0.99305177,"teacher_disagreement_score":0.016007742,"about_ca_system_score_codex":0.016007742,"about_ca_system_score_gemma":0.022792174,"threshold_uncertainty_score":0.116144836},"labels":[],"label_agreement":null},{"id":"W4243015442","doi":"10.32920/14669178.v1","title":"Is offensive commenting contagious online? Examining public vs. interpersonal swearing in response to Donald Trump's YouTube campaign videos","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Offensive; Psychology; Interpersonal communication; Social psychology; Spillover effect; Emotional contagion; Prosocial behavior; Economics","score_opus":0.054377752861147,"score_gpt":0.27959727675669505,"score_spread":0.22521952389554806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243015442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99624026,0.00007787859,0.00028304016,0.00037508193,0.000018769366,0.000028724266,0.0002901499,0.000007638433,0.0026784965],"genre_scores_gemma":[0.9986339,0.000079832585,0.00015202547,0.00008972498,0.000032501564,0.000039170583,0.00024808143,0.000010080602,0.0007146074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970317,0.0013201965,0.00021740107,0.0004099705,0.00063546177,0.0003851735],"domain_scores_gemma":[0.9435705,0.02850026,0.019848946,0.0024159753,0.0035131942,0.0021511596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029510888,0.0002328182,0.000299917,0.0010109701,0.00088806136,0.0018493368,0.00046186533,0.0008506666,0.004430567],"category_scores_gemma":[0.045483503,0.00023277936,0.0004113935,0.0010186601,0.0007939129,0.0018191888,0.0017267689,0.001287723,0.00093045033],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001783658,0.0001393736,0.96294314,0.00010832338,0.00013726203,0.00023914802,0.020911321,0.00014105039,0.0009333817,0.00031759922,0.0010752884,0.012875785],"study_design_scores_gemma":[0.0000023482826,0.000057531888,0.98066956,0.000060100298,0.00003632493,0.00009723272,0.017257206,0.0005883828,0.00024955036,0.00011128199,0.00085496524,0.000015628897],"about_ca_topic_score_codex":0.011893121,"about_ca_topic_score_gemma":0.016848665,"teacher_disagreement_score":0.011893121,"about_ca_system_score_codex":0.00066647935,"about_ca_system_score_gemma":0.00043956836,"threshold_uncertainty_score":0.023647785},"labels":[],"label_agreement":null},{"id":"W4244299922","doi":"10.1108/oxan-es261983","title":"Canada violence increases focus on online platforms","year":2021,"lang":"en","type":"other","venue":"Emerald expert briefings","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Focus (optics); Internet privacy; Computer security; Computer science; Physics","score_opus":0.010788216892951763,"score_gpt":0.22555510074857846,"score_spread":0.2147668838556267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244299922","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08632088,0.0021700817,0.00050226646,0.20956494,0.0023481124,0.00030871257,0.014321597,0.00051308546,0.68395036],"genre_scores_gemma":[0.30979982,0.0035205476,0.000718118,0.08149961,0.0007055614,0.00017264641,0.0037738837,0.0002723795,0.59953743],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784255,0.000116619536,0.000030665014,0.00009597565,0.00086077367,0.0010533904],"domain_scores_gemma":[0.9911191,0.00064168393,0.00057618786,0.00011696603,0.0033759784,0.0041699437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081660034,0.0004111665,0.00024873615,0.0023872638,0.012315764,0.0045799734,0.0016001719,0.004860117,0.08118063],"category_scores_gemma":[0.0064014154,0.00042988552,0.000509687,0.0025451183,0.001643508,0.0014109751,0.0026971765,0.0036728196,0.0050437725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003857042,0.000077699806,0.022212073,0.00008608601,0.000014032557,0.0001861287,0.0014579325,0.000118959564,0.00018812007,0.0039780894,0.94275814,0.028884098],"study_design_scores_gemma":[0.00009079788,0.00006544667,0.23536614,0.0003290191,0.000057823996,0.00016375555,0.030995388,0.0005118281,0.0007210286,0.001877489,0.7297458,0.00007542628],"about_ca_topic_score_codex":0.9924616,"about_ca_topic_score_gemma":0.9979286,"teacher_disagreement_score":0.08118063,"about_ca_system_score_codex":0.0485895,"about_ca_system_score_gemma":0.12011141,"threshold_uncertainty_score":0.35254312},"labels":[],"label_agreement":null},{"id":"W4245102657","doi":"10.1057/978-1-137-39826-0_7","title":"Conclusion","year":2017,"lang":"en","type":"book-chapter","venue":"Palgrave Macmillan UK eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Order (exchange); Psychology; Sentiment analysis; Computer science; Linguistics; Natural language processing; Philosophy","score_opus":0.018520215019776635,"score_gpt":0.23474944568924117,"score_spread":0.21622923066946453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245102657","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005775777,0.007856678,0.011075034,0.089435495,0.014419697,0.00030524324,0.001370808,0.00044976326,0.86931145],"genre_scores_gemma":[0.06288947,0.011744021,0.009922607,0.04203406,0.0028681776,0.00038980637,0.002547843,0.00061960693,0.8669845],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758494,0.00046589453,0.00009463209,0.0004267551,0.0010858717,0.00034182807],"domain_scores_gemma":[0.9972255,0.00053442596,0.000094457,0.00023512353,0.0014740882,0.00043639002],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002690615,0.0006652495,0.00032063772,0.001043807,0.003297642,0.0067609814,0.0018032729,0.0030016333,0.1448359],"category_scores_gemma":[0.008535893,0.00024225039,0.0006536124,0.00096654106,0.0013378067,0.004184578,0.0032276728,0.0031299696,0.061565988],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007547382,0.00007163034,0.001319483,0.000698286,0.000011829443,0.00050233153,0.006150103,0.00031296356,0.0006331701,0.20056738,0.61700606,0.17265134],"study_design_scores_gemma":[0.0000032494231,0.000009823279,0.00037854476,0.00039077323,0.0000028391423,0.00011301485,0.0022593886,0.000043134678,0.0002154824,0.01049925,0.9860793,0.000005240831],"about_ca_topic_score_codex":0.0038182482,"about_ca_topic_score_gemma":0.0055291783,"teacher_disagreement_score":0.8551641,"about_ca_system_score_codex":0.0042889123,"about_ca_system_score_gemma":0.004441558,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4245437284","doi":"10.32920/14645577","title":"The Efficacy of Facebook as a Tool in Criminal Investigations","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Crowdsourcing; Context (archaeology); Upload; Criminal justice; Globe; Cyberpsychology; Psychology; Media studies; Internet privacy; Sociology; Computer science; Criminology; World Wide Web; History","score_opus":0.02649786554716767,"score_gpt":0.2683834848162736,"score_spread":0.24188561926910593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245437284","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8622102,0.0022334924,0.0016472996,0.010077119,0.00029177786,0.00031372593,0.000056442852,0.00007232313,0.12309759],"genre_scores_gemma":[0.9955265,0.00083664356,0.00072167907,0.0005776276,0.000090415946,0.00018759919,0.000016288914,0.000020442121,0.0020228105],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9174136,0.07032643,0.0013520226,0.0018393294,0.007744774,0.0013237925],"domain_scores_gemma":[0.71026856,0.24612747,0.02201283,0.009341209,0.008591999,0.0036579536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037649155,0.00043019318,0.00037704257,0.0033015967,0.008345946,0.010624089,0.0016091664,0.0019100879,0.0036007364],"category_scores_gemma":[0.1629636,0.0005437253,0.0003847464,0.0013595853,0.01261347,0.0073248693,0.0070665046,0.0024523127,0.00095576415],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033943405,0.0008307507,0.08941238,0.0009878479,0.00011526665,0.00061934517,0.6754365,0.00020667602,0.001000189,0.0292936,0.010271137,0.19148684],"study_design_scores_gemma":[0.00009323809,0.0014029203,0.15826198,0.0040893205,0.00020279836,0.0008758086,0.6815243,0.0019190905,0.002696816,0.009102515,0.13962317,0.00020806065],"about_ca_topic_score_codex":0.0035207658,"about_ca_topic_score_gemma":0.004374976,"teacher_disagreement_score":0.037649155,"about_ca_system_score_codex":0.0032477477,"about_ca_system_score_gemma":0.0027109215,"threshold_uncertainty_score":0.19911015},"labels":[],"label_agreement":null},{"id":"W4245705942","doi":"10.5325/jinfopoli.10.1.0329","title":"Conducting Critical Analysis on International Communication Rights Standards: The Contributions of Graphical Knowledge Modeling","year":2020,"lang":"en","type":"article","venue":"Journal of Information Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université TÉLUQ","funders":"","keywords":"Human rights; Normative; Fundamental rights; International human rights law; Political science; Context (archaeology); Sociology; Law and economics; Law; Engineering ethics; Public relations; Engineering","score_opus":0.029483536858032452,"score_gpt":0.33810743196171616,"score_spread":0.3086238951036837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245705942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19828652,0.0022006454,0.59459895,0.06639144,0.000844659,0.0012520379,0.00025494103,0.00036441398,0.13580634],"genre_scores_gemma":[0.8997694,0.0006043188,0.09559451,0.0012612069,0.00012948968,0.0007910456,0.0000740113,0.00011637685,0.0016596372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8027648,0.17037046,0.0038395235,0.004833118,0.016401859,0.0017901685],"domain_scores_gemma":[0.40950787,0.5182416,0.014228684,0.031401012,0.025358934,0.0012618154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15329763,0.00090100145,0.000980394,0.014030917,0.007610611,0.019480214,0.0028786985,0.0028401928,0.0040202094],"category_scores_gemma":[0.27431342,0.0008077714,0.0007886047,0.0066915257,0.05155973,0.028724449,0.01123025,0.00582222,0.000374544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003969443,0.000042022024,0.002021876,0.00016806694,0.000017566552,0.00009650002,0.06792886,0.00094561605,0.00025652285,0.899258,0.0013078386,0.027917432],"study_design_scores_gemma":[0.000023750856,0.000048210404,0.0014933286,0.0012986736,0.000034789853,0.000111432586,0.09968226,0.009017577,0.001963242,0.8465324,0.039737422,0.00005684113],"about_ca_topic_score_codex":0.0037925886,"about_ca_topic_score_gemma":0.004011453,"teacher_disagreement_score":0.15329763,"about_ca_system_score_codex":0.016257834,"about_ca_system_score_gemma":0.016687967,"threshold_uncertainty_score":0.8107251},"labels":[],"label_agreement":null},{"id":"W4245979527","doi":"10.2307/3504637","title":"10.2307/3504637","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political philosophy; Philosophy of law; Free speech; Private speech; Sociology; Sociolinguistics; Political science; Psychology; Law; Politics; Linguistics; Social psychology; Public law; Philosophy","score_opus":0.004003883792755175,"score_gpt":0.16186622567573858,"score_spread":0.1578623418829834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245979527","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035044816,0.00039167938,0.00058173604,0.00032031417,0.00050341355,0.00006651305,0.0011504239,0.001007897,0.99562764],"genre_scores_gemma":[0.0010460165,0.00030292338,0.00043555952,0.0003733939,0.000104549035,0.000031187505,0.0012017046,0.00038124248,0.9961234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993932,0.00005000142,0.00004944194,0.00017062324,0.00024393987,0.00009273707],"domain_scores_gemma":[0.9977247,0.0004698415,0.0000902155,0.0004141144,0.00060817495,0.0006929551],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00083657284,0.0017480289,0.0011576423,0.0023157783,0.0016220261,0.00495242,0.001810237,0.002655146,0.9844475],"category_scores_gemma":[0.0020930914,0.0007906307,0.0007867823,0.0024196198,0.0015822079,0.0051522246,0.0039766002,0.0016060898,0.9859341],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017539157,0.00014412054,0.0004694779,0.00036852306,0.000015786823,0.00021089384,0.000079738384,0.00016984837,0.0018675554,0.004776669,0.5820471,0.40967494],"study_design_scores_gemma":[0.000022677472,0.000036066005,0.0006989495,0.00015418373,0.0000059197905,0.00022120136,0.00010921622,0.00012765736,0.00027190155,0.0009659255,0.9973705,0.000015794933],"about_ca_topic_score_codex":0.0035722416,"about_ca_topic_score_gemma":0.003844041,"teacher_disagreement_score":0.015552521,"about_ca_system_score_codex":0.0010344465,"about_ca_system_score_gemma":0.0012355715,"threshold_uncertainty_score":0.022183716},"labels":[],"label_agreement":null},{"id":"W4247713031","doi":"10.4018/978-1-59904-937-3.ch034","title":"Digital Morality and Ethics","year":2008,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Morality; Information ethics; Ethics of technology; Government (linguistics); Political science; Engineering ethics; Computer ethics; Democracy; Sociology; Applied ethics; Environmental ethics; Meta-ethics; Law; Engineering; Politics; Philosophy","score_opus":0.030834028303976514,"score_gpt":0.2498367115016726,"score_spread":0.21900268319769609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247713031","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029929813,0.019912628,0.008171953,0.021016385,0.00078452396,0.000042671054,0.00005675912,0.000045010514,0.9469771],"genre_scores_gemma":[0.44410932,0.045828875,0.01392839,0.017024148,0.0029560307,0.00032462255,0.00021402983,0.00016609454,0.47544846],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981458,0.0009502747,0.00006458005,0.0002143509,0.0004763685,0.00014865406],"domain_scores_gemma":[0.9990176,0.0005467775,0.00007060811,0.00014115286,0.00013245222,0.000091379436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014684623,0.00049440644,0.00028073508,0.00093139015,0.0025628936,0.008009451,0.00060350745,0.0033259287,0.009895017],"category_scores_gemma":[0.0025123358,0.00019599455,0.00022338376,0.0008601549,0.017345933,0.0061306176,0.0026494975,0.0043661282,0.0024889247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011819155,0.0000050692993,0.00003384424,0.000028274228,9.861525e-7,0.00001478668,0.0006799512,0.000092331924,0.000030381878,0.9837818,0.0076175807,0.007713886],"study_design_scores_gemma":[0.0000028375268,0.000006045746,0.00014105775,0.0002200644,0.0000015560335,0.00008518167,0.00062021246,0.00017695205,0.000077277575,0.5463568,0.4523066,0.0000053278573],"about_ca_topic_score_codex":0.0016176099,"about_ca_topic_score_gemma":0.0012750869,"teacher_disagreement_score":0.009895017,"about_ca_system_score_codex":0.0038916662,"about_ca_system_score_gemma":0.0024207896,"threshold_uncertainty_score":0.033102155},"labels":[],"label_agreement":null},{"id":"W4248178748","doi":"10.4018/978-1-60566-060-8.ch165","title":"Ethical, Cultural and Socio-Economic Factors of Software Piracy Determinants in a Developing Country","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Poverty; Empirical research; Developing country; Software; Norm (philosophy); Political science; Public relations; Economic growth; Computer science; Economics; Law","score_opus":0.025185774301449467,"score_gpt":0.27364047383478723,"score_spread":0.24845469953333776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248178748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99706984,0.00014035079,0.000056476663,0.00020218997,0.0000032238179,0.0000071816603,0.00002042488,0.0000011042965,0.0024992044],"genre_scores_gemma":[0.9993094,0.00025506332,0.000081936545,0.000026542823,0.0000032513205,0.0000048839906,0.000016230222,9.552978e-7,0.00030173073],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9995142,0.00022319843,0.000029042334,0.000041509364,0.00007435808,0.0001177036],"domain_scores_gemma":[0.99636,0.0017706063,0.00098869,0.000110080495,0.00024369542,0.0005269095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007963777,0.00017142062,0.0001906166,0.0008592268,0.0015515214,0.0014302051,0.00023741492,0.00031581905,0.0035705685],"category_scores_gemma":[0.003354196,0.00018686877,0.00025024856,0.0010749353,0.0015314843,0.0008739254,0.0010510619,0.00082175113,0.0002884095],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027911683,0.00021485036,0.9824746,0.00002692817,0.000015329415,0.0004473152,0.006517811,0.000086548906,0.00013751787,0.00069848524,0.00031448025,0.009038261],"study_design_scores_gemma":[0.0000023389482,0.0000618899,0.9768683,0.000055165892,0.000015533833,0.00030353368,0.021095192,0.00028625733,0.00008025442,0.00026493848,0.00095815794,0.000008383032],"about_ca_topic_score_codex":0.012985712,"about_ca_topic_score_gemma":0.019363543,"teacher_disagreement_score":0.012985712,"about_ca_system_score_codex":0.0006693617,"about_ca_system_score_gemma":0.0009824276,"threshold_uncertainty_score":0.025820255},"labels":[],"label_agreement":null},{"id":"W4248491679","doi":"10.18653/v1/w19-35","title":"Proceedings of the Third Workshop on Abusive Language Online","year":2019,"lang":"en","type":"paratext","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Computer science","score_opus":0.011054449156143604,"score_gpt":0.24935286076063112,"score_spread":0.23829841160448753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248491679","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13387437,0.031494927,0.081054434,0.13384481,0.04473999,0.0011225761,0.008095457,0.003827099,0.5619464],"genre_scores_gemma":[0.33225387,0.014020687,0.027380865,0.013358291,0.008181998,0.0007720642,0.011896881,0.0025010835,0.5896342],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962978,0.0014837933,0.0001670677,0.00047462073,0.0011283649,0.00044826232],"domain_scores_gemma":[0.9868788,0.006179455,0.00041309572,0.0018956581,0.0028143937,0.0018185592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063589076,0.0009555111,0.00091205304,0.002217991,0.0032592816,0.009592652,0.0019034813,0.002521796,0.05146991],"category_scores_gemma":[0.014815096,0.0004901869,0.00055203337,0.00162313,0.0023655738,0.0075810216,0.0053252396,0.0038103773,0.012869446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006092796,0.0009310099,0.007938756,0.00048322737,0.000048842085,0.00055965566,0.012304219,0.0003767012,0.0047845375,0.018657282,0.52369094,0.42961553],"study_design_scores_gemma":[0.00003286151,0.00010159252,0.007984088,0.0004549274,0.000051561186,0.00024611654,0.009801546,0.001567283,0.0029989083,0.008901548,0.96781677,0.000042779655],"about_ca_topic_score_codex":0.011626499,"about_ca_topic_score_gemma":0.023944475,"teacher_disagreement_score":0.05146991,"about_ca_system_score_codex":0.0015688317,"about_ca_system_score_gemma":0.0030306561,"threshold_uncertainty_score":0.17218399},"labels":[],"label_agreement":null},{"id":"W4249586274","doi":"10.26686/wgtn.17006713","title":"The Limits of Free Speech: Democratic Legitimacy in Canada and New Zealand","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Democracy; Legitimacy; Democratic legitimacy; Free speech; Order (exchange); Law and economics; Citizen journalism; Political science; Law; Sociology; Politics; Economics","score_opus":0.012689727810039351,"score_gpt":0.2215647601747734,"score_spread":0.20887503236473404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249586274","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7977036,0.008398169,0.001233363,0.05039271,0.00032874735,0.00015994097,0.00028293804,0.000030222971,0.14147036],"genre_scores_gemma":[0.9847371,0.0027864315,0.0005286898,0.0009835657,0.000029121788,0.0000259591,0.000053061303,0.0000209593,0.010835104],"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99252456,0.0009974439,0.00024993788,0.00074406294,0.003036661,0.0024474708],"domain_scores_gemma":[0.9890382,0.0026735945,0.0013947806,0.00050881103,0.0035615675,0.0028229188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050206496,0.00038344928,0.00071071065,0.0028947708,0.019412644,0.009617134,0.0016164614,0.0017145746,0.0043740226],"category_scores_gemma":[0.015827019,0.0005344208,0.00041280538,0.004379171,0.01888242,0.0035845612,0.0048578763,0.0043126196,0.00022650757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033298437,0.00018065308,0.084772326,0.00043809265,0.000081478305,0.0027645642,0.5244351,0.0007746594,0.0016166026,0.22151169,0.028328493,0.13476336],"study_design_scores_gemma":[0.000071453775,0.00007923798,0.30567303,0.00085607386,0.00009889495,0.00054625585,0.4521709,0.0017807399,0.00093833613,0.019049508,0.2184178,0.00031767882],"about_ca_topic_score_codex":0.9965065,"about_ca_topic_score_gemma":0.9984493,"teacher_disagreement_score":0.1408069,"about_ca_system_score_codex":0.1408069,"about_ca_system_score_gemma":0.1805623,"threshold_uncertainty_score":0.9965422},"labels":[],"label_agreement":null},{"id":"W4251794571","doi":"10.15353/cgjsc.v5i1.3741","title":"Publication Bans in a Facebook Age: How Internet Vigilantes Have Challenged the Youth Criminal Justice Act’s “Secrecy Laws” Following the 2011 Vancouver Stanley Cup Riot","year":2016,"lang":"en","type":"article","venue":"Canadian Graduate Journal of Sociology and Criminology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"The Internet; Secrecy; Criminology; Economic Justice; Social media; Political science; Shame; Law; Criminal justice; Internet privacy; Anonymity; Sociology; Computer science; World Wide Web","score_opus":0.09970350844329162,"score_gpt":0.2583551956255781,"score_spread":0.1586516871822865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251794571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92124146,0.0012439154,0.0005127448,0.025357293,0.0004237516,0.00007658802,0.000051044997,0.0000550911,0.05103814],"genre_scores_gemma":[0.98309547,0.0009448778,0.00023981286,0.0061306055,0.00012691975,0.000037988837,0.000039173654,0.000048501704,0.009336591],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98826027,0.0053415513,0.00042486686,0.00082148553,0.0026305267,0.0025213384],"domain_scores_gemma":[0.97753793,0.009117678,0.0034116898,0.0015606378,0.003840894,0.0045312084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008975978,0.00029627924,0.00047449503,0.0019427892,0.037912928,0.01674398,0.0027771099,0.0052207992,0.0038059186],"category_scores_gemma":[0.033526257,0.0007702135,0.00036705713,0.0017009791,0.01647593,0.0059977593,0.008533677,0.0084867915,0.0006274954],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004526371,0.00015578994,0.025427008,0.000076048054,0.00001721203,0.0029435211,0.9116287,0.00008037977,0.0005636048,0.018711891,0.013944419,0.026406113],"study_design_scores_gemma":[0.000013049746,0.00008550992,0.02532677,0.0004797191,0.00003644876,0.00078274333,0.8332543,0.00024678782,0.0005977425,0.0022510933,0.13685504,0.00007086237],"about_ca_topic_score_codex":0.43941632,"about_ca_topic_score_gemma":0.602242,"teacher_disagreement_score":0.5605837,"about_ca_system_score_codex":0.015876302,"about_ca_system_score_gemma":0.017631153,"threshold_uncertainty_score":0.87371725},"labels":[],"label_agreement":null},{"id":"W4252044959","doi":"10.22215/etd/2015-10861","title":"The Demise of Section 13 of the Canadian Human Rights Act: Reappraising the 'Battle for Free Speech'","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Demise; Repeal; Ideology; Battle; Liberalism; Mainstream; Political science; Section (typography); Free speech; Human rights; Law; Democracy; History; Politics; Advertising","score_opus":0.016582193509117486,"score_gpt":0.27148328919962456,"score_spread":0.25490109569050706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252044959","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14717326,0.012663156,0.0059711155,0.304179,0.0036954596,0.00015278782,0.0006071414,0.00021263103,0.5253455],"genre_scores_gemma":[0.8282401,0.0051967055,0.003132129,0.05866496,0.00035088504,0.000045379133,0.00020375563,0.0001229026,0.10404314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98804164,0.0011071953,0.00015947768,0.0006307982,0.007350246,0.002710627],"domain_scores_gemma":[0.9887764,0.004169397,0.0005860772,0.0005133586,0.0041085915,0.0018461121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0081732385,0.00036656403,0.00030356497,0.0016828778,0.023241958,0.010844612,0.001401876,0.0046413746,0.0049221884],"category_scores_gemma":[0.016608186,0.0003645586,0.00040325773,0.0019738993,0.023276487,0.0031045203,0.0030834486,0.010107744,0.00057454954],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005360426,0.00003224589,0.0061683245,0.00015202662,0.000017545588,0.0004063569,0.09813602,0.00029567338,0.0016938336,0.6642794,0.14029466,0.08847027],"study_design_scores_gemma":[0.000014881759,0.000039890878,0.026878562,0.0004949177,0.000026489355,0.00018216384,0.0424763,0.00043083055,0.0017879923,0.021058993,0.9064481,0.0001609451],"about_ca_topic_score_codex":0.9719163,"about_ca_topic_score_gemma":0.9848918,"teacher_disagreement_score":0.065446205,"about_ca_system_score_codex":0.065446205,"about_ca_system_score_gemma":0.1651381,"threshold_uncertainty_score":0.47484767},"labels":[],"label_agreement":null},{"id":"W4252351349","doi":"10.32920/14669178","title":"Is offensive commenting contagious online? Examining public vs. interpersonal swearing in response to Donald Trump's YouTube campaign videos","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Offensive; Psychology; Social psychology; Interpersonal communication; Emotional contagion; Spillover effect; Economics","score_opus":0.054377752861147,"score_gpt":0.27959727675669505,"score_spread":0.22521952389554806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252351349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99624026,0.00007787859,0.00028304016,0.00037508193,0.000018769366,0.000028724266,0.0002901499,0.000007638433,0.0026784965],"genre_scores_gemma":[0.9986339,0.000079832585,0.00015202547,0.00008972498,0.000032501564,0.000039170583,0.00024808143,0.000010080602,0.0007146074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970317,0.0013201965,0.00021740107,0.0004099705,0.00063546177,0.0003851735],"domain_scores_gemma":[0.9435705,0.02850026,0.019848946,0.0024159753,0.0035131942,0.0021511596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029510888,0.0002328182,0.000299917,0.0010109701,0.00088806136,0.0018493368,0.00046186533,0.0008506666,0.004430567],"category_scores_gemma":[0.045483503,0.00023277936,0.0004113935,0.0010186601,0.0007939129,0.0018191888,0.0017267689,0.001287723,0.00093045033],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001783658,0.0001393736,0.96294314,0.00010832338,0.00013726203,0.00023914802,0.020911321,0.00014105039,0.0009333817,0.00031759922,0.0010752884,0.012875785],"study_design_scores_gemma":[0.0000023482826,0.000057531888,0.98066956,0.000060100298,0.00003632493,0.00009723272,0.017257206,0.0005883828,0.00024955036,0.00011128199,0.00085496524,0.000015628897],"about_ca_topic_score_codex":0.011893121,"about_ca_topic_score_gemma":0.016848665,"teacher_disagreement_score":0.011893121,"about_ca_system_score_codex":0.00066647935,"about_ca_system_score_gemma":0.00043956836,"threshold_uncertainty_score":0.023647785},"labels":[],"label_agreement":null},{"id":"W4253714495","doi":"10.31234/osf.io/a9h83","title":"Moral Language Use by U.S. Political Elites","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Politics; Power (physics); Political science; Democracy; Period (music); Presidential system; Presidential election; Law; Political economy; Sociology; Philosophy; Aesthetics","score_opus":0.027952452939820724,"score_gpt":0.25769597202601613,"score_spread":0.2297435190861954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253714495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99808264,0.000047587408,0.000094582734,0.00011159236,0.000003925467,0.000005608651,0.00030123934,0.0000052880896,0.0013476079],"genre_scores_gemma":[0.9987967,0.00004521398,0.00008357873,0.000049026028,0.000011483142,0.000011188832,0.00050905405,0.0000046808877,0.00048910984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937576,0.00023487175,0.000035906152,0.00013045208,0.0000967737,0.000126324],"domain_scores_gemma":[0.9967361,0.0012291447,0.0009892151,0.00021432168,0.00036079076,0.00047053586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001374561,0.0002147376,0.00029097366,0.0023986606,0.0008485128,0.0020846783,0.00021724842,0.00045557768,0.0032916572],"category_scores_gemma":[0.0054060053,0.00015337918,0.00025879528,0.0016644425,0.00055277,0.001267277,0.0013018226,0.00060186203,0.00072810316],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018946677,0.0002491886,0.9702966,0.000037046877,0.00007634698,0.00008269016,0.0071881316,0.0005024163,0.001073035,0.0007163028,0.00160894,0.017979804],"study_design_scores_gemma":[0.000023937037,0.00014468339,0.9815693,0.00003171151,0.000036604302,0.00007323329,0.009341844,0.005017141,0.00040804743,0.0006750032,0.0026559525,0.00002251204],"about_ca_topic_score_codex":0.0128191495,"about_ca_topic_score_gemma":0.015060633,"teacher_disagreement_score":0.0128191495,"about_ca_system_score_codex":0.00055404246,"about_ca_system_score_gemma":0.00029465076,"threshold_uncertainty_score":0.025489032},"labels":[],"label_agreement":null},{"id":"W4280616805","doi":"10.18280/ria.360201","title":"Design and Implementation of a Multichannel Convolutional Neural Network for Hate Speech Detection in Social Networks","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Addis Ababa University","keywords":"Convolutional neural network; Computer science; Support vector machine; Deep learning; Artificial intelligence; Speech recognition; Language model; Channel (broadcasting); Voice activity detection; Machine learning; Artificial neural network; Speech processing","score_opus":0.03709172516819915,"score_gpt":0.2825796487874865,"score_spread":0.24548792361928734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280616805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1137327,0.0007360333,0.8721201,0.0006348945,0.00026154827,0.0005004613,0.000348436,0.0051536844,0.0065121907],"genre_scores_gemma":[0.8341594,0.00031023423,0.15936568,0.00024211801,0.000034602133,0.000445655,0.00037839636,0.000059868435,0.0050040986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976355,0.000026206142,0.000011193129,0.00007719445,0.000059107613,0.00006269727],"domain_scores_gemma":[0.99977964,0.000035089466,0.000022984555,0.000019271942,0.00011699089,0.000025909525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045687694,0.0008040265,0.00041929563,0.00047686484,0.00038992547,0.00054116966,0.0017135865,0.00085405866,0.002052487],"category_scores_gemma":[0.00065955054,0.00043331744,0.00042301146,0.00024486,0.00032589442,0.00078917487,0.000555768,0.00075269083,0.0005557037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006364945,0.00050816324,0.0070295334,0.00026225072,0.00020727879,0.00039396773,0.00009779762,0.5205677,0.056083497,0.005392216,0.00673574,0.40208536],"study_design_scores_gemma":[0.00000887689,0.000072833165,0.00034661504,0.0000052869013,0.000014089869,0.000021034866,0.0000061624974,0.9918234,0.006889403,0.00027168402,0.0005334817,0.000007149747],"about_ca_topic_score_codex":0.017230056,"about_ca_topic_score_gemma":0.018752977,"teacher_disagreement_score":0.017230056,"about_ca_system_score_codex":0.0014882499,"about_ca_system_score_gemma":0.0012761379,"threshold_uncertainty_score":0.034259558},"labels":[],"label_agreement":null},{"id":"W4282023445","doi":"10.3390/rel13060521","title":"Hashtagged Trolling and Emojified Hate against Muslims on Social Media","year":2022,"lang":"en","type":"article","venue":"Religions","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Islam; Social media; Christianity; Aside; Exploratory research; Media studies; Sociology; Religious studies; Political science; Law; Theology; Social science; Literature; Philosophy; Art","score_opus":0.023275526384770685,"score_gpt":0.2308301500147083,"score_spread":0.20755462362993762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282023445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99372077,0.00007568491,0.00043061702,0.000052081014,0.000017104227,0.000034173907,0.00024270128,0.000014123206,0.005412876],"genre_scores_gemma":[0.99624455,0.00012528182,0.0006066064,0.000069734895,0.000035206307,0.000028305147,0.00025680388,0.000009109455,0.00262451],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991947,0.00034397317,0.000045948895,0.00008923249,0.00021894791,0.000107229615],"domain_scores_gemma":[0.9951375,0.002385218,0.0013639772,0.0002848832,0.0006342733,0.00019413028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072267756,0.00033730682,0.00020384093,0.0014651058,0.0007419261,0.0012435806,0.00020721402,0.0003932197,0.0028223929],"category_scores_gemma":[0.0048055844,0.00012574164,0.00016513802,0.0008554558,0.00049197924,0.0013410577,0.000811458,0.00042632324,0.0011917237],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000759902,0.0005415642,0.77033454,0.00083856226,0.00015548707,0.001142172,0.07479522,0.00030396675,0.027573125,0.0018095223,0.0030320792,0.118713826],"study_design_scores_gemma":[0.0000065663453,0.0004545634,0.90102726,0.00022821358,0.00009855837,0.0010272173,0.07207355,0.0021689504,0.010183117,0.00041401695,0.012250953,0.00006710773],"about_ca_topic_score_codex":0.0014207495,"about_ca_topic_score_gemma":0.0034169692,"teacher_disagreement_score":0.0028223929,"about_ca_system_score_codex":0.0003020125,"about_ca_system_score_gemma":0.00014334309,"threshold_uncertainty_score":0.009441853},"labels":[],"label_agreement":null},{"id":"W4282036346","doi":"10.1016/j.jebo.2022.05.014","title":"How racial animus forms and spreads: Evidence from the coronavirus pandemic","year":2022,"lang":"en","type":"article","venue":"Journal of Economic Behavior & Organization","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pandemic; Epithet; Salience (neuroscience); Coronavirus disease 2019 (COVID-19); China; Coronavirus; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Political science; Criminology; History; Psychology; Law; Medicine","score_opus":0.033400482040784184,"score_gpt":0.25328925810339153,"score_spread":0.21988877606260734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282036346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902699,0.00056712807,0.00017780592,0.002223065,0.000034767272,0.000017056766,0.0004022778,0.000004655804,0.006303299],"genre_scores_gemma":[0.9982205,0.0005369469,0.00012031985,0.0004062972,0.00005071245,0.000013480164,0.00024521592,0.00000826695,0.00039825673],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99865794,0.0007511106,0.00005311586,0.00017663073,0.000115836156,0.0002454259],"domain_scores_gemma":[0.9873046,0.0056770113,0.0037543713,0.001200665,0.0011355685,0.00092774635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021654733,0.00016967895,0.00021672045,0.0011042678,0.001701406,0.0016066448,0.0005076281,0.0010219767,0.0042211604],"category_scores_gemma":[0.016567715,0.00028312788,0.00025442333,0.0010350114,0.001662621,0.0018915596,0.0018285346,0.0018324109,0.0007646686],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075996993,0.00043015496,0.9251918,0.0001665159,0.00017092704,0.0006793583,0.031200375,0.0003264011,0.00083985896,0.0031797856,0.0070006703,0.030054271],"study_design_scores_gemma":[0.00003328397,0.00013302774,0.96100086,0.00014911576,0.00005185812,0.0002744354,0.02770406,0.0006880323,0.0002589029,0.0013470607,0.008322206,0.000037113245],"about_ca_topic_score_codex":0.022382783,"about_ca_topic_score_gemma":0.025053754,"teacher_disagreement_score":0.022382783,"about_ca_system_score_codex":0.0005634312,"about_ca_system_score_gemma":0.00056988455,"threshold_uncertainty_score":0.044505},"labels":[],"label_agreement":null},{"id":"W4282961510","doi":"10.1002/cl2.1245","title":"PROTOCOL: Hate online and in traditional media: A systematic review of the evidence for associations or impacts on individuals, audiences, and communities","year":2022,"lang":"en","type":"review","venue":"Campbell Systematic Reviews","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université du Québec à Montréal","funders":"","keywords":"Protocol (science); Promotion (chess); Ideology; Psychology; Media content; Consumption (sociology); Empirical evidence; Social psychology; Empirical research; Systematic review; Public relations; Political science; Sociology; Social science; Computer science; MEDLINE; Law; Medicine; Alternative medicine; Multimedia","score_opus":0.35732396157604923,"score_gpt":0.4034717106940373,"score_spread":0.04614774911798808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282961510","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056128495,0.0028615254,0.0019335716,0.0014385772,0.00067026756,0.97778773,0.013726064,0.00020242555,0.00081851153],"genre_scores_gemma":[0.00080102717,0.0012390104,0.0043674954,0.00040126216,0.000041419546,0.9918898,0.00074525,0.000019952065,0.0004948308],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.90738696,0.03698115,0.035023596,0.005914417,0.011757619,0.002936239],"domain_scores_gemma":[0.8469505,0.055863682,0.029759476,0.01549998,0.04779922,0.004127222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.16031417,0.0046621677,0.013985362,0.015875729,0.0050229323,0.009302706,0.005043868,0.00896335,0.08544957],"category_scores_gemma":[0.2572772,0.0049659805,0.0113125965,0.016386047,0.0066046407,0.009949628,0.0065052807,0.0076708086,0.013671688],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033986515,0.0001373985,0.000826022,0.89292574,0.0021862267,0.00027246267,0.0024126023,0.00056143035,0.00092961255,0.005490492,0.05548672,0.03537268],"study_design_scores_gemma":[0.017290402,0.000997769,0.0048678135,0.7968785,0.007935911,0.00031150572,0.0027938855,0.0007742999,0.0018684311,0.010436668,0.15536432,0.00048039583],"about_ca_topic_score_codex":0.011217015,"about_ca_topic_score_gemma":0.016762966,"teacher_disagreement_score":0.16031417,"about_ca_system_score_codex":0.021771863,"about_ca_system_score_gemma":0.11660998,"threshold_uncertainty_score":0.8478325},"labels":[],"label_agreement":null},{"id":"W4283170666","doi":"10.1145/3531146.3533088","title":"Taxonomy of Risks posed by Language Models","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":604,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Misinformation; Taxonomy (biology); Harm; Risk analysis (engineering); Futures studies; Computer science; Risk management; Data science; Management science; Knowledge management; Engineering ethics; Psychology; Business; Artificial intelligence; Computer security; Social psychology; Engineering","score_opus":0.07387969715411008,"score_gpt":0.28462327550732947,"score_spread":0.21074357835321939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283170666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1465897,0.0081715,0.63808537,0.05345459,0.0005182718,0.0010008927,0.0005506115,0.0012673405,0.15036166],"genre_scores_gemma":[0.8288844,0.005505418,0.15217103,0.002587718,0.00040074904,0.00088572805,0.00055626186,0.000238929,0.008769788],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92419946,0.031843413,0.00656043,0.0033816462,0.030202093,0.0038129769],"domain_scores_gemma":[0.8756186,0.07425313,0.013977911,0.016932413,0.016523054,0.0026949926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03159864,0.0018763024,0.0008698409,0.008971202,0.007098341,0.012589732,0.0032737558,0.0079946965,0.003166432],"category_scores_gemma":[0.08222337,0.0010322748,0.0021426345,0.0035229765,0.016332833,0.023881076,0.013232015,0.0077935415,0.00079194497],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007163408,0.00009699293,0.01173439,0.00038479763,0.000065738954,0.0009807671,0.016193341,0.009468224,0.0007324404,0.90893316,0.0029164169,0.048422035],"study_design_scores_gemma":[0.000020842303,0.00010813767,0.002261511,0.0011065885,0.00008880118,0.0024683243,0.011284185,0.025179248,0.001473445,0.8954215,0.06045306,0.00013430846],"about_ca_topic_score_codex":0.0048475047,"about_ca_topic_score_gemma":0.002538707,"teacher_disagreement_score":0.03159864,"about_ca_system_score_codex":0.0069292015,"about_ca_system_score_gemma":0.0066742403,"threshold_uncertainty_score":0.16711158},"labels":[],"label_agreement":null},{"id":"W4285035410","doi":"10.20355/jcie29489","title":"Perspectives of Muslim and Minority Canadian Youth on Hate Speech and Social Media","year":2022,"lang":"en","type":"article","venue":"Journal of Contemporary Issues in Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Identity (music); Alienation; Mental health; Psychology; Immigration; Sociology; Social media; Intersectionality; Social psychology; Criminology; Gender studies; Political science; Law","score_opus":0.019457837843075466,"score_gpt":0.2627267201496023,"score_spread":0.24326888230652682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285035410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9785953,0.0015127377,0.00012372572,0.003640995,0.00013697123,0.00003814214,0.00016182539,0.000010499588,0.015779814],"genre_scores_gemma":[0.9937663,0.0015281813,0.00011525558,0.0008846422,0.00003496134,0.000015149244,0.00006238705,0.000009759209,0.0035834142],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99705327,0.0006612118,0.000059934453,0.00014144936,0.0006790955,0.001404961],"domain_scores_gemma":[0.99625874,0.0005504106,0.00037360605,0.000077989294,0.0009840004,0.0017553433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025865568,0.00081945217,0.00065464573,0.00243104,0.03139677,0.007518088,0.0011882291,0.0012875844,0.0035188526],"category_scores_gemma":[0.0034316843,0.00040160582,0.0005844727,0.002760993,0.008660166,0.0017115265,0.0049840594,0.003421954,0.00030740583],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040352432,0.000036850863,0.030874157,0.00006728418,0.000010606594,0.00091823156,0.9561909,0.000020951105,0.0005296363,0.0013150872,0.002283317,0.007712588],"study_design_scores_gemma":[0.0000013117951,0.000013235919,0.010331622,0.00005420954,0.000008991481,0.00017382426,0.9827959,0.000016271284,0.00007964905,0.000052469582,0.0064567993,0.00001571572],"about_ca_topic_score_codex":0.93353105,"about_ca_topic_score_gemma":0.9644672,"teacher_disagreement_score":0.066468954,"about_ca_system_score_codex":0.024063544,"about_ca_system_score_gemma":0.02989853,"threshold_uncertainty_score":0.17459404},"labels":[],"label_agreement":null},{"id":"W4285035695","doi":"10.22215/etd/2022-15070","title":"Cyberbullying Detection using Ensemble Method","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Support vector machine; Ensemble learning; Artificial intelligence; Machine learning; Feature extraction; Social media; Word (group theory); Feature (linguistics); Pattern recognition (psychology); Data mining; Mathematics; World Wide Web","score_opus":0.018401508290115696,"score_gpt":0.3032037769450274,"score_spread":0.2848022686549117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285035695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3281722,0.0022238598,0.65463257,0.00066346774,0.00068555673,0.00036687474,0.0024894257,0.004352325,0.006413655],"genre_scores_gemma":[0.8420177,0.0006431812,0.1447973,0.00019304805,0.00031272965,0.0002144744,0.006183871,0.00014855017,0.0054890392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985902,0.0002580557,0.00010309629,0.00051005976,0.0003441213,0.00019444597],"domain_scores_gemma":[0.9983084,0.0004934983,0.00012537374,0.0002736746,0.0007025755,0.00009646562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015901974,0.001191251,0.0016013249,0.0031945913,0.00071124284,0.0011025991,0.0011545874,0.0011280043,0.0013193621],"category_scores_gemma":[0.0035621098,0.00026419628,0.0014767086,0.0013912705,0.0002970732,0.0010709086,0.0010167679,0.0015319218,0.00085226895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003521788,0.000870287,0.05822815,0.00016918653,0.0006443617,0.0003268535,0.00024273622,0.1072179,0.010950505,0.0017832302,0.017517727,0.80169684],"study_design_scores_gemma":[0.000009939278,0.00010926115,0.012438807,0.00002562527,0.00011451387,0.00011476029,0.0001381799,0.9777198,0.004877666,0.0013725627,0.0030494966,0.000029373718],"about_ca_topic_score_codex":0.0065512704,"about_ca_topic_score_gemma":0.007526685,"teacher_disagreement_score":0.0065512704,"about_ca_system_score_codex":0.00044953497,"about_ca_system_score_gemma":0.00067271246,"threshold_uncertainty_score":0.0130262375},"labels":[],"label_agreement":null},{"id":"W4285043884","doi":"10.22215/etd/2022-14961","title":"Accelerated Epistemic Harm: Understanding the Role of Social Media Engagement Algorithms in Online Radicalization","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Radicalization; Harm; Timeline; Social media; Legislature; Political science; Epistemology; Sociology; Social psychology; Criminology; Psychology; Law; Terrorism; Philosophy","score_opus":0.04918855507275119,"score_gpt":0.29465308231948073,"score_spread":0.24546452724672954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285043884","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30111694,0.0020564948,0.20975256,0.0856902,0.00060933304,0.0002917381,0.0001332213,0.00021792801,0.40013152],"genre_scores_gemma":[0.9801676,0.0007160217,0.010941207,0.0015899169,0.00016681686,0.00015097852,0.00003195836,0.00006134351,0.006174047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99410534,0.0036207777,0.00017791099,0.0006067939,0.00080060295,0.0006885863],"domain_scores_gemma":[0.9623635,0.025274517,0.00587987,0.0029283531,0.0019707084,0.0015830806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008936777,0.0005296518,0.00046857077,0.002201206,0.0038456297,0.01203835,0.0018697075,0.0040624924,0.006899132],"category_scores_gemma":[0.030539645,0.000497186,0.00090077834,0.0009999779,0.027354375,0.023037644,0.0081475815,0.006389538,0.0007378116],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003525149,0.00013806768,0.010534328,0.00010894963,0.00001509039,0.0002175273,0.044090863,0.0011612065,0.0002842152,0.9285619,0.0012572783,0.013595367],"study_design_scores_gemma":[0.00001704378,0.00006838616,0.009854268,0.0003420314,0.000032492146,0.00028257395,0.04388483,0.009612289,0.00064500904,0.903595,0.03161908,0.000047073565],"about_ca_topic_score_codex":0.0025333937,"about_ca_topic_score_gemma":0.0026786549,"teacher_disagreement_score":0.01203835,"about_ca_system_score_codex":0.0042124065,"about_ca_system_score_gemma":0.0033539536,"threshold_uncertainty_score":0.047262788},"labels":[],"label_agreement":null},{"id":"W4285100697","doi":"10.2196/preprints.40403","title":"Development of a COVID-19–Related Anti-Asian Tweet Data Set: Quantitative Study (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Computer science; Social media; Set (abstract data type); Data set; Coronavirus disease 2019 (COVID-19); Stigma (botany); Preprint; Information retrieval; Artificial intelligence; World Wide Web; Psychology; Medicine","score_opus":0.10497001893192517,"score_gpt":0.35919747465146373,"score_spread":0.25422745571953853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285100697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70497787,0.00021968507,0.040168487,0.0027331528,0.0004264308,0.00955372,0.23117714,0.002077802,0.008665687],"genre_scores_gemma":[0.49423894,0.000251395,0.13290532,0.0009285862,0.00024295456,0.02807567,0.3359947,0.00066834013,0.0066940347],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9946689,0.0026050345,0.00052039133,0.00065879937,0.0013496921,0.00019712292],"domain_scores_gemma":[0.96262753,0.02197535,0.001787172,0.0030514144,0.009151481,0.0014069971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0078587625,0.00033221356,0.00037731408,0.003046847,0.0013761813,0.0012344309,0.0010571289,0.0009305891,0.0068039987],"category_scores_gemma":[0.027967202,0.0002675175,0.00039390495,0.0031024136,0.00095367525,0.0017502467,0.0017181622,0.0015784738,0.004919519],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011761936,0.005936964,0.30371875,0.0047821305,0.00019840273,0.0014226456,0.027136931,0.008536285,0.020787587,0.012222259,0.31525022,0.29883155],"study_design_scores_gemma":[0.00042291824,0.001953498,0.5919692,0.00095714885,0.000115333954,0.00076682906,0.041058876,0.052603506,0.024120562,0.0056223064,0.27997363,0.00043624407],"about_ca_topic_score_codex":0.0076292236,"about_ca_topic_score_gemma":0.010285528,"teacher_disagreement_score":0.0078587625,"about_ca_system_score_codex":0.0012316509,"about_ca_system_score_gemma":0.0015762331,"threshold_uncertainty_score":0.041561663},"labels":[],"label_agreement":null},{"id":"W4285211372","doi":"10.18653/v1/2022.acl-long.378","title":"Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Generalizability theory; Computer science; Robustness (evolution); Interpretability; Artificial intelligence; Moderation; Machine learning; Natural language processing; Metric (unit); Psychology","score_opus":0.003906633246131122,"score_gpt":0.20452484117602474,"score_spread":0.2006182079298936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285211372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29482013,0.0013782225,0.69692594,0.001378897,0.00017966078,0.00033252657,0.00031789666,0.002502376,0.002164415],"genre_scores_gemma":[0.93751013,0.00030763404,0.05957261,0.00038945113,0.00012116197,0.00016971158,0.0006708579,0.000219121,0.0010393027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9887294,0.0061787274,0.00076682685,0.0026994136,0.0011674351,0.00045808652],"domain_scores_gemma":[0.92280686,0.05995898,0.0037532025,0.009137569,0.003697161,0.00064618175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025135595,0.0028376859,0.002428673,0.0031568906,0.0011326709,0.0033701665,0.0028917252,0.0032672987,0.0011892023],"category_scores_gemma":[0.09239104,0.0010376103,0.0014799971,0.0018379888,0.0033060326,0.006336321,0.0049587055,0.007373793,0.0006740881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001233114,0.0005909979,0.051039945,0.000411575,0.0007081408,0.00064502045,0.002414624,0.4579304,0.02031248,0.009266149,0.0038147126,0.45163283],"study_design_scores_gemma":[0.000021643029,0.00016857481,0.0031824284,0.000039677616,0.000043061886,0.00011269295,0.00015791964,0.97846806,0.0041465503,0.013135257,0.00048952654,0.00003462496],"about_ca_topic_score_codex":0.005776135,"about_ca_topic_score_gemma":0.005100007,"teacher_disagreement_score":0.025135595,"about_ca_system_score_codex":0.0012883327,"about_ca_system_score_gemma":0.0012468114,"threshold_uncertainty_score":0.13293129},"labels":[],"label_agreement":null},{"id":"W4285449248","doi":"10.29173/crossings17","title":"E-voting in Canada: Does age affect attitudes towards online voting?","year":2021,"lang":"en","type":"article","venue":"Crossings An Undergraduate Arts Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Voting; Ballot; Polling; Affect (linguistics); Principle of legality; Political science; Secret ballot; Disapproval voting; Cardinal voting systems; Public relations; Bullet voting; Representation (politics); Ranked voting system; Politics; Internet privacy; Social psychology; Psychology; Law; Computer science; Communication","score_opus":0.022411137838016577,"score_gpt":0.2748346435417168,"score_spread":0.25242350570370026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285449248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97871333,0.00064203,0.00012654345,0.0049316175,0.00007177113,0.000050254974,0.0015799336,0.000011225469,0.013873277],"genre_scores_gemma":[0.9932679,0.0004647928,0.00010119351,0.00047595415,0.0000137994775,0.000013585859,0.0005661058,0.000009324317,0.005087172],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980574,0.00017894631,0.00007465585,0.0001874862,0.0006692666,0.0008323194],"domain_scores_gemma":[0.98973185,0.00096188067,0.0017548223,0.0002694799,0.004071869,0.0032100335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015946287,0.0002277745,0.00044193468,0.0010843545,0.0071740285,0.0029812553,0.0010920708,0.00086078956,0.008469123],"category_scores_gemma":[0.0076371417,0.00023981166,0.0007975712,0.0029241114,0.0015306051,0.0009372807,0.0011454211,0.0018904804,0.00082262343],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001365805,0.00013987464,0.96800697,0.000036501566,0.000033776367,0.000119635064,0.008500497,0.00010713417,0.000117469564,0.000867326,0.005216082,0.016718179],"study_design_scores_gemma":[0.0000121692965,0.000039043003,0.9700495,0.000095892705,0.000030483114,0.00004404135,0.022066245,0.000406495,0.000081310085,0.00016629434,0.0069751265,0.000033451],"about_ca_topic_score_codex":0.9951348,"about_ca_topic_score_gemma":0.99729306,"teacher_disagreement_score":0.03237479,"about_ca_system_score_codex":0.03237479,"about_ca_system_score_gemma":0.04437851,"threshold_uncertainty_score":0.23489666},"labels":[],"label_agreement":null},{"id":"W4286457280","doi":"10.1075/ps.21014.bar","title":"“They fabricated lies against us and described us in the harshest of ways”","year":2022,"lang":"en","type":"article","venue":"Pragmatics and Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Transitive relation; Focus (optics); Publishing; Sociology; Media studies; Social media; Scale (ratio); Islam; State (computer science); Computer science; Linguistics; World Wide Web; Political science; History; Law; Mathematics; Geography; Philosophy; Algorithm","score_opus":0.01392979640100497,"score_gpt":0.20591666818474477,"score_spread":0.1919868717837398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286457280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70688784,0.0013871486,0.02209159,0.0070452886,0.000597852,0.00036362273,0.0015868059,0.00023108961,0.2598087],"genre_scores_gemma":[0.97396797,0.0005017665,0.003978608,0.0009446535,0.000072201685,0.00023867731,0.00047321658,0.00012089281,0.01970196],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99695575,0.0017676348,0.00012906067,0.0002748813,0.0006637835,0.0002088978],"domain_scores_gemma":[0.9951716,0.0027089308,0.00070282276,0.00039252584,0.0008080311,0.00021618333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030706807,0.0003904248,0.00029879677,0.001726528,0.0044096946,0.0036755898,0.00047782244,0.0007584087,0.010179608],"category_scores_gemma":[0.012069059,0.00021628963,0.00013486578,0.0014116135,0.006150587,0.0032349054,0.0030302168,0.00158789,0.0017231021],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007040755,0.000033288157,0.013257674,0.00042540388,0.000021084186,0.00060366554,0.85956454,0.00006600812,0.0025043178,0.06569211,0.021342652,0.036418915],"study_design_scores_gemma":[0.00000600358,0.000036030247,0.015816553,0.0004790984,0.000016738451,0.0007646667,0.6973944,0.0003686243,0.0022019818,0.00983115,0.2730484,0.000036441852],"about_ca_topic_score_codex":0.0040937094,"about_ca_topic_score_gemma":0.0062792804,"teacher_disagreement_score":0.010179608,"about_ca_system_score_codex":0.0013485824,"about_ca_system_score_gemma":0.00090070063,"threshold_uncertainty_score":0.03405416},"labels":[],"label_agreement":null},{"id":"W4286593262","doi":"10.4018/978-1-6684-6307-9.ch023","title":"Delete, Delete, Hang-Up","year":2022,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Tragedy (event); Hang; Distress; Criminology; Political science; Law; Psychology; Social psychology; Internet privacy; Engineering; Computer science; Psychiatry; Psychotherapist","score_opus":0.015755338781707318,"score_gpt":0.22429957955293878,"score_spread":0.20854424077123146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286593262","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0093415305,0.0029179675,0.088487744,0.003861147,0.005591768,0.00056424487,0.008213345,0.041218396,0.8398039],"genre_scores_gemma":[0.03877304,0.0031897994,0.028544107,0.0037469354,0.0011652606,0.00033525276,0.006120999,0.020230664,0.8978939],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9994925,0.00009751964,0.00003235961,0.00008688188,0.0002214947,0.0000690885],"domain_scores_gemma":[0.9982583,0.00052814645,0.00009456069,0.00061653287,0.0003785966,0.00012387014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065028673,0.0012274892,0.0006560867,0.00148946,0.0017611177,0.0029765847,0.0014830888,0.0012496285,0.23526911],"category_scores_gemma":[0.0055688554,0.0005343039,0.0006588217,0.0015962344,0.0012188966,0.0080869915,0.003026941,0.0013396116,0.23918125],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017474042,0.000067798195,0.0006887444,0.00045288008,0.000013702373,0.00051897066,0.0019943102,0.00024988264,0.002296776,0.02448358,0.680031,0.28902766],"study_design_scores_gemma":[0.000007778415,0.000024990966,0.000351466,0.00010072032,0.000008084538,0.0003139265,0.0004994472,0.0003096983,0.0015337415,0.0055726985,0.99126107,0.00001633008],"about_ca_topic_score_codex":0.0017580244,"about_ca_topic_score_gemma":0.0030246426,"teacher_disagreement_score":0.23526911,"about_ca_system_score_codex":0.0004729843,"about_ca_system_score_gemma":0.0004564749,"threshold_uncertainty_score":0.7870536},"labels":[],"label_agreement":null},{"id":"W4286951123","doi":"10.48550/arxiv.2110.00116","title":"#ContextMatters: Advantages and Limitations of Using Machine Learning to\\n Support Women in Politics","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Politics; Affect (linguistics); Government (linguistics); Political science; Natural experiment; Intervention (counseling); Social media; Inequality; Public relations; Social psychology; Psychology; Medicine; Law","score_opus":0.08615127937938073,"score_gpt":0.20943360965002528,"score_spread":0.12328233027064454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286951123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2927719,0.014627166,0.46030527,0.08450248,0.0031478612,0.0016588158,0.010796375,0.01599466,0.116195515],"genre_scores_gemma":[0.6736627,0.0025900253,0.29499203,0.008460262,0.0009588579,0.0009548583,0.0036004889,0.0006225547,0.014158183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925149,0.004611779,0.0002790016,0.0011034748,0.0012675988,0.00022317658],"domain_scores_gemma":[0.9681277,0.024919948,0.0009077036,0.0034046953,0.0022244742,0.0004153951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008300979,0.0010925938,0.00057717174,0.0013522111,0.001290104,0.004412513,0.002196375,0.0016208799,0.00657765],"category_scores_gemma":[0.030455898,0.00042452753,0.00051667495,0.0009272405,0.0012959031,0.0073806853,0.0024354742,0.0027281335,0.003990121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007390364,0.0004758252,0.03349854,0.0016364534,0.00023044737,0.0001372825,0.0028803053,0.007905893,0.0063952627,0.019486332,0.04844455,0.87817013],"study_design_scores_gemma":[0.00018414482,0.0011892561,0.029403979,0.0020246159,0.0002373227,0.0005446912,0.0073731723,0.3963817,0.027964085,0.13591906,0.39842582,0.00035211467],"about_ca_topic_score_codex":0.005047521,"about_ca_topic_score_gemma":0.01133302,"teacher_disagreement_score":0.008300979,"about_ca_system_score_codex":0.0011885099,"about_ca_system_score_gemma":0.0013942046,"threshold_uncertainty_score":0.04390025},"labels":[],"label_agreement":null},{"id":"W4287887319","doi":"10.18653/v1/2022.woah-1.13","title":"Enriching Abusive Language Detection with Community Context","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pejorative; Computer science; Context (archaeology); Internet privacy; Computer security; Natural language processing; Psychology; Linguistics","score_opus":0.007308982947046404,"score_gpt":0.20340909306487132,"score_spread":0.1961001101178249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287887319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7858307,0.005052571,0.18491548,0.002324788,0.00039931125,0.0004596785,0.0014118972,0.0022746308,0.017330978],"genre_scores_gemma":[0.96873426,0.0003469094,0.0284043,0.0001894538,0.00014582988,0.00007733725,0.00047953686,0.00007180261,0.0015505799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698824,0.0013489845,0.00015109114,0.000715529,0.0004958561,0.00030034536],"domain_scores_gemma":[0.9875639,0.007047398,0.0014174023,0.0014822634,0.0017594997,0.00072946446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035531286,0.0014129821,0.0010832698,0.004878998,0.0013422347,0.003003993,0.0010337752,0.0015533076,0.0024006446],"category_scores_gemma":[0.018795384,0.00041721514,0.00073040626,0.0015294519,0.0008742319,0.0058768606,0.0032207817,0.0022867073,0.0015623501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091087486,0.0013230648,0.39787927,0.0007049199,0.00034336984,0.00087461167,0.0049862773,0.01962707,0.020979717,0.0064446726,0.009774145,0.5361521],"study_design_scores_gemma":[0.000055392127,0.000566749,0.10030054,0.00042641663,0.00039161215,0.0018017027,0.004201804,0.8221314,0.015633615,0.03805814,0.016219098,0.0002136063],"about_ca_topic_score_codex":0.0033624028,"about_ca_topic_score_gemma":0.00799938,"teacher_disagreement_score":0.004878998,"about_ca_system_score_codex":0.000568037,"about_ca_system_score_gemma":0.0008338181,"threshold_uncertainty_score":0.01879096},"labels":[],"label_agreement":null},{"id":"W4289326033","doi":"10.1145/3274386","title":"Opinion Conflicts","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Incivility; Reputation; Baseline (sea); Computer science; Sentiment analysis; Fake news; Social media; Fraction (chemistry); Psychology; Computer security; Internet privacy; Social psychology; World Wide Web; Artificial intelligence; Political science","score_opus":0.05491092177609213,"score_gpt":0.3229254744530044,"score_spread":0.26801455267691227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289326033","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10307934,0.004840074,0.08464078,0.018946704,0.003880819,0.0007471701,0.0066764336,0.0012427833,0.77594596],"genre_scores_gemma":[0.81072474,0.0018129658,0.019672818,0.008493567,0.0017561396,0.00046711942,0.005265016,0.00039803438,0.15140952],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99633336,0.0009839962,0.00028524754,0.00075894274,0.0012135867,0.00042474206],"domain_scores_gemma":[0.9932526,0.0026555182,0.0011325825,0.00072047097,0.0017915743,0.0004473527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022348715,0.00063232216,0.00048890593,0.00093701907,0.002356327,0.0042459494,0.0011239499,0.0017545085,0.061597843],"category_scores_gemma":[0.016639017,0.0002458425,0.0005285804,0.0008767842,0.00091078784,0.0042265994,0.002729553,0.0012849584,0.015803024],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007022677,0.0001953089,0.027105879,0.00094036903,0.00020782891,0.0025631683,0.012118919,0.001976574,0.0101461895,0.23241033,0.24607551,0.4655576],"study_design_scores_gemma":[0.00008936454,0.00016156817,0.011693762,0.00026600715,0.0001284635,0.002325999,0.008258586,0.013538754,0.0044826553,0.11862647,0.84032416,0.00010424831],"about_ca_topic_score_codex":0.0016946807,"about_ca_topic_score_gemma":0.002003462,"teacher_disagreement_score":0.061597843,"about_ca_system_score_codex":0.0012124105,"about_ca_system_score_gemma":0.0006911739,"threshold_uncertainty_score":0.2060653},"labels":[],"label_agreement":null},{"id":"W4291820059","doi":"10.1108/ccij-04-2022-0040","title":"Corporate social responsibility and public diplomacy as formulas to reduce hate speech on social media in the fake news era","year":2022,"lang":"en","type":"article","venue":"Corporate Communications An International Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislation; Social media; Political science; Transparency (behavior); Public relations; Legislature; European union; Sanctions; Corporate social responsibility; Originality; Sociology; Law; Business","score_opus":0.14426295951245305,"score_gpt":0.34986964303792795,"score_spread":0.2056066835254749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291820059","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41259956,0.0026097498,0.050720178,0.025637055,0.00071511173,0.0008221573,0.00004155334,0.00028813464,0.5065665],"genre_scores_gemma":[0.98552424,0.00028325157,0.005278079,0.000812009,0.0000670956,0.00020476169,0.000009380204,0.000032813055,0.007788316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9800915,0.013799843,0.00042700517,0.0008894393,0.0034275248,0.0013646655],"domain_scores_gemma":[0.9600473,0.025458485,0.0061472734,0.0032792366,0.002881386,0.0021863412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013223613,0.00075588335,0.00034911194,0.0025513007,0.0063903397,0.013000278,0.0013654993,0.0038720644,0.006167916],"category_scores_gemma":[0.03671419,0.00036012058,0.0004210201,0.0010237147,0.026344307,0.008988027,0.009984385,0.0036690247,0.0007104212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013975397,0.00054287835,0.00905858,0.00065876334,0.00004492883,0.00090331695,0.14389573,0.0008748587,0.0018366171,0.734275,0.0059927106,0.10177688],"study_design_scores_gemma":[0.0002022046,0.0010373554,0.02793145,0.0040690345,0.0003171185,0.0017773559,0.33887035,0.008598357,0.011339373,0.26307663,0.34258097,0.00019979234],"about_ca_topic_score_codex":0.0032570888,"about_ca_topic_score_gemma":0.0048829676,"teacher_disagreement_score":0.013223613,"about_ca_system_score_codex":0.0071132206,"about_ca_system_score_gemma":0.007791835,"threshold_uncertainty_score":0.06993401},"labels":[],"label_agreement":null},{"id":"W4293318909","doi":"10.29173/irie480","title":"Upgrading the protection of children from manipulative and addictive strategies in online games","year":2022,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"KU Leuven","keywords":"Legislature; Unitary state; Perspective (graphical); Multidisciplinary approach; Universality (dynamical systems); Framing (construction); Addiction; Context (archaeology); Internet privacy; Risk analysis (engineering); Computer science; Law and economics; Public relations; Business; Political science; Psychology; Engineering; Law; Sociology; Artificial intelligence","score_opus":0.03938888452279132,"score_gpt":0.2995608498395405,"score_spread":0.2601719653167492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293318909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72686285,0.003787802,0.09188787,0.016156714,0.00020868792,0.00036664752,0.00009904356,0.00030214762,0.16032827],"genre_scores_gemma":[0.9798344,0.0012419574,0.014295018,0.0011158258,0.00003789772,0.0002430985,0.0000260313,0.000033791286,0.003172068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98500144,0.00980728,0.00065817474,0.00092318514,0.0024722356,0.0011377245],"domain_scores_gemma":[0.9700937,0.019235548,0.0043188375,0.0036741325,0.0014247659,0.0012529847],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.010920586,0.0006679269,0.00047101852,0.0016088125,0.0021547824,0.004264839,0.0017745831,0.002495759,0.0038601388],"category_scores_gemma":[0.040966455,0.00048546112,0.00059676974,0.00049628067,0.010030352,0.0048950724,0.0055212392,0.0028014905,0.0006279393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034497897,0.0013769461,0.051855084,0.0011641068,0.00009696575,0.0015443575,0.057044372,0.0022171787,0.01122084,0.49383876,0.0044618817,0.3748345],"study_design_scores_gemma":[0.00023302867,0.0025581468,0.12347071,0.008290092,0.00031294802,0.00830215,0.053613316,0.015169476,0.06232185,0.49092615,0.23444492,0.0003573672],"about_ca_topic_score_codex":0.001835666,"about_ca_topic_score_gemma":0.0032429765,"teacher_disagreement_score":0.99784523,"about_ca_system_score_codex":0.0012708453,"about_ca_system_score_gemma":0.0027731222,"threshold_uncertainty_score":0.05775428},"labels":[],"label_agreement":null},{"id":"W4294384215","doi":"10.31219/osf.io/dnhbm","title":"Do Unfounded Allegations of Election Fraud Influence the Likelihood of Voting?","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Legitimacy; Ballot; Voting; Political science; Politics; Spoilt vote; Perception; Secret ballot; Public relations; Group voting ticket; Psychology; Law","score_opus":0.010611351360565823,"score_gpt":0.2502380532280629,"score_spread":0.2396267018674971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294384215","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944792,0.00010249439,0.00035260033,0.0005768876,0.000013257987,0.000019586076,0.00007836216,0.00000649682,0.004371124],"genre_scores_gemma":[0.99919754,0.000056412297,0.00013799104,0.00007148586,0.000010869789,0.0000077592385,0.000050059327,0.0000032062528,0.00046461142],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9925257,0.004807518,0.00040239358,0.00061170995,0.001078739,0.0005739579],"domain_scores_gemma":[0.84253097,0.098467015,0.042331714,0.008292162,0.004254005,0.0041241553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008010126,0.000194503,0.00048972544,0.00095171464,0.00074342225,0.003510092,0.0005143814,0.0013752726,0.008996126],"category_scores_gemma":[0.11779997,0.00032168394,0.00046013988,0.00078406907,0.0016106049,0.0014559028,0.0009175648,0.0015689033,0.0016570772],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005723959,0.0009153969,0.9761563,0.00005099591,0.00011969071,0.00007183837,0.0028302707,0.00023616412,0.0004879896,0.00086909643,0.0004217296,0.017268123],"study_design_scores_gemma":[0.00002058266,0.00039665453,0.99254155,0.000033226417,0.000064251384,0.000121700476,0.0029210288,0.0012487522,0.00041778682,0.0011439546,0.0010678867,0.000022760127],"about_ca_topic_score_codex":0.0031352038,"about_ca_topic_score_gemma":0.0035803316,"teacher_disagreement_score":0.008996126,"about_ca_system_score_codex":0.0005621824,"about_ca_system_score_gemma":0.0005186099,"threshold_uncertainty_score":0.042362094},"labels":[],"label_agreement":null},{"id":"W4296363272","doi":"10.5121/csit.2022.121511","title":"Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Word2vec; Computer science; Word embedding; Word (group theory); Artificial intelligence; Social media; Natural language processing; The Internet; Key (lock); Embedding; Support vector machine; Machine learning; Speech recognition; World Wide Web","score_opus":0.06441875478281359,"score_gpt":0.281362161260077,"score_spread":0.21694340647726343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296363272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9168417,0.008187895,0.05567306,0.0009218394,0.0011804936,0.00034571945,0.0026359393,0.006598763,0.007614636],"genre_scores_gemma":[0.94119716,0.0010611737,0.04619701,0.00021937709,0.00016888231,0.00016012405,0.0075437715,0.00013585665,0.00331678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974031,0.0009339584,0.0003289031,0.00054192205,0.0004939299,0.00029827456],"domain_scores_gemma":[0.99430496,0.0031536082,0.00037412305,0.00046796058,0.0013934649,0.00030594395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037848228,0.0026797927,0.0019201428,0.0028711122,0.00067327265,0.0013849305,0.0011573512,0.0020604853,0.0024610932],"category_scores_gemma":[0.009590447,0.00035588484,0.0008584404,0.0014179791,0.0005331237,0.002397529,0.0015830345,0.0013749041,0.0017750831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0059659337,0.0031467972,0.047121674,0.0011773304,0.0011910022,0.0005022263,0.00021511705,0.16607185,0.019556768,0.0011098735,0.017661935,0.7362795],"study_design_scores_gemma":[0.00007693306,0.0008129595,0.005859933,0.00004948585,0.000097931734,0.00018170007,0.00015127717,0.9799958,0.011321302,0.00043483207,0.0009770988,0.00004078658],"about_ca_topic_score_codex":0.008120022,"about_ca_topic_score_gemma":0.009245601,"teacher_disagreement_score":0.008120022,"about_ca_system_score_codex":0.00084107,"about_ca_system_score_gemma":0.00071303116,"threshold_uncertainty_score":0.020016313},"labels":[],"label_agreement":null},{"id":"W4296363324","doi":"10.5121/csit.2022.121507","title":"Cyberbullying Detection using Ensemble Method","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Granularity; Computer science; Ensemble forecasting; Support vector machine; Ensemble learning; Social media; Artificial intelligence; Machine learning; Transformer; Data mining; Engineering; World Wide Web","score_opus":0.021685541765960516,"score_gpt":0.26848363624956734,"score_spread":0.24679809448360682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296363324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3022411,0.001876303,0.6841595,0.00039811502,0.00036013886,0.00019384226,0.0022244633,0.0047227666,0.0038236375],"genre_scores_gemma":[0.89107144,0.00054834154,0.10020218,0.000089618035,0.00021087844,0.00011262102,0.0040393397,0.00013155949,0.0035940034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873334,0.00021894788,0.00008743014,0.0004590568,0.0003306169,0.00017063342],"domain_scores_gemma":[0.99843913,0.0004720222,0.00014581026,0.00030515628,0.0005495595,0.00008841731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015272254,0.001314426,0.0015082419,0.0044238665,0.00055058306,0.00092228345,0.001013249,0.00095397746,0.0012093454],"category_scores_gemma":[0.003210654,0.00024724394,0.0015474926,0.0018622031,0.00022746004,0.0013170155,0.0009541843,0.0012264823,0.0009057252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032868647,0.00053579453,0.06657306,0.00012203469,0.0007028611,0.00039095516,0.00022839473,0.103594,0.009898645,0.0013443321,0.009697185,0.80658406],"study_design_scores_gemma":[0.000007809041,0.000100509555,0.012451091,0.00002019807,0.00012938783,0.00020605468,0.0000857112,0.98006433,0.003607214,0.0014797997,0.0018201866,0.00002775699],"about_ca_topic_score_codex":0.0068270196,"about_ca_topic_score_gemma":0.006994526,"teacher_disagreement_score":0.0068270196,"about_ca_system_score_codex":0.0003607262,"about_ca_system_score_gemma":0.0005616594,"threshold_uncertainty_score":0.0135746},"labels":[],"label_agreement":null},{"id":"W4296593804","doi":"10.17975/sfj-2022-015","title":"A sentiment analysis of the Black Lives Matter movement using Twitter","year":2022,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; University of Waterloo; University of Toronto; University of British Columbia","funders":"","keywords":"Sentiment analysis; Movement (music); Social media; Artificial intelligence; Computer science; Injustice; Social movement; Politics; Natural language processing; Data science; Psychology; Political science; Social psychology; World Wide Web","score_opus":0.023119547812057648,"score_gpt":0.23960409133732685,"score_spread":0.2164845435252692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296593804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975654,0.00012040055,0.004394663,0.0009620858,0.0001605248,0.00014229429,0.0114723,0.00030713648,0.0067866594],"genre_scores_gemma":[0.97377384,0.00016975772,0.007524004,0.00013583829,0.00015943218,0.00010969993,0.013678409,0.00003054574,0.0044184052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99978393,0.00005906357,0.000019744972,0.000037622623,0.00006026124,0.000039349736],"domain_scores_gemma":[0.9994124,0.00018177829,0.000105337924,0.00003601643,0.00020801589,0.00005652056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004290976,0.00041849038,0.0001808616,0.0015443998,0.00045965475,0.00041422746,0.0001867546,0.00031875505,0.001423115],"category_scores_gemma":[0.0011239895,0.00008153425,0.00035974864,0.00095499324,0.0001515655,0.0006441987,0.00031924772,0.0003838873,0.0009985622],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001091749,0.000814023,0.6635211,0.00045554075,0.0002163809,0.0015553714,0.002667958,0.010411678,0.058922652,0.0023592229,0.050370757,0.2076136],"study_design_scores_gemma":[0.000035505454,0.00056740374,0.7123513,0.0000750425,0.00008067394,0.00042114785,0.0048560114,0.24477875,0.011754972,0.0012115121,0.023802122,0.000065528926],"about_ca_topic_score_codex":0.0071493387,"about_ca_topic_score_gemma":0.012510095,"teacher_disagreement_score":0.0071493387,"about_ca_system_score_codex":0.00039219618,"about_ca_system_score_gemma":0.00025388054,"threshold_uncertainty_score":0.014215469},"labels":[],"label_agreement":null},{"id":"W4297139304","doi":"10.33282/abaa.v14i56.878","title":"Comment moderation strategies in Canadian news media and their principles","year":2022,"lang":"en","type":"article","venue":"ALBAHITH ALALAMI","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"News media; News values; Moderation; Political science; Democracy; Media studies; Public relations; Advertising; Sociology; Psychology; Business; Politics; Law; Social psychology","score_opus":0.015674842537503394,"score_gpt":0.20968823945557652,"score_spread":0.19401339691807312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297139304","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22896788,0.010867304,0.038594935,0.10298757,0.0012697958,0.0012947193,0.0015730015,0.0009894511,0.6134553],"genre_scores_gemma":[0.9494025,0.0022689768,0.016135823,0.004752708,0.0002497059,0.00056419487,0.0002753978,0.00018907987,0.026161693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.95771694,0.012225536,0.0019254003,0.0033566577,0.020635875,0.004139554],"domain_scores_gemma":[0.8811259,0.050729062,0.008740129,0.0065136068,0.047855992,0.005035195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04222638,0.0008517457,0.00064653717,0.008228514,0.020534366,0.015647078,0.002828043,0.0029345653,0.0045614974],"category_scores_gemma":[0.104597196,0.0010529823,0.00050461217,0.0074416813,0.017325893,0.0054788874,0.0056674085,0.003830061,0.00056659424],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023067054,0.00010082733,0.056330185,0.0010717013,0.000087401044,0.0007081449,0.2607313,0.00096605654,0.003170497,0.45470122,0.04242519,0.17947689],"study_design_scores_gemma":[0.00009715846,0.00009625511,0.08159033,0.001471326,0.00017980936,0.00027221258,0.11500642,0.0020590024,0.004171519,0.045892347,0.7488183,0.00034536616],"about_ca_topic_score_codex":0.87437147,"about_ca_topic_score_gemma":0.9187326,"teacher_disagreement_score":0.12562853,"about_ca_system_score_codex":0.070359126,"about_ca_system_score_gemma":0.106801145,"threshold_uncertainty_score":0.5104936},"labels":[],"label_agreement":null},{"id":"W4297536022","doi":"10.1007/s11042-022-13777-0","title":"Net activism and whistleblowing on YouTube: a text mining analysis","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centre National de la Recherche Scientifique; Institut national de la recherche scientifique","keywords":"Computer science; Metadata; Representation (politics); Hierarchy; Natural language processing; Social media; Context (archaeology); Feature (linguistics); Cluster analysis; Artificial intelligence; Information retrieval; Resource (disambiguation); World Wide Web; Linguistics; Politics","score_opus":0.01736515410158408,"score_gpt":0.24066728730686401,"score_spread":0.22330213320527995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297536022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97210884,0.0006227624,0.0012471378,0.00036791354,0.00006008782,0.00011946516,0.019244144,0.0001022885,0.006127412],"genre_scores_gemma":[0.96168894,0.0007601246,0.003312671,0.00008781096,0.00011064326,0.00015919052,0.026174722,0.000036753296,0.0076690866],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997068,0.00004024185,0.000030276708,0.000046829664,0.00012069417,0.000055108936],"domain_scores_gemma":[0.99876946,0.00053561234,0.00022861139,0.00003787605,0.00032258412,0.00010582963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027722656,0.0003142986,0.0001865478,0.0057024998,0.0006856397,0.0007459188,0.000287789,0.00040306503,0.0016369536],"category_scores_gemma":[0.0013203324,0.00008337693,0.00033840566,0.00336755,0.00018570818,0.0007252065,0.00043227425,0.0003239225,0.000771838],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048883166,0.00037727255,0.8033364,0.0006048724,0.00023645931,0.0014166887,0.0032535645,0.001123173,0.01118486,0.0011122208,0.026070204,0.15079549],"study_design_scores_gemma":[0.0000069222797,0.00011805188,0.96394724,0.00010405075,0.00010276655,0.00062873546,0.0057212226,0.013464873,0.0034934396,0.00026366735,0.012122905,0.00002614946],"about_ca_topic_score_codex":0.030008975,"about_ca_topic_score_gemma":0.07078635,"teacher_disagreement_score":0.030008975,"about_ca_system_score_codex":0.00048881344,"about_ca_system_score_gemma":0.00050220004,"threshold_uncertainty_score":0.0596686},"labels":[],"label_agreement":null},{"id":"W4297887757","doi":"10.31219/osf.io/k5dzr","title":"Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"Yale University; National Science Foundation","keywords":"Outrage; Social psychology; Morality; Psychology; Hostility; Politics; Moral disengagement; Social media; Political science; Law","score_opus":0.020079063082528298,"score_gpt":0.26945569444794537,"score_spread":0.24937663136541707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297887757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99539244,0.000036170804,0.00086624926,0.0001086621,0.000007893324,0.000010133218,0.000026766995,0.000009357226,0.003542257],"genre_scores_gemma":[0.99927205,0.00003329256,0.00027345095,0.00006074871,0.0000089573505,0.0000124508715,0.000027433785,0.000006565129,0.00030512002],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99725753,0.001423405,0.00013929808,0.0003716646,0.00062237884,0.00018568887],"domain_scores_gemma":[0.9566814,0.017873354,0.01805877,0.0032818338,0.0024742254,0.0016304539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003618844,0.00041577232,0.0002831538,0.0010512418,0.0008308027,0.0026415875,0.00025212974,0.00072812673,0.0033611949],"category_scores_gemma":[0.023979018,0.0004027608,0.0002833839,0.0004045983,0.0016920071,0.0016519742,0.0019002886,0.0012398757,0.00038810069],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005382732,0.0005654264,0.9224808,0.000160837,0.0001918157,0.00021467287,0.035473563,0.00049607235,0.012640148,0.0010856509,0.0004788458,0.025673857],"study_design_scores_gemma":[0.000010128604,0.00019861896,0.97760195,0.00005796309,0.000058763297,0.0001557807,0.015408317,0.0012688134,0.002707339,0.0015097406,0.000985279,0.000037368503],"about_ca_topic_score_codex":0.0016085061,"about_ca_topic_score_gemma":0.0018923854,"teacher_disagreement_score":0.003618844,"about_ca_system_score_codex":0.0004201124,"about_ca_system_score_gemma":0.00025163166,"threshold_uncertainty_score":0.019138515},"labels":[],"label_agreement":null},{"id":"W4300816910","doi":"10.19145/e-gifder.1116850","title":"ÇERÇEVELEME ETİĞİ TARTIŞMALARINDA TEMEL KAVRAMLAR","year":2022,"lang":"tr","type":"article","venue":"Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Cambridge; McGill University; University of Minnesota; Harvard University","keywords":"Political science; Humanities; Art","score_opus":0.008316722688018736,"score_gpt":0.1896346503976917,"score_spread":0.18131792770967298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300816910","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34963462,0.08257259,0.074615516,0.039008994,0.0063468046,0.0012096171,0.004584121,0.0030511182,0.43897662],"genre_scores_gemma":[0.6420326,0.04390611,0.05331621,0.0054292777,0.0011457688,0.00075250166,0.0033725544,0.0008642734,0.24918069],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980902,0.00044360379,0.00014939644,0.0002735355,0.0007218317,0.00032130707],"domain_scores_gemma":[0.9969823,0.00066490547,0.00043768802,0.00021082794,0.0012550507,0.00044925054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019085993,0.00087011495,0.00089931866,0.0011405216,0.0023898077,0.004131929,0.0012315635,0.0020081687,0.049662277],"category_scores_gemma":[0.004672324,0.00043996767,0.0011240252,0.00100087,0.0014988562,0.0025190592,0.0026267588,0.0026833417,0.012646438],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016778681,0.0010661192,0.036613483,0.005297197,0.00026311004,0.0059892107,0.011853861,0.0016625307,0.018158723,0.031171542,0.10481874,0.7814277],"study_design_scores_gemma":[0.00010428645,0.0012992207,0.06147354,0.0028694808,0.0002419187,0.007867993,0.019857353,0.0019567423,0.013977572,0.012666263,0.8774075,0.00027802825],"about_ca_topic_score_codex":0.0057380325,"about_ca_topic_score_gemma":0.00898213,"teacher_disagreement_score":0.049662277,"about_ca_system_score_codex":0.0015832719,"about_ca_system_score_gemma":0.004244992,"threshold_uncertainty_score":0.16613686},"labels":[],"label_agreement":null},{"id":"W4300961522","doi":"10.1186/s12889-022-14203-8","title":"Correction: A content analysis of Canadian influencer crisis messages on Instagram and the public’s response during COVID-19","year":2022,"lang":"en","type":"erratum","venue":"BMC Public Health","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Biostatistics; Public health; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Journal of Public Health; Pandemic; Crisis communication; Content analysis; Crisis response; Public relations; Health policy; Public health care; Virology; Nursing; Political science; Social science; Pathology; Outbreak","score_opus":0.06656967721670459,"score_gpt":0.3017937613334004,"score_spread":0.23522408411669582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300961522","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007065249,0.0006376137,0.0008582825,0.15403815,0.81312776,0.000128114,0.014329517,0.0011387801,0.015035307],"genre_scores_gemma":[0.032229245,0.0046123685,0.006457102,0.14142437,0.11221123,0.00079178414,0.0111161,0.0048081167,0.6863496],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98945624,0.00089469174,0.0010218539,0.0009349279,0.0066149896,0.0010773501],"domain_scores_gemma":[0.88830894,0.016724957,0.002819508,0.0038335326,0.08536129,0.0029517577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005795173,0.0022774704,0.0014299599,0.008095878,0.009595032,0.0064839306,0.0054192943,0.008582731,0.08524287],"category_scores_gemma":[0.12363548,0.0011903379,0.0012360569,0.007791835,0.004093956,0.002467721,0.003274053,0.008996093,0.039991178],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011494921,0.0000026893536,0.00006427245,0.000042322044,0.0000028226416,0.00005767062,0.00012216801,0.000011454365,0.000019819361,0.0002730291,0.99780566,0.0015866365],"study_design_scores_gemma":[0.000034868546,0.000009363188,0.0018581878,0.00040679553,0.000027825861,0.00010167634,0.0007662281,0.0001827532,0.00027793171,0.00032399292,0.99596006,0.00005025344],"about_ca_topic_score_codex":0.72707576,"about_ca_topic_score_gemma":0.6714422,"teacher_disagreement_score":0.27292424,"about_ca_system_score_codex":0.021653201,"about_ca_system_score_gemma":0.042558532,"threshold_uncertainty_score":0.5490631},"labels":[],"label_agreement":null},{"id":"W4302307746","doi":"10.3138/cjc.2022-0015","title":"At the Limits of Free Speech: The Conditions that Enable Islamophobic Discourse","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Dismissal; Complaint; Ideal (ethics); Power (physics); Racism; Sociology; Free speech; Public discourse; Discourse analysis; Political science; Law; Gender studies; Linguistics; Philosophy","score_opus":0.02828604454495131,"score_gpt":0.2512840950907278,"score_spread":0.22299805054577648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302307746","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47635686,0.00181194,0.010990875,0.023934241,0.00041783636,0.0001306392,0.00015097245,0.00011673859,0.4860898],"genre_scores_gemma":[0.9974075,0.00015791401,0.00033596443,0.00031354584,0.000055355158,0.000024035588,0.000012787455,0.000015957983,0.0016768052],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930154,0.002850165,0.0002884433,0.0006181807,0.0020098914,0.0012178837],"domain_scores_gemma":[0.9807748,0.012895692,0.0019152834,0.0016415402,0.0016372298,0.0011355884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050481823,0.00024242046,0.00034564527,0.0013772446,0.010344718,0.012316067,0.0009986545,0.002540652,0.008216824],"category_scores_gemma":[0.021668753,0.0003902492,0.00021312152,0.0010502962,0.046078272,0.010213006,0.0070309285,0.0035979387,0.0009951091],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009943553,0.00005942359,0.0035053287,0.00014982677,0.00000551262,0.0010775035,0.5016488,0.00014858044,0.0016789599,0.4716274,0.0029739651,0.017025284],"study_design_scores_gemma":[0.000033834567,0.00006619132,0.012532868,0.00087886833,0.000019560259,0.0015024234,0.61181724,0.00075460406,0.0029936258,0.21617773,0.15313615,0.00008685729],"about_ca_topic_score_codex":0.015421215,"about_ca_topic_score_gemma":0.01178962,"teacher_disagreement_score":0.015421215,"about_ca_system_score_codex":0.0052447943,"about_ca_system_score_gemma":0.005400401,"threshold_uncertainty_score":0.03805381},"labels":[],"label_agreement":null},{"id":"W4304013957","doi":"10.32920/21301068","title":"Disclosing #MeToo in 2022","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario; University of Toronto","funders":"","keywords":"Harassment; Sexual assault; Psychology; Social media; Social issues; Qualitative analysis; Criminology; Social psychology; Qualitative research; Suicide prevention; Poison control; Medicine; Political science; Sociology; Law","score_opus":0.014765862505714879,"score_gpt":0.2531887802133146,"score_spread":0.2384229177075997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304013957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5693702,0.002761709,0.00925302,0.039947286,0.0054849605,0.00092697225,0.0031351321,0.0005628191,0.36855778],"genre_scores_gemma":[0.8692757,0.001521943,0.002403476,0.008175957,0.0005749556,0.00055582373,0.0008295334,0.00021850916,0.11644411],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9960757,0.0020860317,0.000232305,0.00017302406,0.0010219666,0.0004110103],"domain_scores_gemma":[0.99324435,0.0029369008,0.0010199542,0.000600364,0.0011387058,0.0010598206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036622344,0.0002597148,0.00020616461,0.0005945094,0.004232942,0.0035791798,0.0005612043,0.0015184698,0.021297796],"category_scores_gemma":[0.018433573,0.00018919716,0.00028308306,0.0006716349,0.0016152797,0.0038023242,0.004446057,0.0020630704,0.0049212356],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029734688,0.00024705488,0.030997628,0.0010151553,0.00003360901,0.0053219465,0.51551837,0.00029619652,0.005896453,0.06555723,0.15398374,0.2208352],"study_design_scores_gemma":[0.0000104451,0.00016564087,0.008252227,0.0005174942,0.000012433947,0.0014059737,0.18900266,0.00035563874,0.0022941846,0.00354652,0.79437643,0.00006043607],"about_ca_topic_score_codex":0.0044229683,"about_ca_topic_score_gemma":0.0060559716,"teacher_disagreement_score":0.021297796,"about_ca_system_score_codex":0.0019654676,"about_ca_system_score_gemma":0.001938444,"threshold_uncertainty_score":0.07124817},"labels":[],"label_agreement":null},{"id":"W4304014473","doi":"10.32920/21301068.v1","title":"Disclosing #MeToo in 2022","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario; University of Toronto","funders":"","keywords":"Harassment; Sexual assault; Psychology; Social media; Social issues; Qualitative analysis; Social psychology; Criminology; Qualitative research; Medicine; Suicide prevention; Poison control; Political science; Sociology; Law","score_opus":0.014765862505714879,"score_gpt":0.2531887802133146,"score_spread":0.2384229177075997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304014473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5693702,0.002761709,0.00925302,0.039947286,0.0054849605,0.00092697225,0.0031351321,0.0005628191,0.36855778],"genre_scores_gemma":[0.8692757,0.001521943,0.002403476,0.008175957,0.0005749556,0.00055582373,0.0008295334,0.00021850916,0.11644411],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9960757,0.0020860317,0.000232305,0.00017302406,0.0010219666,0.0004110103],"domain_scores_gemma":[0.99324435,0.0029369008,0.0010199542,0.000600364,0.0011387058,0.0010598206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036622344,0.0002597148,0.00020616461,0.0005945094,0.004232942,0.0035791798,0.0005612043,0.0015184698,0.021297796],"category_scores_gemma":[0.018433573,0.00018919716,0.00028308306,0.0006716349,0.0016152797,0.0038023242,0.004446057,0.0020630704,0.0049212356],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029734688,0.00024705488,0.030997628,0.0010151553,0.00003360901,0.0053219465,0.51551837,0.00029619652,0.005896453,0.06555723,0.15398374,0.2208352],"study_design_scores_gemma":[0.0000104451,0.00016564087,0.008252227,0.0005174942,0.000012433947,0.0014059737,0.18900266,0.00035563874,0.0022941846,0.00354652,0.79437643,0.00006043607],"about_ca_topic_score_codex":0.0044229683,"about_ca_topic_score_gemma":0.0060559716,"teacher_disagreement_score":0.021297796,"about_ca_system_score_codex":0.0019654676,"about_ca_system_score_gemma":0.001938444,"threshold_uncertainty_score":0.07124817},"labels":[],"label_agreement":null},{"id":"W4304620174","doi":"10.1177/08912416221129880","title":"The Moral Discourse of Free Speech: A Virtual Ethnographic Study","year":2022,"lang":"en","type":"article","venue":"Journal of Contemporary Ethnography","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Authoritarianism; Censorship; Free speech; Sociology; Subjectivity; Ethnography; Social media; Politics; Framing (construction); Media studies; Democracy; Social psychology; Political science; Psychology; Law; Epistemology","score_opus":0.034609311730413164,"score_gpt":0.2765112040303839,"score_spread":0.24190189229997072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304620174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9826968,0.000783319,0.0028969983,0.001387103,0.00010880468,0.00018594389,0.00011339843,0.000022866985,0.011804781],"genre_scores_gemma":[0.9943948,0.000788782,0.0010432696,0.0005189398,0.000063520405,0.000244522,0.00007147085,0.000046165074,0.0028284541],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9863877,0.011291467,0.00026170549,0.00058127893,0.0005738322,0.00090395624],"domain_scores_gemma":[0.9747311,0.021029517,0.0011242583,0.0008043007,0.00076688005,0.001543917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010062885,0.00066493824,0.0007679811,0.0026445114,0.011288968,0.007704497,0.0016086509,0.0019492788,0.0031913132],"category_scores_gemma":[0.017556943,0.00060206855,0.00034781717,0.001976025,0.013519126,0.009242217,0.0090051135,0.0037325555,0.00059279386],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017151502,0.00005620619,0.0015163916,0.000048740756,0.0000024545661,0.00031214478,0.992733,0.000019926092,0.0003038136,0.0016522588,0.00040975172,0.0029281694],"study_design_scores_gemma":[0.0000044201224,0.000042119605,0.0014385383,0.00013015751,0.0000032343335,0.00018952186,0.98490095,0.000106718246,0.00016179404,0.00056027394,0.012450662,0.000011655605],"about_ca_topic_score_codex":0.0040662508,"about_ca_topic_score_gemma":0.0077623096,"teacher_disagreement_score":0.011288968,"about_ca_system_score_codex":0.002457356,"about_ca_system_score_gemma":0.0021766713,"threshold_uncertainty_score":0.053218305},"labels":[],"label_agreement":null},{"id":"W4306318367","doi":"10.31235/osf.io/bzs5e","title":"Overuse of Moral Language Dampens Content Engagement on Social Media","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Morality; Mainstream; Social media; Psychology; Moral disengagement; Social psychology; Lexicon; Doxastic logic; Political science; Epistemology; Linguistics; Law","score_opus":0.11953763432718477,"score_gpt":0.2950554252682244,"score_spread":0.17551779094103964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306318367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882608,0.00010644736,0.0019696888,0.00032970685,0.00002439077,0.000028362516,0.00008067548,0.0000991804,0.009100717],"genre_scores_gemma":[0.99784756,0.00004315665,0.0005973305,0.000106604,0.000021745629,0.00002253629,0.000063992935,0.00004126595,0.0012558034],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971283,0.0012821505,0.0001221504,0.000458396,0.00069565437,0.00031335643],"domain_scores_gemma":[0.9624795,0.02418246,0.006400754,0.0030422956,0.0017078716,0.0021871056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021736047,0.00034136875,0.00032233476,0.0008800346,0.0007229005,0.003114268,0.00037686448,0.00081326126,0.007979671],"category_scores_gemma":[0.04193282,0.0002785786,0.00023999832,0.00045303174,0.001531224,0.003021011,0.0025764534,0.0015464709,0.0016704322],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027247074,0.002142482,0.61904955,0.0010322372,0.00027811195,0.00064655015,0.036439117,0.0025977555,0.08096129,0.010454063,0.004976178,0.23869789],"study_design_scores_gemma":[0.00004715793,0.0016775789,0.9315037,0.00023902385,0.00013722216,0.00047215464,0.013940363,0.014452528,0.014246038,0.012710058,0.010491385,0.000082833365],"about_ca_topic_score_codex":0.00077731226,"about_ca_topic_score_gemma":0.0010423309,"teacher_disagreement_score":0.007979671,"about_ca_system_score_codex":0.00040348523,"about_ca_system_score_gemma":0.00036039844,"threshold_uncertainty_score":0.026694655},"labels":[],"label_agreement":null},{"id":"W4308319116","doi":"10.55574/qgsj9827","title":"A NEW CYBERBULLYING LAW? EXTENSION OF LEGAL INTERPRETATIONS IN CHINA AND RUSSIA","year":2022,"lang":"en","type":"article","venue":"International Journal of Law Ethics and Technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cyberspace; Impunity; Law; China; Political science; Anonymity; Face (sociological concept); Order (exchange); Power (physics); Sociology; Human rights; Business; The Internet; Computer science","score_opus":0.012845068157257451,"score_gpt":0.2824879981165476,"score_spread":0.2696429299592902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308319116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766359,0.00062034087,0.00039406418,0.0023436865,0.000034034518,0.000025647025,0.000017332144,0.000010290308,0.019918589],"genre_scores_gemma":[0.9981798,0.0002076466,0.00012745791,0.00012035889,0.0000075980342,0.000006490278,0.000009770408,0.0000016019418,0.0013392003],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790406,0.00070043677,0.00016351853,0.00026833912,0.00048018256,0.00048338593],"domain_scores_gemma":[0.99851364,0.0004641482,0.00049173686,0.00014952227,0.00021000567,0.00017102576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002364948,0.00016589857,0.00032294102,0.001865663,0.005053557,0.004084763,0.0011065039,0.0011615383,0.0016920898],"category_scores_gemma":[0.002954201,0.00023076138,0.00037047046,0.0012694575,0.0095382035,0.0021016232,0.002891339,0.0018589746,0.00008220958],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007902299,0.00015192163,0.14516959,0.0002514618,0.00004168737,0.015083909,0.40849817,0.0010700634,0.0033642154,0.38176394,0.0024570466,0.04206896],"study_design_scores_gemma":[0.0000321589,0.00014011082,0.40029016,0.0008101186,0.000119798344,0.002651765,0.43139714,0.005098964,0.0029912852,0.044443674,0.11186845,0.00015629457],"about_ca_topic_score_codex":0.06922421,"about_ca_topic_score_gemma":0.07622903,"teacher_disagreement_score":0.06922421,"about_ca_system_score_codex":0.0079793725,"about_ca_system_score_gemma":0.007511175,"threshold_uncertainty_score":0.13764256},"labels":[],"label_agreement":null},{"id":"W4309236126","doi":"10.1007/s10943-022-01692-5","title":"Health, Religiosity and Hatred: A Study of the Impacts of COVID-19 on World Jewry","year":2022,"lang":"en","type":"review","venue":"Journal of Religion and Health","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Antisemitism; Hatred; Pandemic; Public health; Judaism; Coronavirus disease 2019 (COVID-19); Religiosity; Political science; Exploratory research; Mythology; Misinformation; Sociology; History; Development economics; Criminology; Law; Social science; Politics; Medicine; Disease; Economics","score_opus":0.07482992568367909,"score_gpt":0.385522179534981,"score_spread":0.31069225385130195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309236126","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016938739,0.99746954,0.000030012905,0.00035779,0.000062004605,0.000008843988,0.00006048665,0.0000013877362,0.00031604787],"genre_scores_gemma":[0.008059397,0.9913107,0.00013139796,0.00022506602,0.00009296971,0.000010510017,0.00005492725,9.617343e-7,0.000114077375],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995202,0.00016146955,0.00007743236,0.000070276605,0.00013742692,0.00003320924],"domain_scores_gemma":[0.99627256,0.002510096,0.00056033244,0.00006006363,0.00044981574,0.00014721087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001842519,0.0005446697,0.0016562869,0.001618374,0.0003688631,0.0010264907,0.0006349007,0.00091903383,0.003138314],"category_scores_gemma":[0.0044588577,0.00031086826,0.0012840788,0.002784687,0.00066237064,0.0007663475,0.00083859527,0.0012371801,0.00033042702],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000325411,0.00019897117,0.012268182,0.056062862,0.0021750093,0.000071448965,0.0004772051,0.00010173488,0.00014765639,0.0010487429,0.006857047,0.9202657],"study_design_scores_gemma":[0.0003268979,0.0018861635,0.33260706,0.2212716,0.01572324,0.0015598818,0.004382144,0.00042673195,0.0006672668,0.003069512,0.41784042,0.0002391154],"about_ca_topic_score_codex":0.0150133455,"about_ca_topic_score_gemma":0.045430012,"teacher_disagreement_score":0.0150133455,"about_ca_system_score_codex":0.00077592506,"about_ca_system_score_gemma":0.0025208835,"threshold_uncertainty_score":0.029851913},"labels":[],"label_agreement":null},{"id":"W4310038929","doi":"10.21810/jicw.v5i2.5071","title":"Implications of Misinformation for Public Order Policing","year":2022,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Misinformation; Presentation (obstetrics); Reputation; Order (exchange); Sociology; Criminology; Public relations; Political science; Media studies; Law; Social science; Business; Medicine","score_opus":0.046315272866235664,"score_gpt":0.2828880680660368,"score_spread":0.23657279519980112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310038929","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010794021,0.0106542,0.0007474778,0.9346478,0.004068158,0.000023712322,0.000054623728,0.00003127431,0.03897869],"genre_scores_gemma":[0.7096733,0.03238746,0.0014261875,0.22377318,0.011080055,0.00007544186,0.00006136744,0.00006032782,0.021462722],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97985166,0.01282659,0.0005679115,0.0007650485,0.0041348822,0.0018538742],"domain_scores_gemma":[0.9205548,0.054289453,0.007503531,0.0015911955,0.011810275,0.004250746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013585913,0.00048034187,0.0003913299,0.0024807635,0.011594444,0.011840695,0.0011794812,0.00953519,0.009776155],"category_scores_gemma":[0.08766706,0.00047012593,0.00066953484,0.0017489068,0.010189189,0.006991248,0.003935532,0.011455414,0.0014994186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028727422,0.0001382141,0.012589547,0.000475424,0.000063666674,0.005139848,0.047139708,0.0014235278,0.00030634445,0.15939972,0.5916531,0.1813836],"study_design_scores_gemma":[0.00004867997,0.00013604104,0.009316791,0.002740242,0.00005241089,0.0028000448,0.07966323,0.0011277903,0.0008680275,0.10260631,0.80043554,0.0002049818],"about_ca_topic_score_codex":0.023358341,"about_ca_topic_score_gemma":0.04144986,"teacher_disagreement_score":0.023358341,"about_ca_system_score_codex":0.016721379,"about_ca_system_score_gemma":0.012658607,"threshold_uncertainty_score":0.12132269},"labels":[],"label_agreement":null},{"id":"W4310934721","doi":"10.1145/3570748.3570753","title":"Exploring Crowdsourced Content Moderation Through Lens of Reddit during COVID-19","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Moderation; Coronavirus disease 2019 (COVID-19); Computer science; User-generated content; Crowdsourcing; Lens (geology); Content (measure theory); World Wide Web; Social media; Optics; Physics; Mathematics; Medicine","score_opus":0.2327180794807586,"score_gpt":0.269048175474353,"score_spread":0.03633009599359441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310934721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9538661,0.0007365908,0.0072417622,0.0018368835,0.00017271703,0.0002693677,0.0029311215,0.000258362,0.03268702],"genre_scores_gemma":[0.991012,0.00017655765,0.004612509,0.0002739441,0.00009722498,0.00016415835,0.0007277386,0.000083806946,0.0028520378],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99764365,0.0012235005,0.00007189526,0.00040086845,0.0004798572,0.00018012313],"domain_scores_gemma":[0.9746766,0.017433656,0.0030838551,0.002294134,0.001804885,0.00070681924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003521621,0.00035201298,0.00034348952,0.0026415624,0.0018273975,0.0025880765,0.0005258439,0.00077731936,0.0031521313],"category_scores_gemma":[0.01858295,0.00020465869,0.00022136171,0.0019911486,0.0012679102,0.002253529,0.002487301,0.00086110045,0.00066202134],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013693151,0.00044738982,0.4710156,0.0020120046,0.0002476208,0.0021090053,0.27835053,0.0031743648,0.016133172,0.014591673,0.023776324,0.18677297],"study_design_scores_gemma":[0.00014514937,0.00051867386,0.6391007,0.0008912065,0.00019746038,0.0006943546,0.14496365,0.02652493,0.008185594,0.012841251,0.1656977,0.00023941678],"about_ca_topic_score_codex":0.008013025,"about_ca_topic_score_gemma":0.016374331,"teacher_disagreement_score":0.008013025,"about_ca_system_score_codex":0.0012547176,"about_ca_system_score_gemma":0.0008066384,"threshold_uncertainty_score":0.018624365},"labels":[],"label_agreement":null},{"id":"W4311192871","doi":"10.36227/techrxiv.21708188","title":"Regional language toxic comment classification","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Marathi; Popularity; Social media; Hindi; Computer science; Gujarati; Focus (optics); Entertainment; Artificial intelligence; Natural language processing; Data science; World Wide Web; Political science; Linguistics","score_opus":0.03997279582722367,"score_gpt":0.2830121337848864,"score_spread":0.2430393379576627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311192871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54640174,0.0035677687,0.009216323,0.001988585,0.0013471291,0.00069069857,0.36278495,0.008810636,0.06519214],"genre_scores_gemma":[0.53440094,0.0008007861,0.012948342,0.000409123,0.0004415708,0.00046551233,0.40771824,0.00045636526,0.042359155],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893945,0.00017957891,0.000104027,0.00028439873,0.00023869747,0.00025384602],"domain_scores_gemma":[0.9978047,0.00039535956,0.00023265409,0.00037071554,0.0009751399,0.00022148556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058347563,0.0009892436,0.00050121633,0.0040426096,0.000866137,0.0011562097,0.00081739516,0.0007757073,0.016076809],"category_scores_gemma":[0.002728999,0.00012296303,0.0010624167,0.002954461,0.00028912368,0.0007469367,0.00095317425,0.0006823982,0.015766282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010780826,0.0004892579,0.15846503,0.0018983602,0.00027237393,0.0019149863,0.0013860186,0.004828287,0.019400666,0.0022153181,0.48851553,0.31953603],"study_design_scores_gemma":[0.00011956945,0.00047358152,0.32010338,0.00044723388,0.00031769995,0.0020756924,0.007719635,0.08070162,0.028993832,0.0018440107,0.5570114,0.00019235934],"about_ca_topic_score_codex":0.025770638,"about_ca_topic_score_gemma":0.04225338,"teacher_disagreement_score":0.025770638,"about_ca_system_score_codex":0.00095798646,"about_ca_system_score_gemma":0.0011228075,"threshold_uncertainty_score":0.053782284},"labels":[],"label_agreement":null},{"id":"W4311204652","doi":"10.36227/techrxiv.21708188.v1","title":"Regional language toxic comment classification","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Marathi; Popularity; Hindi; Social media; Focus (optics); Computer science; Gujarati; Entertainment; Artificial intelligence; Natural language processing; Data science; World Wide Web; Political science; Linguistics","score_opus":0.03997279582722367,"score_gpt":0.2830121337848864,"score_spread":0.2430393379576627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311204652","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65712786,0.0041969167,0.01247574,0.0018304416,0.0015258677,0.000706133,0.26073334,0.0073708184,0.05403284],"genre_scores_gemma":[0.6391057,0.00089239643,0.015898809,0.00037285752,0.0005380672,0.00047064212,0.30507192,0.0004204562,0.037229165],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988844,0.00019206166,0.0001145423,0.00028296144,0.00026719828,0.00025880017],"domain_scores_gemma":[0.9974923,0.00046259112,0.00026865947,0.00038161423,0.0011536705,0.00024121464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000676592,0.0010282951,0.0005307594,0.003679797,0.00086132734,0.0011128628,0.0007795451,0.00080411386,0.011650216],"category_scores_gemma":[0.003050189,0.000113331604,0.0010450443,0.00245766,0.00028902665,0.0007498207,0.00095009065,0.0006929913,0.010562034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014954027,0.0006014347,0.21407083,0.002238344,0.00038770173,0.0024690623,0.0015308316,0.006715756,0.023584895,0.0024868392,0.3930806,0.35133827],"study_design_scores_gemma":[0.00014127177,0.0005696517,0.34217817,0.00052205677,0.00042384304,0.0026633046,0.00845576,0.10995747,0.03428672,0.0022483626,0.4983357,0.00021768374],"about_ca_topic_score_codex":0.017684512,"about_ca_topic_score_gemma":0.030204672,"teacher_disagreement_score":0.017684512,"about_ca_system_score_codex":0.00079640927,"about_ca_system_score_gemma":0.0010468309,"threshold_uncertainty_score":0.03897381},"labels":[],"label_agreement":null},{"id":"W4311681158","doi":"10.22215/etd/2022-15230","title":"Comparative Evaluation on Effect of ELMo in Combination with Machine Learning, and Ensemble Models","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Word embedding; Machine learning; Word (group theory); Artificial intelligence; Ensemble learning; Process (computing); Embedding; Natural language processing; Mathematics","score_opus":0.015246856293217429,"score_gpt":0.293212676833301,"score_spread":0.27796582054008356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311681158","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6957713,0.048608378,0.1960091,0.0049484656,0.005170574,0.00072598015,0.004833924,0.00975881,0.03417349],"genre_scores_gemma":[0.8920525,0.004927988,0.08215983,0.00095951866,0.0006869781,0.00027102587,0.00877738,0.00058828306,0.009576579],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99738795,0.0010425341,0.00022275189,0.0005988129,0.0004943734,0.00025368226],"domain_scores_gemma":[0.99475807,0.0026771834,0.00017661543,0.00043098698,0.0016697258,0.0002875497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065271948,0.0031757804,0.00254147,0.0025994268,0.0012613324,0.0020022593,0.0018797447,0.002151244,0.004096016],"category_scores_gemma":[0.00893033,0.00046375944,0.0017356789,0.0012470952,0.00045897788,0.0026582931,0.0013364817,0.0018685049,0.0013140586],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004509391,0.0022474714,0.021391917,0.0011368517,0.0021434368,0.0002564954,0.00018081695,0.36128882,0.0036816068,0.0014182655,0.033062734,0.5686822],"study_design_scores_gemma":[0.00012843176,0.0008223462,0.0050049336,0.000098569195,0.00061072485,0.00009034803,0.00020457775,0.98510104,0.0038529437,0.0008961532,0.003139591,0.000050319497],"about_ca_topic_score_codex":0.017239433,"about_ca_topic_score_gemma":0.020120904,"teacher_disagreement_score":0.017239433,"about_ca_system_score_codex":0.001097135,"about_ca_system_score_gemma":0.0015394386,"threshold_uncertainty_score":0.034519553},"labels":[],"label_agreement":null},{"id":"W4311707726","doi":"10.1080/01639625.2022.2154179","title":"The Group Nature of Academic Dishonesty &amp; Diffusion of Responsibility in Online Student Chat Groups","year":2022,"lang":"en","type":"article","venue":"Deviant Behavior","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Academic dishonesty; Dishonesty; Cheating; Academic integrity; Psychology; Deviance (statistics); Context (archaeology); Social psychology; Computer science","score_opus":0.0159764267436487,"score_gpt":0.2987468349855003,"score_spread":0.2827704082418516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311707726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9789012,0.000089578774,0.0069315718,0.0007122903,0.000011521558,0.000046461424,0.0000089597615,0.00001751518,0.013280813],"genre_scores_gemma":[0.999292,0.000017150343,0.0004385027,0.000012225651,0.0000028495829,0.000008004416,0.0000023956975,0.0000015823152,0.00022524972],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99395484,0.004267221,0.00020476751,0.00044018164,0.0007998663,0.0003330552],"domain_scores_gemma":[0.9693759,0.017654756,0.0068921647,0.0031635263,0.0012934048,0.0016201895],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00502975,0.00022728422,0.00023816832,0.0016187185,0.0020073948,0.0024934213,0.0007488075,0.0009371217,0.0022580686],"category_scores_gemma":[0.024553463,0.00026180115,0.000351964,0.00055459695,0.0063703978,0.0032989462,0.0029229883,0.0011238931,0.00013924995],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003246908,0.0006762328,0.39535612,0.00022277025,0.000092511145,0.0019509373,0.4139151,0.001433332,0.0076689357,0.10427403,0.0005681278,0.07351719],"study_design_scores_gemma":[0.00008325254,0.00079296884,0.53430265,0.00032208618,0.00009532133,0.002658963,0.3208245,0.02258483,0.005066214,0.09937549,0.013748758,0.00014487162],"about_ca_topic_score_codex":0.0019066556,"about_ca_topic_score_gemma":0.0020617445,"teacher_disagreement_score":0.9990629,"about_ca_system_score_codex":0.0013125914,"about_ca_system_score_gemma":0.0008826669,"threshold_uncertainty_score":0.026600182},"labels":[],"label_agreement":null},{"id":"W4312084660","doi":"10.1016/j.dib.2022.108832","title":"A curated dataset for hate speech detection on social media text","year":2022,"lang":"en","type":"article","venue":"Data in Brief","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Lakehead University","keywords":"Computer science; Slang; Social media; Natural language processing; Preprocessor; Vocabulary; Classifier (UML); Sentence; Artificial intelligence; Perplexity; Speech recognition; Information retrieval; World Wide Web; Linguistics; Language model","score_opus":0.059261112135079716,"score_gpt":0.2942761866840025,"score_spread":0.2350150745489228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312084660","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03491829,0.0014849403,0.005032131,0.00060129474,0.00076537236,0.00091766147,0.9434591,0.0050868983,0.0077343676],"genre_scores_gemma":[0.0107117975,0.00020886962,0.0052757883,0.00014766661,0.00009123941,0.00075565325,0.9802201,0.00012607452,0.0024627256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974649,0.0004721286,0.0003835795,0.000608898,0.00081133685,0.00025917706],"domain_scores_gemma":[0.99668723,0.0009026955,0.000367842,0.0006510928,0.0010719637,0.0003192277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011436848,0.002342935,0.0010710408,0.004306301,0.0015868753,0.0012814068,0.0017763104,0.0023451787,0.009800845],"category_scores_gemma":[0.005081556,0.00031384855,0.0013076869,0.0026225396,0.00064291985,0.001603915,0.0020668712,0.0018315956,0.017721053],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004992682,0.0008768477,0.010403668,0.0030167296,0.00014414909,0.0008693585,0.0005583881,0.0018722602,0.009349769,0.0011175215,0.8879263,0.08336574],"study_design_scores_gemma":[0.0003000764,0.0005429921,0.092475675,0.0008463951,0.00018039533,0.002184482,0.0016343938,0.01789722,0.015478019,0.0017483991,0.86644584,0.0002660802],"about_ca_topic_score_codex":0.009383424,"about_ca_topic_score_gemma":0.023214782,"teacher_disagreement_score":0.009800845,"about_ca_system_score_codex":0.0012066978,"about_ca_system_score_gemma":0.0015225477,"threshold_uncertainty_score":0.032787085},"labels":[],"label_agreement":null},{"id":"W4312119814","doi":"","title":"LA CYBERJUSTICE COMME RÉPONSE AUX BESOINS JURIDIQUES DES PERSONNES ITINÉRANTES: SON POTENTIEL ET SES EMBÛCHES","year":2013,"lang":"fr","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Political science","score_opus":0.26898899250842756,"score_gpt":0.4981316762764725,"score_spread":0.22914268376804492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312119814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47634673,0.052162156,0.015125083,0.07712238,0.0013231385,0.00014891704,0.00048844155,0.00007444513,0.37720874],"genre_scores_gemma":[0.97300184,0.011496299,0.0009796479,0.0019889055,0.000425414,0.00008117415,0.00006731428,0.000020182617,0.0119392155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99537647,0.0021612085,0.00021735087,0.00052753487,0.0010725601,0.00064482086],"domain_scores_gemma":[0.98594713,0.007373125,0.0033094985,0.00061176467,0.00177228,0.0009861378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004062531,0.0003313238,0.00047616445,0.0024693322,0.0029926284,0.007539441,0.0009591239,0.003130223,0.013818583],"category_scores_gemma":[0.020502262,0.00026572336,0.00047232036,0.0023281558,0.010691367,0.006782816,0.00435275,0.0044057206,0.00084489834],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013871092,0.0001853642,0.06135221,0.0010137944,0.000092799324,0.001557278,0.09428482,0.00082678214,0.0009553532,0.7188918,0.008076891,0.112624146],"study_design_scores_gemma":[0.000033115946,0.00038445403,0.27710864,0.0056129983,0.00015748596,0.0036025885,0.14544049,0.001578728,0.0019227625,0.26272988,0.30108568,0.00034319682],"about_ca_topic_score_codex":0.00918291,"about_ca_topic_score_gemma":0.012125606,"teacher_disagreement_score":0.013818583,"about_ca_system_score_codex":0.0036989301,"about_ca_system_score_gemma":0.003514209,"threshold_uncertainty_score":0.046227813},"labels":[],"label_agreement":null},{"id":"W4313136413","doi":"10.1177/20539517221143361","title":"Cognitive assemblages: The entangled nature of algorithmic content moderation","year":2022,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; International Centre for Comparative Criminology","funders":"Fondation du Risque; Institut Mines-Télécom","keywords":"Moderation; Computer science; Cognition; Limiting; Social media; Set (abstract data type); Data science; Computer security; Internet privacy; Cognitive science; World Wide Web; Psychology","score_opus":0.09870896551830664,"score_gpt":0.2820379095833584,"score_spread":0.18332894406505174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313136413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45888907,0.001311083,0.32330292,0.010612531,0.00029285884,0.0002802748,0.00022464673,0.000533377,0.20455323],"genre_scores_gemma":[0.97701615,0.00015811251,0.020151366,0.00030050648,0.00007624196,0.00012247152,0.000062475614,0.00009743027,0.002015162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9907554,0.005160137,0.0003089269,0.0015005809,0.0016767625,0.0005982114],"domain_scores_gemma":[0.96978945,0.018268188,0.0037354173,0.0050161043,0.0020365166,0.0011543109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009252233,0.0005856033,0.00045098207,0.0039308546,0.0042240517,0.008843522,0.0014201108,0.0017161669,0.0058064754],"category_scores_gemma":[0.039272293,0.00058564404,0.00060165324,0.0019591304,0.01914292,0.01160808,0.008897737,0.0022968932,0.0006198472],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015752534,0.0000791634,0.031615578,0.00022845952,0.0000999791,0.00045152454,0.102858864,0.0022332387,0.004503121,0.79559404,0.0018867335,0.060291827],"study_design_scores_gemma":[0.0000692496,0.00013520119,0.03212109,0.00025639994,0.00009704374,0.0005172457,0.038969174,0.01281586,0.0030297756,0.8503305,0.06152706,0.00013131445],"about_ca_topic_score_codex":0.0020458272,"about_ca_topic_score_gemma":0.0018804003,"teacher_disagreement_score":0.009252233,"about_ca_system_score_codex":0.0023484689,"about_ca_system_score_gemma":0.0019548119,"threshold_uncertainty_score":0.048931062},"labels":[],"label_agreement":null},{"id":"W4313690393","doi":"10.2139/ssrn.4317451","title":"Legal Remedies for Online Attacks: Young People’s Perspectives","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Internet privacy; Political science; Law; Computer security; Criminology; Psychology; Computer science","score_opus":0.006049373321593611,"score_gpt":0.23931377928557118,"score_spread":0.23326440596397757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313690393","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14717467,0.040777966,0.00090498495,0.7211473,0.002553314,0.00006533725,0.0002462815,0.000014816105,0.08711535],"genre_scores_gemma":[0.83603275,0.03419235,0.00042590007,0.11614105,0.0028654113,0.00006693582,0.00007739068,0.000024965999,0.010173298],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99578696,0.0014083254,0.00026128622,0.00032279556,0.0008369379,0.001383718],"domain_scores_gemma":[0.9728459,0.013825364,0.0030576235,0.0003193107,0.004270265,0.00568158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008516906,0.00022760972,0.00051454373,0.0024976446,0.0057377717,0.007564053,0.0012843497,0.0104402825,0.0076584495],"category_scores_gemma":[0.028246919,0.0003188833,0.0005799438,0.0012968365,0.007993861,0.0066845505,0.0038042811,0.010148933,0.000723072],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017173521,0.0008278245,0.09320495,0.0010224989,0.000096384996,0.0055405074,0.20504265,0.00038065243,0.0010772275,0.34805954,0.11440668,0.23016948],"study_design_scores_gemma":[0.00006945845,0.00042218372,0.06963196,0.006113309,0.000102782345,0.003169821,0.3335255,0.00037289772,0.0007312111,0.05467575,0.5310584,0.00012670748],"about_ca_topic_score_codex":0.03445803,"about_ca_topic_score_gemma":0.057941254,"teacher_disagreement_score":0.03445803,"about_ca_system_score_codex":0.004317567,"about_ca_system_score_gemma":0.010000065,"threshold_uncertainty_score":0.06851494},"labels":[],"label_agreement":null},{"id":"W4318613404","doi":"10.2139/ssrn.4325513","title":"Popularity Bias in Anonymous Matching Markets: Efficiency, Errors, and Fake Views","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Queen's University","funders":"","keywords":"Popularity; Matching (statistics); Anonymity; Computer science; Internet privacy; Psychology; Computer security; Social psychology; Statistics; Mathematics","score_opus":0.036372612876672775,"score_gpt":0.2762988297243136,"score_spread":0.23992621684764082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318613404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84782135,0.0009389296,0.12682404,0.00532773,0.00010619183,0.00019385567,0.0007566229,0.00033441634,0.017696813],"genre_scores_gemma":[0.99390584,0.00015559499,0.0038757261,0.00015885354,0.00009835852,0.000035575296,0.00008861901,0.000028339819,0.0016532016],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.981125,0.011080765,0.0009841375,0.002575687,0.0030927702,0.0011416473],"domain_scores_gemma":[0.52631557,0.37324464,0.05271348,0.0374433,0.008231068,0.0020519025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042364657,0.00045134246,0.0024780356,0.0024815176,0.0014922518,0.0059894663,0.0025182697,0.0035953806,0.009249188],"category_scores_gemma":[0.28796682,0.0009279043,0.0007745148,0.0036123386,0.0046079205,0.011742519,0.002736816,0.0025154755,0.0010740883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024762314,0.00064013945,0.24999101,0.0005074957,0.00072585535,0.00066824234,0.0035187649,0.047449008,0.0022195769,0.5164775,0.010012642,0.16531345],"study_design_scores_gemma":[0.00035221782,0.00028909981,0.049359433,0.000105055435,0.00025831256,0.00057622016,0.0011414078,0.28395978,0.0033426469,0.65725917,0.0032328754,0.0001237734],"about_ca_topic_score_codex":0.0023637186,"about_ca_topic_score_gemma":0.0014831044,"teacher_disagreement_score":0.042364657,"about_ca_system_score_codex":0.0020061075,"about_ca_system_score_gemma":0.0017230609,"threshold_uncertainty_score":0.22404838},"labels":[],"label_agreement":null},{"id":"W4318659828","doi":"10.4337/jlp.2022.02.01","title":"Heckling and free speech","year":2022,"lang":"en","type":"article","venue":"Journal of Legal Philosophy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Free speech; Presentation (obstetrics); Politics; First amendment; Accountability; State (computer science); Expression (computer science); Law; Sociology; Political science; Psychology; Computer science; Medicine","score_opus":0.00982486995447335,"score_gpt":0.20984927124861114,"score_spread":0.2000244012941378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318659828","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09148519,0.012847063,0.048423313,0.03729536,0.0010446998,0.00008043688,0.00009757279,0.00022651503,0.80849975],"genre_scores_gemma":[0.9441018,0.0018148589,0.0027860445,0.005159782,0.0005784211,0.00004832569,0.00003463914,0.00007708671,0.045398973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9922854,0.0039653024,0.00024265207,0.0011966813,0.0013864685,0.00092348806],"domain_scores_gemma":[0.9850419,0.01095427,0.0012243795,0.0015142987,0.0007556831,0.00050954305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005937615,0.0005067323,0.0003906238,0.0016770654,0.005529687,0.0069152275,0.0007950561,0.0034535087,0.009951582],"category_scores_gemma":[0.015826626,0.00030011692,0.00040762944,0.00096996495,0.035986274,0.009183085,0.0039653596,0.0045125377,0.0014174397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026003321,0.000018374132,0.00079817127,0.00004292173,0.0000047947647,0.00010971816,0.0097492235,0.000119771765,0.00012279264,0.96666425,0.004335245,0.018008733],"study_design_scores_gemma":[0.00001767669,0.00005402241,0.002143793,0.00023215025,0.000014942967,0.0004722515,0.007037044,0.0006798963,0.0009640235,0.8279264,0.16040613,0.000051690917],"about_ca_topic_score_codex":0.0031402286,"about_ca_topic_score_gemma":0.0028462044,"teacher_disagreement_score":0.009951582,"about_ca_system_score_codex":0.0032394533,"about_ca_system_score_gemma":0.0013729563,"threshold_uncertainty_score":0.0332914},"labels":[],"label_agreement":null},{"id":"W4320733729","doi":"10.1108/intr-07-2022-0563","title":"Examining the motivations of sharing political deepfake videos: the role of political brand hate and moral consciousness","year":2023,"lang":"en","type":"article","venue":"Internet Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Ideology; Politics; Harm; Consciousness; Political consciousness; Social psychology; Psychology; Political science; Sociology; Public relations; Law","score_opus":0.08443513135309778,"score_gpt":0.34202910868213354,"score_spread":0.2575939773290358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320733729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961772,0.000044630335,0.00039674283,0.00016524829,0.000005401324,0.000033907894,0.000038120506,0.000004093707,0.0031347163],"genre_scores_gemma":[0.99880064,0.000059368933,0.00042901273,0.00006093358,0.000005477917,0.00002522104,0.000034507746,0.0000024786787,0.00058237993],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99859375,0.00070511515,0.00007857247,0.00009998762,0.00027269777,0.00024988767],"domain_scores_gemma":[0.977242,0.012667809,0.006584519,0.0007100597,0.0013689717,0.0014267386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023320308,0.0002142053,0.00017127945,0.000646154,0.0008200321,0.0022010994,0.00031563063,0.0005491779,0.004061422],"category_scores_gemma":[0.019403791,0.00020124196,0.00034252246,0.00038539746,0.00086156576,0.0014652244,0.00087787054,0.0010214959,0.00037191706],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034422879,0.0014023352,0.8959832,0.00048177154,0.00009531816,0.0005027141,0.04180582,0.00028172944,0.003971895,0.0028874993,0.0009141415,0.051329274],"study_design_scores_gemma":[0.00002023686,0.0005232294,0.90872383,0.0001961681,0.00008882133,0.0003247766,0.08059481,0.0023289374,0.0019407052,0.001280518,0.0039161453,0.00006172667],"about_ca_topic_score_codex":0.0030216144,"about_ca_topic_score_gemma":0.005029268,"teacher_disagreement_score":0.004061422,"about_ca_system_score_codex":0.00079525687,"about_ca_system_score_gemma":0.0010908621,"threshold_uncertainty_score":0.013586819},"labels":[],"label_agreement":null},{"id":"W4320920881","doi":"10.3390/info14020124","title":"Modeling and Moderation of COVID-19 Social Network Chat","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Canadian Institutes of Health Research","keywords":"Misinformation; Computer science; Moderation; Conversation; Social media; Newspaper; PageRank; Hidden Markov model; Coronavirus disease 2019 (COVID-19); Casual; World Wide Web; Internet privacy; Artificial intelligence; Machine learning; Psychology; Computer security; Sociology; Communication","score_opus":0.023118616562597637,"score_gpt":0.2566452818126749,"score_spread":0.23352666525007723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320920881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8686244,0.00014292184,0.12853186,0.0003219077,0.000029347468,0.00010457273,0.00039149067,0.0003950333,0.0014584814],"genre_scores_gemma":[0.9835934,0.00004661077,0.015280577,0.000018726465,0.000019795902,0.00008490302,0.0002894003,0.000029611112,0.0006369096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990262,0.0005254788,0.00003941956,0.00022463386,0.0000937379,0.00009062206],"domain_scores_gemma":[0.9892237,0.008952308,0.0006922843,0.00044703291,0.0004142532,0.00027038876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002695498,0.00045851804,0.0005170218,0.001226677,0.00060643425,0.0007274976,0.0007298378,0.00070626865,0.001069463],"category_scores_gemma":[0.012482861,0.00043954368,0.0004991985,0.0005109298,0.0005926724,0.0011101628,0.0010031138,0.0010904871,0.00031925534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011768946,0.00058399106,0.33648723,0.0003796498,0.0003545103,0.00096195564,0.011435356,0.48911673,0.019868918,0.019782292,0.0028285233,0.11702398],"study_design_scores_gemma":[0.000007996305,0.00003534315,0.010443935,0.0000109011835,0.000013694809,0.000037024154,0.00022569248,0.98545873,0.0007524909,0.0025727786,0.00042992475,0.000011461577],"about_ca_topic_score_codex":0.007705543,"about_ca_topic_score_gemma":0.011548168,"teacher_disagreement_score":0.007705543,"about_ca_system_score_codex":0.0006778955,"about_ca_system_score_gemma":0.0006026823,"threshold_uncertainty_score":0.015321374},"labels":[],"label_agreement":null},{"id":"W4321437518","doi":"10.1007/s11245-023-09898-2","title":"A Spoonful of Sugar Makes the Hate Speech Go Down: Sugar-Coating in White Nationalist Recruitment Speech","year":2023,"lang":"en","type":"article","venue":"Topoi","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nationalism; White (mutation); Sugar; Utterance; Rhetoric; Coating; Linguistics; Aesthetics; Psychology; Sociology; Art; Food science; Philosophy; Political science; Chemistry; Politics; Law; Biochemistry","score_opus":0.040568833098659836,"score_gpt":0.2811712041164411,"score_spread":0.24060237101778126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321437518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97024137,0.000092803115,0.00080622936,0.0019700727,0.00013806843,0.000029950914,0.00013785069,0.000037184047,0.026546456],"genre_scores_gemma":[0.98646575,0.00008778326,0.00045064752,0.0009559133,0.00004436928,0.0000257172,0.00014071887,0.000032596326,0.011796542],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99924326,0.00024441414,0.000026063135,0.00009213012,0.00022923476,0.00016492653],"domain_scores_gemma":[0.9975815,0.0009510716,0.00039453222,0.00012393783,0.00047335698,0.0004755655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010143245,0.0002077127,0.00019457347,0.000462561,0.0026167824,0.0020950334,0.00036880773,0.0012247651,0.005242996],"category_scores_gemma":[0.0062638833,0.00019908592,0.000099427874,0.00047743428,0.001012713,0.0012772693,0.0013033741,0.0016959937,0.0018587532],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010198677,0.001241034,0.49412617,0.00021728157,0.00006057532,0.0037009607,0.27404687,0.0002966032,0.018682074,0.010514278,0.059952445,0.13614188],"study_design_scores_gemma":[0.000020285679,0.00027278435,0.58178604,0.00023117702,0.00006073016,0.0018405913,0.34205472,0.004071206,0.0072139897,0.0031107557,0.059228633,0.00010903983],"about_ca_topic_score_codex":0.02068741,"about_ca_topic_score_gemma":0.048157793,"teacher_disagreement_score":0.02068741,"about_ca_system_score_codex":0.0009247485,"about_ca_system_score_gemma":0.000878347,"threshold_uncertainty_score":0.041134},"labels":[],"label_agreement":null},{"id":"W4322421454","doi":"10.1073/pnas.2209384120","title":"Is hate speech detection the solution the world wants?","year":2023,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Stakeholder; Key (lock); Computer science; Data science; Voice activity detection; Public relations; Speech processing; Political science; Computer security; Artificial intelligence","score_opus":0.04615980716756638,"score_gpt":0.2977012030647309,"score_spread":0.2515413958971645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322421454","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27418324,0.0053451154,0.09577541,0.42920125,0.0027278615,0.00019553736,0.0005894783,0.00043252966,0.19154955],"genre_scores_gemma":[0.96250564,0.0025972547,0.010585037,0.01589858,0.00057496934,0.00006121144,0.00018793576,0.00016288173,0.007426577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9807232,0.011819781,0.0007242959,0.001421375,0.004055418,0.0012559384],"domain_scores_gemma":[0.8936287,0.06302522,0.013943174,0.00869775,0.017098866,0.003606336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022365164,0.0005991862,0.0007045084,0.0030540715,0.006141086,0.014055305,0.0014376712,0.0052551036,0.0066930093],"category_scores_gemma":[0.10681268,0.0004223019,0.00043265932,0.0017246463,0.013657093,0.01948675,0.0046210005,0.0053226417,0.0022969227],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019626592,0.00021956897,0.13388033,0.0009921774,0.00014112715,0.0010571438,0.17674589,0.0009800235,0.006937075,0.32895675,0.058038954,0.2918547],"study_design_scores_gemma":[0.000026090254,0.00016937025,0.044152167,0.0024751748,0.00013924134,0.00177237,0.33666041,0.006984333,0.012243227,0.31153983,0.28349304,0.0003447469],"about_ca_topic_score_codex":0.005323679,"about_ca_topic_score_gemma":0.005055689,"teacher_disagreement_score":0.022365164,"about_ca_system_score_codex":0.0031593477,"about_ca_system_score_gemma":0.0047194147,"threshold_uncertainty_score":0.118279755},"labels":[],"label_agreement":null},{"id":"W4322747517","doi":"10.21608/jsb.2023.185880.1549","title":"التوظیف السیاسی لخطاب الكراهیة فی تغطیة الریاضة: دراسة حالة لكأس العالم بقطر 2022","year":2023,"lang":"ar","type":"article","venue":"مجلة البحوث الإعلامیة","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science","score_opus":0.01722232065524097,"score_gpt":0.24409205676673268,"score_spread":0.22686973611149172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322747517","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008622586,0.002701039,0.0013290296,0.009586942,0.0024501765,0.0001118205,0.0005737302,0.00022622812,0.9743983],"genre_scores_gemma":[0.044617545,0.0017985043,0.0010539418,0.0029810339,0.00038017533,0.00017359725,0.00036254153,0.0003254806,0.9483072],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990663,0.00017381668,0.000051449886,0.00011705255,0.00039380544,0.00019752326],"domain_scores_gemma":[0.999297,0.00020887921,0.000039527422,0.00007469299,0.00029277755,0.00008707448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008353955,0.00054143585,0.0001979172,0.00061171973,0.003478009,0.0069552734,0.00044350163,0.0019904682,0.18090862],"category_scores_gemma":[0.0032726603,0.0004456664,0.00024616078,0.0008717535,0.002680872,0.0019642932,0.0017395838,0.004382143,0.12749834],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034464538,0.00011850158,0.0035230638,0.0005302659,0.000020824677,0.00093894976,0.017868228,0.00031958162,0.008736539,0.34920162,0.48959735,0.12880033],"study_design_scores_gemma":[0.0000079392385,0.000020812085,0.0042317132,0.00011404501,0.0000031386387,0.0001291358,0.004210657,0.000048736154,0.0016429962,0.0054180133,0.9841559,0.0000169102],"about_ca_topic_score_codex":0.013339932,"about_ca_topic_score_gemma":0.021836147,"teacher_disagreement_score":0.18090862,"about_ca_system_score_codex":0.0033873841,"about_ca_system_score_gemma":0.003322013,"threshold_uncertainty_score":0.60519964},"labels":[],"label_agreement":null},{"id":"W4322754941","doi":"10.5206/fpq/2022.3/4.14295","title":"Online Extremism, AI, and (Human) Content Moderation","year":2022,"lang":"en","type":"article","venue":"Feminist Philosophy Quarterly","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Moderation; Content (measure theory); Psychology; Internet privacy; Computer science; Social psychology","score_opus":0.03412652545692186,"score_gpt":0.24719220718595603,"score_spread":0.21306568172903417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322754941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5454176,0.0019189242,0.052451577,0.030100321,0.0005062142,0.00015607804,0.00014078117,0.00025038017,0.36905813],"genre_scores_gemma":[0.991392,0.0002330292,0.002578471,0.00097639917,0.00014457139,0.000048056663,0.000020993399,0.000036125275,0.0045704185],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9906213,0.0066270367,0.00018681696,0.00079291005,0.0011687657,0.00060313824],"domain_scores_gemma":[0.9589758,0.029669976,0.0057943854,0.0033277767,0.0012904353,0.00094166445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008973484,0.0003641883,0.0002899916,0.0017862882,0.004014413,0.0068595964,0.000835575,0.0019519522,0.0074771326],"category_scores_gemma":[0.0285665,0.00026159605,0.00020565807,0.0011591895,0.015749715,0.007269478,0.004472609,0.0025166245,0.0005710476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001737095,0.0001414545,0.033335593,0.00031329616,0.000052967695,0.0004868603,0.111014284,0.00071913644,0.0030773205,0.7676527,0.0069535044,0.076079115],"study_design_scores_gemma":[0.00007314314,0.00036293786,0.051598337,0.000847966,0.00008932511,0.0013819563,0.12656985,0.006342193,0.008183694,0.47724968,0.32715008,0.00015085196],"about_ca_topic_score_codex":0.00078189606,"about_ca_topic_score_gemma":0.0009617108,"teacher_disagreement_score":0.008973484,"about_ca_system_score_codex":0.001960188,"about_ca_system_score_gemma":0.0008003631,"threshold_uncertainty_score":0.04745686},"labels":[],"label_agreement":null},{"id":"W4323544814","doi":"10.1177/17504813231156748","title":"A corpus-assisted discourse analysis of the representation of Syrian refugees in Canadian newspapers","year":2023,"lang":"en","type":"article","venue":"Discourse & Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Refugee; Newspaper; Critical discourse analysis; Ideology; Representation (politics); Immigration; Discourse analysis; Politics; Syrian refugees; Sociology; Corpus linguistics; Media studies; Gender studies; Political science; Linguistics; Law","score_opus":0.021816063128375623,"score_gpt":0.32118269494503254,"score_spread":0.2993666318166569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323544814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9513287,0.0015307828,0.0023550827,0.0013835684,0.00013079283,0.00050496566,0.011341149,0.00012643842,0.031298578],"genre_scores_gemma":[0.96873885,0.0020740088,0.010042758,0.00024663453,0.000073310875,0.0004717965,0.0069386447,0.00009995812,0.011314049],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9981951,0.00042376842,0.00009699781,0.00027763835,0.00067555066,0.00033093223],"domain_scores_gemma":[0.9890515,0.004431748,0.0008661864,0.00041996935,0.0047101374,0.0005203159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002942361,0.00054695405,0.00038437158,0.01615952,0.0087009445,0.004309772,0.0008688006,0.00054165523,0.003564546],"category_scores_gemma":[0.009522945,0.00026472376,0.00021297201,0.019794496,0.0037777422,0.0012205278,0.0020754188,0.00083149335,0.00036866785],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002507878,0.00008851094,0.032958113,0.0008995722,0.000026165228,0.0013963069,0.8424826,0.00024067523,0.012814449,0.006575712,0.011772592,0.090494476],"study_design_scores_gemma":[0.000016817097,0.000048118174,0.1427694,0.0005255294,0.00006678394,0.0003475638,0.6657804,0.0009371201,0.004592546,0.000552913,0.18426819,0.00009458621],"about_ca_topic_score_codex":0.8886711,"about_ca_topic_score_gemma":0.93456805,"teacher_disagreement_score":0.1113289,"about_ca_system_score_codex":0.025115252,"about_ca_system_score_gemma":0.03758934,"threshold_uncertainty_score":0.22396904},"labels":[],"label_agreement":null},{"id":"W4360989207","doi":"10.18280/ria.370122","title":"An Ensemble Approach for Cyber Bullying: Text Messages and Images","year":2023,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.05758539978429706,"score_gpt":0.2938051088227666,"score_spread":0.23621970903846956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360989207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38327688,0.009609251,0.58609384,0.0015652264,0.0009973933,0.0004511804,0.003921219,0.005253219,0.008831734],"genre_scores_gemma":[0.8258071,0.003167455,0.1550698,0.00028205625,0.0006283867,0.00021953703,0.004818619,0.00014397096,0.00986306],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994711,0.000053479154,0.000038266728,0.00016253987,0.00019313688,0.00008144307],"domain_scores_gemma":[0.99944645,0.000091846596,0.00006681313,0.000068131485,0.00029024956,0.000036458347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050817686,0.0010702601,0.00085730496,0.0024178203,0.00039531523,0.0006482126,0.0008810314,0.0007877965,0.0011103473],"category_scores_gemma":[0.0012694962,0.00022986718,0.0010912322,0.0015416316,0.00020390136,0.0013283746,0.00063743116,0.00085122645,0.0007573831],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038710437,0.00046021125,0.0099975495,0.00024517358,0.00025690635,0.0003542722,0.0002532845,0.026763327,0.033897255,0.00088557793,0.0072903377,0.91920906],"study_design_scores_gemma":[0.000014129577,0.00038568446,0.018246425,0.00005145378,0.00028959333,0.00047942455,0.00039486063,0.9517128,0.017547287,0.0017482594,0.00908786,0.00004214163],"about_ca_topic_score_codex":0.008021611,"about_ca_topic_score_gemma":0.009664377,"teacher_disagreement_score":0.008021611,"about_ca_system_score_codex":0.00042396496,"about_ca_system_score_gemma":0.00040089054,"threshold_uncertainty_score":0.015949845},"labels":[],"label_agreement":null},{"id":"W4361295203","doi":"10.22329/il.v43i1.7304","title":"Whataboutisms: The Good, the Bad and the Ugly","year":2023,"lang":"en","type":"article","venue":"Informal Logic","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rhetorical question; Fallacy; Variety (cybernetics); Relevance (law); Epistemology; Rhetorical device; Function (biology); Rhetoric; Sociology; Economic Justice; Law and economics; Philosophy; Law; Political science; Linguistics; Computer science","score_opus":0.010946402060970613,"score_gpt":0.21668509617720935,"score_spread":0.20573869411623874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361295203","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1152516,0.009729221,0.21179442,0.16533537,0.003395003,0.00024037606,0.0002620743,0.000311722,0.4936802],"genre_scores_gemma":[0.9610001,0.0012204385,0.017475551,0.00707805,0.0004899748,0.0000903072,0.000063105465,0.00016665619,0.012415827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9892658,0.00701666,0.00046734276,0.00083927665,0.0017563269,0.0006545494],"domain_scores_gemma":[0.9790075,0.015357844,0.0024472347,0.0015053111,0.0010284272,0.0006537352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010944464,0.0006825452,0.0006105792,0.0018525108,0.005760779,0.010392251,0.0011215418,0.0050928816,0.004964567],"category_scores_gemma":[0.02923892,0.0004329283,0.0004997365,0.0012422594,0.030486165,0.015216591,0.00554978,0.006527541,0.00081502536],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028571763,0.000013099084,0.0009928751,0.000100890495,0.000010761292,0.00025674966,0.02087577,0.000076441815,0.00047775704,0.9662701,0.0038841078,0.007012786],"study_design_scores_gemma":[0.000014618672,0.00004794124,0.0017180286,0.0007509012,0.000040583578,0.0012226743,0.042287044,0.0011268535,0.0022586698,0.73199624,0.2184802,0.0000562675],"about_ca_topic_score_codex":0.0016528222,"about_ca_topic_score_gemma":0.0024014956,"teacher_disagreement_score":0.010944464,"about_ca_system_score_codex":0.002849758,"about_ca_system_score_gemma":0.0018417657,"threshold_uncertainty_score":0.05788058},"labels":[],"label_agreement":null},{"id":"W4361296415","doi":"10.1017/asjcl.2023.9","title":"Falsehoods, Foreign Interference, and Compelled Speech in Singapore","year":2023,"lang":"en","type":"article","venue":"Asian Journal of Comparative Law","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Jurisprudence; Statute; Doctrine; Political science; Misinformation; Law; Government (linguistics); Constitutional right; Free speech; State (computer science); Sociology; Supreme court; Computer science; Philosophy","score_opus":0.03184545017738778,"score_gpt":0.28088326953833104,"score_spread":0.24903781936094327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361296415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68950915,0.0023003248,0.014520274,0.022478707,0.00028777585,0.00007546029,0.00013111025,0.000061382605,0.2706358],"genre_scores_gemma":[0.99502325,0.0002201305,0.0005066648,0.00077808194,0.000020615007,0.000010372422,0.000012491189,0.000005651308,0.0034226584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9913856,0.0037211431,0.0009754761,0.000783776,0.0024881808,0.0006458427],"domain_scores_gemma":[0.97283417,0.016902508,0.0042732605,0.0021033613,0.0032275897,0.000659053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010918964,0.00022817358,0.00037807465,0.0014769047,0.0049135475,0.009549959,0.0009967254,0.0041969055,0.0031801776],"category_scores_gemma":[0.016884232,0.00039346097,0.00032883498,0.0010662128,0.025827896,0.0040093353,0.004635171,0.004555134,0.0003863213],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031203304,0.000026716873,0.009955534,0.0001277325,0.00001582391,0.0021326365,0.098363854,0.00039982513,0.0010429231,0.87358254,0.0022286666,0.012092444],"study_design_scores_gemma":[0.000060807604,0.00036007992,0.05605921,0.0023739636,0.00020485514,0.0060908045,0.15224975,0.011963417,0.013435984,0.4327028,0.3240098,0.000488447],"about_ca_topic_score_codex":0.03076831,"about_ca_topic_score_gemma":0.034067564,"teacher_disagreement_score":0.03076831,"about_ca_system_score_codex":0.0065508736,"about_ca_system_score_gemma":0.0049704453,"threshold_uncertainty_score":0.061178446},"labels":[],"label_agreement":null},{"id":"W4362502062","doi":"10.1007/978-3-031-28993-4_12","title":"IN[The Hate Booth]: a Gamified Installation to Counteract Hate Speech","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Narrative; Citizen journalism; Sociology; Public relations; Political science; Psychology; Computer science; Multimedia; Internet privacy; Law; Art","score_opus":0.02542210201555556,"score_gpt":0.2513420922261594,"score_spread":0.22591999021060383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362502062","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036671896,0.004063314,0.09094808,0.0070373374,0.004490363,0.0005073928,0.000812433,0.014629703,0.84083945],"genre_scores_gemma":[0.15936618,0.0027910478,0.0811388,0.00460105,0.0004247582,0.0003503128,0.0011029544,0.0021539263,0.748071],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996804,0.00008753785,0.0000084533995,0.000036813268,0.00013114396,0.000055727833],"domain_scores_gemma":[0.99975497,0.00009199989,0.000014261259,0.000036262107,0.000047896945,0.00005455213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047316978,0.00094433373,0.00033905296,0.0005676941,0.0012801046,0.0022124809,0.0012868929,0.0020530957,0.06706582],"category_scores_gemma":[0.0014697375,0.0003303624,0.00023389395,0.00027959427,0.0008111031,0.0024245682,0.001967739,0.0015067102,0.020000119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002445755,0.00043872537,0.00076170324,0.00028998806,0.00001528419,0.0004629848,0.0017249646,0.0005977338,0.0034985675,0.0227034,0.39309573,0.5761663],"study_design_scores_gemma":[0.000059140177,0.0003451194,0.0035577887,0.0004220138,0.000042681248,0.0012342829,0.0017654641,0.0042625703,0.005891636,0.01236502,0.96997815,0.00007607768],"about_ca_topic_score_codex":0.0018673047,"about_ca_topic_score_gemma":0.0063127046,"teacher_disagreement_score":0.06706582,"about_ca_system_score_codex":0.00022577231,"about_ca_system_score_gemma":0.00055523537,"threshold_uncertainty_score":0.22435755},"labels":[],"label_agreement":null},{"id":"W4362570193","doi":"10.1007/978-3-031-24687-6_155","title":"Should Reviewers and Website Moderators Censor Swearwords in Reviews? An Abstract","year":2023,"lang":"en","type":"book-chapter","venue":"Developments in marketing science: proceedings of the Academy of Marketing Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Lethbridge","funders":"","keywords":"Meaning (existential); Product (mathematics); Censorship; Psychology; Political science; Law; Mathematics","score_opus":0.041141728011104224,"score_gpt":0.2947388936765641,"score_spread":0.25359716566545987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362570193","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03344916,0.3662487,0.034842007,0.2212759,0.034237936,0.0006357665,0.00095207716,0.0010806598,0.3072778],"genre_scores_gemma":[0.4658791,0.13079362,0.020885509,0.057507306,0.05227169,0.0009272611,0.00072123046,0.0010506493,0.26996362],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9852328,0.00777115,0.0008030233,0.0015717816,0.0040641488,0.0005570151],"domain_scores_gemma":[0.83308864,0.10923351,0.012541559,0.007899169,0.033030044,0.0042070546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020766165,0.0006064597,0.0011317806,0.0026782204,0.0023940438,0.010957071,0.0011726271,0.004057067,0.014979613],"category_scores_gemma":[0.13284934,0.0006542323,0.0005143905,0.003365253,0.0025011315,0.011050446,0.0017993579,0.0032817584,0.009853707],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003855855,0.00010573015,0.0076299934,0.0025964214,0.00018362224,0.0004569732,0.005675296,0.00033848686,0.0019319676,0.08933591,0.45748147,0.43387857],"study_design_scores_gemma":[0.00010583815,0.0001653248,0.0106553165,0.0029051513,0.00019894716,0.0007392133,0.0033986792,0.0013844747,0.0016421237,0.060872566,0.9177998,0.0001324958],"about_ca_topic_score_codex":0.001674071,"about_ca_topic_score_gemma":0.0036740324,"teacher_disagreement_score":0.020766165,"about_ca_system_score_codex":0.002324331,"about_ca_system_score_gemma":0.0026486018,"threshold_uncertainty_score":0.10982329},"labels":[],"label_agreement":null},{"id":"W4365151898","doi":"10.5210/spir.v2022i0.13008","title":"RECLAIMING DIGITAL INTIMACY FOR YOUTH: PILOTING DIGITAL SEXUAL VIOLENCE WORKSHOPS FOR UNDER-18S DURING THE COVID-19 PANDEMIC IN ENGLAND AND IRELAND","year":2023,"lang":"en","type":"article","venue":"AoIR Selected Papers of Internet Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Western University","funders":"","keywords":"Harassment; Social media; Psychological intervention; Affordance; Digital media; Criminology; Sociology; Psychology; Political science; Social psychology; Law; Psychiatry","score_opus":0.07168981401287819,"score_gpt":0.33703657639169277,"score_spread":0.2653467623788146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365151898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98794043,0.0002983358,0.00073572027,0.0024355173,0.00012061561,0.0017187577,0.00018059916,0.00008100831,0.0064890753],"genre_scores_gemma":[0.9804495,0.00096566725,0.0072404887,0.0017197357,0.000048373702,0.0024323363,0.00024674454,0.00005729189,0.006839912],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9956565,0.0020371533,0.00015481176,0.00020427162,0.0003877916,0.0015595999],"domain_scores_gemma":[0.9927449,0.0014760281,0.0005244482,0.00028699622,0.0009412928,0.0040262397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076257586,0.00047117012,0.0006313776,0.00083491,0.004209869,0.0031850561,0.0021613466,0.001443989,0.0043058475],"category_scores_gemma":[0.008444241,0.0008546379,0.0007819456,0.00043077613,0.002828492,0.0030975977,0.005872945,0.002590489,0.0011359585],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008087903,0.010289246,0.091089204,0.0022476974,0.000067026194,0.0053187087,0.6564793,0.00047166756,0.005524894,0.0015606256,0.014652208,0.2114907],"study_design_scores_gemma":[0.00025785176,0.006665796,0.096844025,0.0010764335,0.00006802633,0.0005218058,0.8423139,0.00037973784,0.001800849,0.0006878289,0.049290013,0.00009378183],"about_ca_topic_score_codex":0.034019608,"about_ca_topic_score_gemma":0.14363295,"teacher_disagreement_score":0.034019608,"about_ca_system_score_codex":0.003773108,"about_ca_system_score_gemma":0.012157147,"threshold_uncertainty_score":0.067643225},"labels":[],"label_agreement":null},{"id":"W4365999109","doi":"10.1145/3579523","title":"Slowing it Down: Towards Facilitating Interpersonal Mindfulness in Online Polarizing Conversations Over Social Media","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Conversation; Mindfulness; Interpersonal communication; Psychology; Psychological intervention; Intervention (counseling); Social media; Moderation; Social psychology; Developmental psychology; Applied psychology; Psychotherapist; Communication; Computer science","score_opus":0.06403190975117384,"score_gpt":0.32598218047739397,"score_spread":0.2619502707262201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365999109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94887006,0.00022900205,0.042066175,0.0008646173,0.000062097744,0.0011334997,0.000035581063,0.0006507959,0.0060881684],"genre_scores_gemma":[0.8985954,0.00026607694,0.097455725,0.00046465843,0.000051958763,0.0018100195,0.000043897227,0.00007847664,0.001233867],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831724,0.0010352298,0.00006287978,0.00026988203,0.00018750712,0.000127335],"domain_scores_gemma":[0.99325573,0.004633709,0.0006725624,0.0006457683,0.00019348381,0.0005987556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031144992,0.0005615285,0.0003062784,0.00061456533,0.0010054375,0.0012814447,0.0008893154,0.00057340093,0.0025520336],"category_scores_gemma":[0.011236707,0.0003202891,0.00030348945,0.0003003777,0.0015758264,0.0014909024,0.002455788,0.00092258974,0.00037870306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030797045,0.011192076,0.018160306,0.00326808,0.00012975329,0.00039687165,0.13479221,0.0015577992,0.26115733,0.0068738926,0.0029311366,0.5564608],"study_design_scores_gemma":[0.0064397347,0.051227655,0.31985933,0.0050322753,0.002386116,0.0026912484,0.12630123,0.042539608,0.26402724,0.04279419,0.13603458,0.0006667454],"about_ca_topic_score_codex":0.00044886273,"about_ca_topic_score_gemma":0.0009447271,"teacher_disagreement_score":0.0031144992,"about_ca_system_score_codex":0.00033934918,"about_ca_system_score_gemma":0.000738934,"threshold_uncertainty_score":0.016471267},"labels":[],"label_agreement":null},{"id":"W4366431497","doi":"10.2139/ssrn.4414249","title":"Information Economics and Free Speech","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Free speech; Economics; Political science; Law","score_opus":0.004859135941822937,"score_gpt":0.19405952366322884,"score_spread":0.1892003877214059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366431497","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2547014,0.018004727,0.034035653,0.09417554,0.00070862874,0.00006661501,0.0007052059,0.00006576129,0.5975365],"genre_scores_gemma":[0.9718242,0.0025069157,0.00045326698,0.0013037759,0.00058902276,0.000017909413,0.00005041122,0.000008489024,0.023245968],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99901426,0.0004937686,0.00003739289,0.00011251836,0.00019574711,0.0001462905],"domain_scores_gemma":[0.98209345,0.014938455,0.0013360889,0.0005430458,0.00058632705,0.00050276006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014911033,0.0002495314,0.0004577043,0.0018837757,0.000943782,0.0038360383,0.0002986541,0.002360944,0.01958633],"category_scores_gemma":[0.010379142,0.00020061166,0.00023384401,0.0014482044,0.0036743574,0.0051547694,0.0009066107,0.0019045044,0.0009240241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042977397,0.00006246087,0.003349537,0.0000447118,0.000018968529,0.00015876748,0.0004000013,0.0012508472,0.00009440768,0.97297204,0.005827518,0.015777651],"study_design_scores_gemma":[0.00001141041,0.000022599725,0.0039034528,0.000047346643,0.000011626918,0.00011324621,0.00062280253,0.0025336659,0.00011854549,0.9833694,0.009235961,0.00000995676],"about_ca_topic_score_codex":0.002918689,"about_ca_topic_score_gemma":0.0023089896,"teacher_disagreement_score":0.01958633,"about_ca_system_score_codex":0.0013441996,"about_ca_system_score_gemma":0.00077916525,"threshold_uncertainty_score":0.06552279},"labels":[],"label_agreement":null},{"id":"W4366597077","doi":"10.1145/3544548.3581017","title":"Transcending the “Male Code”: Implicit Masculine Biases in NLP Contexts","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conflation; Framing (construction); Computer science; Gender bias; Code (set theory); Scholarship; Natural language processing; Psychology; Artificial intelligence; Linguistics; Social psychology; Political science; Set (abstract data type)","score_opus":0.05662655978156533,"score_gpt":0.29582518459812934,"score_spread":0.239198624816564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366597077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7624726,0.0019163776,0.18942642,0.011065697,0.0002525257,0.00009749512,0.00075195864,0.00025399588,0.033762857],"genre_scores_gemma":[0.9837737,0.000234977,0.014058094,0.0008147136,0.000080840764,0.00005379069,0.00015969833,0.00009963025,0.0007245453],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9782882,0.015204726,0.0013049301,0.002481024,0.002392273,0.00032890207],"domain_scores_gemma":[0.9126271,0.067202345,0.0066548116,0.008898072,0.0040867375,0.0005309705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015428658,0.00042189713,0.00038253507,0.0018066991,0.0021438112,0.004443502,0.00067147013,0.00091457315,0.002465248],"category_scores_gemma":[0.07900898,0.00041313903,0.00021972824,0.0017434309,0.010447082,0.008675969,0.004683523,0.0020356479,0.0004257569],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046866122,0.000078882345,0.13266943,0.0010847868,0.00011671025,0.0010939521,0.37158406,0.0015318268,0.0193689,0.23064703,0.0051106326,0.23624511],"study_design_scores_gemma":[0.00007508541,0.00018139878,0.114937454,0.0013743596,0.00011871726,0.0039840993,0.14910986,0.020647122,0.039628457,0.5331371,0.13651592,0.00029053254],"about_ca_topic_score_codex":0.0021038183,"about_ca_topic_score_gemma":0.003281958,"teacher_disagreement_score":0.015428658,"about_ca_system_score_codex":0.0011638014,"about_ca_system_score_gemma":0.0010243527,"threshold_uncertainty_score":0.08159554},"labels":[],"label_agreement":null},{"id":"W4366966833","doi":"10.1109/wi-iat55865.2022.00012","title":"Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conversation; Computer science; Transformer; Social media; Graph; Language model; Context model; Artificial intelligence; Data science; Natural language processing; World Wide Web; Theoretical computer science; Sociology; Communication","score_opus":0.01596948223165954,"score_gpt":0.2265711998922252,"score_spread":0.21060171766056568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366966833","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86064017,0.0008034209,0.1233051,0.0007426433,0.00009962797,0.00013786263,0.0026041488,0.0027502908,0.008916787],"genre_scores_gemma":[0.9784557,0.00013947538,0.018311385,0.000052998796,0.00003961277,0.00003574619,0.0013068077,0.00006217615,0.001595992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948066,0.0001826537,0.000021572818,0.00014757972,0.00009281783,0.000074710355],"domain_scores_gemma":[0.99769247,0.0011911258,0.0003800925,0.00018600209,0.0003416052,0.00020868986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079027936,0.00074286957,0.00028784745,0.0027236405,0.00057714456,0.0009841068,0.0005409084,0.00077655376,0.0017776958],"category_scores_gemma":[0.004414181,0.00027542457,0.00044111203,0.0009439888,0.00028036113,0.002176753,0.0010643627,0.00087651564,0.0010050553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001340625,0.0008435612,0.38161013,0.0005171595,0.0004623853,0.0012540732,0.005225086,0.11688564,0.041710682,0.011111346,0.014090777,0.4249485],"study_design_scores_gemma":[0.000019241741,0.00014330025,0.04170606,0.00003046532,0.00009411529,0.00025772478,0.0015169822,0.93235713,0.009237947,0.008423412,0.006173709,0.00003998744],"about_ca_topic_score_codex":0.0060069547,"about_ca_topic_score_gemma":0.016642597,"teacher_disagreement_score":0.0060069547,"about_ca_system_score_codex":0.00065259665,"about_ca_system_score_gemma":0.00051766535,"threshold_uncertainty_score":0.011943996},"labels":[],"label_agreement":null},{"id":"W4367159354","doi":"10.7202/1078136ar","title":"Intra-Group Diversity and How it is Managed by an Outlaw Motorcycle Club","year":2021,"lang":"en","type":"article","venue":"Culture","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Club; Diversity (politics); Subculture (biology); Repertoire; Theme (computing); SWORD; Asset (computer security); Camouflage; Psychology; Political science; Engineering; Computer security; Law; Computer science; Art; World Wide Web; Ecology; Literature; Biology","score_opus":0.01195057441722224,"score_gpt":0.207621013589887,"score_spread":0.19567043917266475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367159354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9836353,0.000114435716,0.0004206542,0.0009899884,0.000014612513,0.000009544803,0.0000060164057,0.000006532777,0.0148028275],"genre_scores_gemma":[0.99895203,0.00004651684,0.00010334532,0.000045739576,0.000005112577,0.0000035619453,0.000004679727,0.0000039539614,0.00083497196],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9951971,0.002871238,0.000093024784,0.00039677534,0.0005021506,0.0009396908],"domain_scores_gemma":[0.9930507,0.0017537965,0.001605694,0.0004786896,0.00072147296,0.002389587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031256522,0.00022612973,0.00023930735,0.0018579608,0.008435443,0.008652604,0.0011905619,0.00087460974,0.002509127],"category_scores_gemma":[0.0106123425,0.00024256241,0.00019427149,0.0010353931,0.008539803,0.0029188069,0.0064159264,0.0013727766,0.00026559696],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014551872,0.0001906726,0.26185334,0.000063317144,0.00006928601,0.0010272581,0.6657608,0.00049934816,0.0021356558,0.012011771,0.0022377844,0.05400526],"study_design_scores_gemma":[0.0000091313295,0.00015524172,0.23282996,0.00011580886,0.000021601669,0.00042697642,0.744738,0.0007702026,0.00040192017,0.0050411695,0.015441679,0.000048294554],"about_ca_topic_score_codex":0.024447044,"about_ca_topic_score_gemma":0.048022162,"teacher_disagreement_score":0.024447044,"about_ca_system_score_codex":0.0031639722,"about_ca_system_score_gemma":0.0029069202,"threshold_uncertainty_score":0.048609495},"labels":[],"label_agreement":null},{"id":"W4367294273","doi":"10.1007/978-3-662-65964-9","title":"Digitale Hate Speech","year":2023,"lang":"en","type":"book","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Atomic Energy of Canada Limited","keywords":"Computer science","score_opus":0.015556634665681295,"score_gpt":0.22089690807035098,"score_spread":0.20534027340466968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367294273","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018504327,0.029982917,0.008806273,0.0059434264,0.0077257035,0.000061374565,0.00083090394,0.00093456596,0.9438644],"genre_scores_gemma":[0.010095311,0.014712923,0.0022642473,0.0022749063,0.0013818914,0.000032291915,0.00038884586,0.0002513232,0.96859837],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99969554,0.000032354666,0.000013711176,0.000046174748,0.00019006341,0.000022132946],"domain_scores_gemma":[0.9993357,0.00033722396,0.00003540214,0.00006348505,0.00016168103,0.000066490524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000325519,0.00064259546,0.00026661513,0.0014201351,0.0010245597,0.004714741,0.00039695,0.001375133,0.07960589],"category_scores_gemma":[0.0017842463,0.00027097604,0.00019796248,0.0010563612,0.0012411169,0.0031182675,0.0011255506,0.0016966083,0.055678204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060502312,0.000022619146,0.00038023,0.00035367656,0.0000066712773,0.00020246315,0.0005803364,0.00013841104,0.0025704484,0.063417405,0.6108383,0.321429],"study_design_scores_gemma":[0.000002145906,0.000015824558,0.00053365243,0.0001217107,0.000003299195,0.00042921462,0.00020790924,0.00010694619,0.0009120815,0.00575568,0.9919057,0.0000058034807],"about_ca_topic_score_codex":0.0019642324,"about_ca_topic_score_gemma":0.0049368115,"teacher_disagreement_score":0.07960589,"about_ca_system_score_codex":0.0006106185,"about_ca_system_score_gemma":0.000682233,"threshold_uncertainty_score":0.26630825},"labels":[],"label_agreement":null},{"id":"W4372184070","doi":"10.1145/3572334.3572396","title":"Understanding Online Harassment and Safety Concerns of Marginalized LGBTQ+ Populations on Social Media in Bangladesh","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Affordance; Social media; Harassment; Internet privacy; SAFER; Identity (music); Presentation (obstetrics); Identity management; Public relations; Face (sociological concept); Online identity; Sociology; Psychology; Political science; Social psychology; Computer science; Computer security; World Wide Web; The Internet","score_opus":0.16800959535064253,"score_gpt":0.3108201995995645,"score_spread":0.14281060424892195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372184070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920231,0.00036613655,0.0001270152,0.0012737948,0.000008872931,0.000017688675,0.000049636423,0.0000021477101,0.006131551],"genre_scores_gemma":[0.9975592,0.0008058949,0.00006250535,0.00035056358,0.000004817377,0.000029747476,0.000025326948,0.0000030006747,0.001158848],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9988919,0.00060061435,0.000058233953,0.00007958028,0.00011932979,0.00025041582],"domain_scores_gemma":[0.9981279,0.0007042413,0.00045461737,0.000080358375,0.00026942458,0.0003635366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001512661,0.00033619857,0.0003566531,0.0008696002,0.004298637,0.0030901136,0.0004994934,0.00081121246,0.0060823974],"category_scores_gemma":[0.0037638436,0.0002886802,0.0001661335,0.001048303,0.0027718882,0.0029251252,0.0034451135,0.0011653,0.00083196966],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000419609,0.000051412695,0.1056182,0.00021562583,0.000012493642,0.0014115582,0.87006736,0.00003092814,0.0012783752,0.0013036156,0.0009674939,0.01900101],"study_design_scores_gemma":[0.0000013405192,0.000032819902,0.023667196,0.00013094852,0.0000054941324,0.00029243217,0.9717414,0.000022982344,0.000072632574,0.00026099678,0.0037643714,0.0000074163395],"about_ca_topic_score_codex":0.03336069,"about_ca_topic_score_gemma":0.04483942,"teacher_disagreement_score":0.03336069,"about_ca_system_score_codex":0.0017578254,"about_ca_system_score_gemma":0.0018775996,"threshold_uncertainty_score":0.066332996},"labels":[],"label_agreement":null},{"id":"W4376865436","doi":"10.5430/wjel.v13n5p544","title":"Sociopragmatic Analysis of Filipino Celebrities’ Posts and Fans’ Comments","year":2023,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sarcasm; Persona; Social media; Politeness; Context (archaeology); Sociology; Media studies; Dimension (graph theory); Advertising; Political science; Irony; Computer science; History; Art; Humanities; Literature; World Wide Web; Law","score_opus":0.007410156351179603,"score_gpt":0.24368577064750402,"score_spread":0.2362756142963244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376865436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99571854,0.000035103058,0.0002731349,0.0000629265,0.000009838959,0.000026485783,0.00016354256,0.00001751106,0.003692868],"genre_scores_gemma":[0.99795485,0.000047765036,0.00035765374,0.000018448705,0.000021094234,0.000052830805,0.00016458638,0.000008493534,0.0013743106],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99937433,0.000249781,0.000048810503,0.00009177684,0.00013733089,0.00009790934],"domain_scores_gemma":[0.9939328,0.0036612332,0.0010889124,0.00021047433,0.0007722185,0.0003344264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009867069,0.00020675556,0.00016485153,0.0037289008,0.0013471838,0.0010261373,0.00017855625,0.00028330312,0.0029277897],"category_scores_gemma":[0.0059911194,0.00009638095,0.000114177026,0.0017051233,0.00068770826,0.0007185158,0.00088767096,0.0003906355,0.00043568935],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046417813,0.00027670973,0.46496415,0.00037177582,0.000034818986,0.0015200138,0.4335754,0.00011339252,0.018858243,0.0018091885,0.0018845105,0.07612766],"study_design_scores_gemma":[0.0000038946596,0.00013349098,0.82953507,0.000056492703,0.000017272798,0.00039973744,0.1601074,0.00091410417,0.0021466122,0.00022018133,0.006440054,0.000025654252],"about_ca_topic_score_codex":0.0027068085,"about_ca_topic_score_gemma":0.005819648,"teacher_disagreement_score":0.0037289008,"about_ca_system_score_codex":0.0005164283,"about_ca_system_score_gemma":0.00024286694,"threshold_uncertainty_score":0.009794414},"labels":[],"label_agreement":null},{"id":"W4377004125","doi":"10.1386/jacm_00108_1","title":"Far-right virtual communities: Exploring users and uses of far-right pages on social media","year":2023,"lang":"en","type":"article","venue":"Journal of Alternative & Community Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Mainstream; Social media; Far right; Media studies; Sociology; Immigration; Opposition (politics); Focus (optics); Political science; Politics; Law","score_opus":0.13479635632085876,"score_gpt":0.2996800906018006,"score_spread":0.16488373428094186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377004125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99369484,0.00015121055,0.0004584109,0.00022477061,0.0000091553875,0.000026796499,0.000032011532,0.000009358014,0.005393502],"genre_scores_gemma":[0.99850756,0.0001170756,0.00038024964,0.00007200481,0.0000055275677,0.00002304041,0.000022638293,0.0000073938963,0.000864524],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99766624,0.0015925411,0.00006335724,0.00012837679,0.0002920933,0.0002573816],"domain_scores_gemma":[0.9924285,0.0050367117,0.00085558096,0.00036315678,0.00046840697,0.0008476736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026868938,0.00031834596,0.0002611196,0.002103319,0.0047882227,0.004324292,0.0004814418,0.00088261603,0.0023914475],"category_scores_gemma":[0.007621931,0.0003147361,0.00019659742,0.00096067047,0.0032455425,0.006195972,0.004835067,0.0008519194,0.00026748536],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000647547,0.000057840676,0.036283232,0.000086158616,0.000011562974,0.00043820994,0.9471874,0.00003735768,0.00081636646,0.0018898407,0.0004905919,0.012636704],"study_design_scores_gemma":[0.0000064217766,0.00005687559,0.019193798,0.00010630645,0.000009894869,0.00029362005,0.96821946,0.00035309285,0.0003004134,0.0008982524,0.010534989,0.000026835756],"about_ca_topic_score_codex":0.012571872,"about_ca_topic_score_gemma":0.030763995,"teacher_disagreement_score":0.012571872,"about_ca_system_score_codex":0.0011888519,"about_ca_system_score_gemma":0.0010462247,"threshold_uncertainty_score":0.024997413},"labels":[],"label_agreement":null},{"id":"W4378070545","doi":"10.31542/cb.v5i1.2524","title":"Masked Reactions","year":2023,"lang":"en","type":"article","venue":"Crossing Borders Student Reflections on Global Social Issues","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Government (linguistics); Sample (material); Psychology; Pandemic; Coronavirus disease 2019 (COVID-19); Qualitative research; Sociology; Social psychology; Social science; Medicine; Philosophy; Linguistics; Chemistry","score_opus":0.041170082459049746,"score_gpt":0.42860912365298764,"score_spread":0.3874390411939379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378070545","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28872514,0.0019006702,0.018025706,0.032236997,0.011036393,0.0026164541,0.0028346302,0.0010798248,0.6415442],"genre_scores_gemma":[0.639766,0.00085451646,0.0042681983,0.017768232,0.0007407873,0.0010740329,0.0006634554,0.00045296052,0.33441183],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9898115,0.0043962924,0.00059331814,0.0012382233,0.002795749,0.0011649781],"domain_scores_gemma":[0.9863138,0.005679885,0.0013613432,0.002278385,0.0034313106,0.00093530293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064986176,0.0006227025,0.00030537337,0.0007069467,0.0038264312,0.0024981948,0.0008144435,0.0024829346,0.07648643],"category_scores_gemma":[0.028161194,0.0002952289,0.00048153655,0.00047651384,0.001631406,0.0021763875,0.0048731603,0.0027682593,0.023798903],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024893624,0.00042644687,0.026841545,0.0018587359,0.00007208714,0.0057910555,0.26808614,0.00032514692,0.049238842,0.0932446,0.27563635,0.2759897],"study_design_scores_gemma":[0.000040704243,0.00027313427,0.008148542,0.0003847928,0.000026781572,0.001917638,0.071668275,0.00035651328,0.009118662,0.006052896,0.9019272,0.00008490275],"about_ca_topic_score_codex":0.0028974034,"about_ca_topic_score_gemma":0.0023894827,"teacher_disagreement_score":0.07648643,"about_ca_system_score_codex":0.0019421921,"about_ca_system_score_gemma":0.0015213187,"threshold_uncertainty_score":0.2558726},"labels":[],"label_agreement":null},{"id":"W4378376001","doi":"10.1080/20403313.2023.2214485","title":"Teamwork through time: collective intentions in the voting process","year":2023,"lang":"en","type":"article","venue":"Jurisprudence","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Ratification; Voting; Democracy; Political science; Corporation; Teamwork; Group (periodic table); Disapproval voting; Social psychology; Law and economics; Business; Public relations; Economics; Psychology; Law; Politics","score_opus":0.017422085101213,"score_gpt":0.28796975587018186,"score_spread":0.27054767076896885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378376001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46636844,0.0012187231,0.20342082,0.014658185,0.0006452246,0.00023811031,0.00011956999,0.00015893122,0.31317207],"genre_scores_gemma":[0.9840306,0.00014399801,0.008574942,0.0003126294,0.000073262,0.000085857064,0.000037537375,0.000042943826,0.006698107],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9892083,0.0072672144,0.00030873937,0.00121499,0.0011525614,0.0008481508],"domain_scores_gemma":[0.97390616,0.015399778,0.0041590943,0.0022256924,0.002096508,0.0022127314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011262042,0.0003785121,0.0003350325,0.0015412904,0.0037354147,0.007990753,0.0010567493,0.002470875,0.009166235],"category_scores_gemma":[0.038699016,0.00041025146,0.00075693004,0.00096078165,0.009596406,0.007577298,0.004892312,0.002890653,0.0010870467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034752052,0.00021965776,0.035811763,0.00012444073,0.00006536836,0.00065884227,0.10961409,0.0027514154,0.0017227173,0.78323716,0.0029626833,0.06248432],"study_design_scores_gemma":[0.000112519614,0.00033708982,0.041831635,0.00038101306,0.00010325036,0.00045958138,0.078597724,0.02091663,0.0017715162,0.78729486,0.06801983,0.00017433347],"about_ca_topic_score_codex":0.0032898353,"about_ca_topic_score_gemma":0.0024382006,"teacher_disagreement_score":0.011262042,"about_ca_system_score_codex":0.0015625517,"about_ca_system_score_gemma":0.002141077,"threshold_uncertainty_score":0.05956012},"labels":[],"label_agreement":null},{"id":"W4378761496","doi":"10.1007/978-3-031-34111-3_37","title":"Multi-feature Transformer for Multiclass Cyberbullying Detection in Bangla","year":2023,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Bengali; Computer science; Artificial intelligence; Social media; Word embedding; Feature (linguistics); Embedding; Machine learning; Natural language processing; Speech recognition; World Wide Web; Linguistics","score_opus":0.012074516531077106,"score_gpt":0.2542587069343211,"score_spread":0.24218419040324402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378761496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53902054,0.0023606385,0.42297676,0.00050103874,0.00046181362,0.00015430526,0.0032681897,0.0055850344,0.02567169],"genre_scores_gemma":[0.88566744,0.0006592,0.087244496,0.00012190533,0.00006223519,0.00005332063,0.0027307973,0.00019552426,0.023265114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974114,0.000038524777,0.000017383498,0.000070513306,0.0000696032,0.00006279442],"domain_scores_gemma":[0.9997564,0.00007109204,0.000016331516,0.000035785764,0.00010053935,0.000019793784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032376288,0.0005105605,0.00041513113,0.00089158665,0.00038428366,0.0006256805,0.00039368795,0.0004012507,0.004751148],"category_scores_gemma":[0.0004319808,0.00017555564,0.00051768805,0.0008395051,0.00016449165,0.0005313129,0.00037004548,0.00037952003,0.003114049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063643605,0.00018204314,0.00695488,0.00014775971,0.00006978116,0.00042456816,0.00015605435,0.006152191,0.12000696,0.0012482532,0.00828551,0.85573554],"study_design_scores_gemma":[0.00005395369,0.0005966608,0.082263954,0.00007034445,0.00031982528,0.0032199437,0.00085166615,0.61462533,0.2616095,0.0031372395,0.033114888,0.00013672713],"about_ca_topic_score_codex":0.004981648,"about_ca_topic_score_gemma":0.005420778,"teacher_disagreement_score":0.004981648,"about_ca_system_score_codex":0.00031029765,"about_ca_system_score_gemma":0.00032954614,"threshold_uncertainty_score":0.015894175},"labels":[],"label_agreement":null},{"id":"W4379364190","doi":"10.1007/s10610-023-09548-8","title":"The Criminal Selfie: Conveying Grievance While Recording and Live Streaming Antisocial Behavior","year":2023,"lang":"en","type":"article","venue":"European Journal on Criminal Policy and Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Selfie; Criminology; Psychology; Social media; Denunciation; Scrutiny; Punishment (psychology); Politics; Social psychology; Internet privacy; Political science; Law","score_opus":0.10771599460571836,"score_gpt":0.3725235046865658,"score_spread":0.26480751008084746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379364190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96251047,0.00030558044,0.009148645,0.0014945195,0.00014026162,0.00018965255,0.00027543385,0.0006610333,0.025274435],"genre_scores_gemma":[0.9833174,0.00027368366,0.010095393,0.00047093,0.00009175047,0.00010437054,0.0001653034,0.00009696078,0.0053841197],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99936014,0.00036749392,0.00002486145,0.00006413751,0.00012340189,0.000059869624],"domain_scores_gemma":[0.9963762,0.002171771,0.0005602806,0.00032447654,0.00027204162,0.00029531302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008547991,0.00032221648,0.00013509888,0.0005409061,0.0008673762,0.0016588263,0.00035758235,0.0008500613,0.00507815],"category_scores_gemma":[0.00905155,0.0001379153,0.00014222991,0.00022608574,0.00091872737,0.0016427031,0.0016008851,0.00057179946,0.00068559294],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023258748,0.0015206935,0.12726909,0.0017580455,0.00014219337,0.0049199197,0.23977964,0.0018527004,0.1399376,0.018014818,0.02825527,0.4342241],"study_design_scores_gemma":[0.00026378085,0.0041236873,0.40520993,0.0024130028,0.00041649412,0.011257499,0.17726214,0.048829373,0.07267421,0.025337648,0.2515564,0.00065590686],"about_ca_topic_score_codex":0.0010665079,"about_ca_topic_score_gemma":0.002185304,"teacher_disagreement_score":0.00507815,"about_ca_system_score_codex":0.00028547575,"about_ca_system_score_gemma":0.00024636366,"threshold_uncertainty_score":0.016988039},"labels":[],"label_agreement":null},{"id":"W4379654079","doi":"10.2196/preprints.49416","title":"Twitter Misinformation Discourses About Vaping: Systematic Content Analysis (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Misinformation; Narrative; Social media; Blame; Electronic cigarette; Internet privacy; Distrust; Public relations; Political science; Crowdsourcing; Advertising; Psychology; Computer science; Social psychology; Medicine; Business; Law","score_opus":0.07347241845097979,"score_gpt":0.290146879106513,"score_spread":0.2166744606555332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379654079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9547235,0.0031243253,0.005387056,0.0074010636,0.00031251073,0.0030967644,0.013142015,0.00011312174,0.012699693],"genre_scores_gemma":[0.96255225,0.0034748076,0.01275872,0.002355722,0.0002639928,0.00843115,0.0056821727,0.00014556559,0.004335557],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99322176,0.0041156216,0.0008537322,0.00048282955,0.0010253475,0.0003008032],"domain_scores_gemma":[0.9507468,0.038024183,0.004275847,0.0015722029,0.005048289,0.0003326765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009629573,0.0002740648,0.00036379587,0.00515341,0.0018636307,0.0028428407,0.00043211915,0.0007419502,0.0036684389],"category_scores_gemma":[0.04693892,0.00030178452,0.00046035834,0.006628621,0.0018193108,0.0036042656,0.0025166532,0.00068620057,0.0007108891],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002899125,0.00017116751,0.09712886,0.008103916,0.00013771912,0.00058498624,0.7466853,0.00023330409,0.0038679822,0.009117214,0.030245813,0.103433765],"study_design_scores_gemma":[0.000063369625,0.00023850123,0.16528182,0.00817769,0.00025358004,0.00023013905,0.6782186,0.0015722195,0.0042234333,0.0037064592,0.13793755,0.000096638745],"about_ca_topic_score_codex":0.0061896984,"about_ca_topic_score_gemma":0.011812308,"teacher_disagreement_score":0.009629573,"about_ca_system_score_codex":0.0026216677,"about_ca_system_score_gemma":0.0041939192,"threshold_uncertainty_score":0.050926685},"labels":[],"label_agreement":null},{"id":"W4380047907","doi":"10.1177/20563051231177915","title":"Hate Influencers’ Mediation of Hate on Telegram: “We Declare War Against the Anti-White System”","year":2023,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of the Fraser Valley","funders":"","keywords":"Influencer marketing; Mediation; White (mutation); Sociology; Political science; Media studies; Law; Business","score_opus":0.017765975632368453,"score_gpt":0.2382946724198825,"score_spread":0.22052869678751405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380047907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.930004,0.000227567,0.009331534,0.0011668538,0.00006410521,0.00006381036,0.00015552578,0.00007473731,0.058911927],"genre_scores_gemma":[0.9951002,0.000092796516,0.0009247186,0.00008298679,0.000015200887,0.000023444785,0.00003819054,0.000028326802,0.00369417],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9988005,0.00074840616,0.000029482106,0.00010540464,0.00015758279,0.00015858172],"domain_scores_gemma":[0.99547887,0.0032631587,0.0005139019,0.00030339992,0.00027685935,0.00016375238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014131288,0.00039249705,0.0001487142,0.0014919491,0.0033948615,0.0041169887,0.0003620388,0.0008311452,0.0055594095],"category_scores_gemma":[0.0066340296,0.00018323673,0.00016896518,0.0009023989,0.005596038,0.004337313,0.0027881928,0.0011228737,0.00042070472],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014486352,0.000061105355,0.030306866,0.00020641279,0.000022169275,0.00079919375,0.86934835,0.00021835972,0.0068496135,0.051617082,0.0027374006,0.037688605],"study_design_scores_gemma":[0.00001372638,0.00010157503,0.057623364,0.000366945,0.000053867996,0.0005278674,0.83471006,0.0022116874,0.008504445,0.008016964,0.087811045,0.000058449183],"about_ca_topic_score_codex":0.006856342,"about_ca_topic_score_gemma":0.009334568,"teacher_disagreement_score":0.006856342,"about_ca_system_score_codex":0.0014853956,"about_ca_system_score_gemma":0.0007328169,"threshold_uncertainty_score":0.01859808},"labels":[],"label_agreement":null},{"id":"W4380301693","doi":"10.1609/icwsm.v17i1.22130","title":"Partisan US News Media Representations of Syrian Refugees","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Refugee; Syrian refugees; Offensive; Political science; Psychology; Media studies; Sociology; Law; Engineering; Operations research","score_opus":0.031192402024069314,"score_gpt":0.2766308534408231,"score_spread":0.2454384514167538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380301693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906804,0.0002934345,0.00016096007,0.00036061511,0.000030309797,0.000016178765,0.0014967808,0.000013908315,0.006947394],"genre_scores_gemma":[0.9959747,0.00040870212,0.00024310639,0.00009849044,0.00007671834,0.00002295292,0.0016358899,0.000018799416,0.0015206399],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911267,0.0003119011,0.00013002307,0.00011797575,0.00017447279,0.00015285246],"domain_scores_gemma":[0.9891754,0.003998034,0.0041379924,0.0007375577,0.0015236316,0.000427352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020185162,0.00032277143,0.00021553042,0.0062723802,0.0012059693,0.0031082607,0.00016428866,0.00035460686,0.0045789145],"category_scores_gemma":[0.009230012,0.00014469132,0.00021790189,0.0041548586,0.00081131025,0.0022684885,0.0020543428,0.000440768,0.0008371247],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042264495,0.00007886579,0.8663169,0.00038669718,0.00015357464,0.000628937,0.0793635,0.00021043197,0.004941126,0.0013134389,0.0051188716,0.041065063],"study_design_scores_gemma":[0.000005745465,0.00006569733,0.897632,0.00014183762,0.00006577017,0.0002593223,0.08310709,0.00057459425,0.0014801727,0.0002626633,0.016371125,0.000034035904],"about_ca_topic_score_codex":0.008331676,"about_ca_topic_score_gemma":0.015561908,"teacher_disagreement_score":0.008331676,"about_ca_system_score_codex":0.00055923394,"about_ca_system_score_gemma":0.0002853903,"threshold_uncertainty_score":0.016566396},"labels":[],"label_agreement":null},{"id":"W4380302097","doi":"10.1609/icwsm.v17i1.22143","title":"Understanding and Detecting Hateful Content Using Contrastive Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Antisemitism; Islamophobia; Artificial intelligence; Linguistics; Computer science; Political science; Judaism; Law; Theology; Politics; Philosophy","score_opus":0.17838639870361164,"score_gpt":0.26882438507290696,"score_spread":0.09043798636929531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380302097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78652143,0.0032277785,0.18096155,0.000569956,0.0003441358,0.00046459932,0.0051448755,0.0052971216,0.017468598],"genre_scores_gemma":[0.9066215,0.0007066422,0.080383815,0.0002547279,0.00016898161,0.00013509662,0.0052476726,0.00015682247,0.006324712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996518,0.00005757864,0.000016069878,0.0001139869,0.00009242744,0.00006804009],"domain_scores_gemma":[0.9987949,0.0005657149,0.00013269056,0.00015483465,0.00027700939,0.00007482524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004959151,0.0009382644,0.00049124047,0.0021989713,0.00036199187,0.0010409848,0.0006273982,0.00069714093,0.0016500219],"category_scores_gemma":[0.002383559,0.00019024003,0.0005491213,0.0008649803,0.0003985319,0.0016619855,0.00091231946,0.0010548371,0.0013240788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011396069,0.0010139334,0.06622962,0.00076008367,0.00026093613,0.0010943387,0.0013466708,0.018218728,0.119336314,0.0018908858,0.019517696,0.76919115],"study_design_scores_gemma":[0.000050541745,0.00083159446,0.09293775,0.00016017024,0.0001863528,0.0010765101,0.001964723,0.8062067,0.07114022,0.003829989,0.02152291,0.0000926297],"about_ca_topic_score_codex":0.004672204,"about_ca_topic_score_gemma":0.008491207,"teacher_disagreement_score":0.004672204,"about_ca_system_score_codex":0.0005895762,"about_ca_system_score_gemma":0.0003162828,"threshold_uncertainty_score":0.00928998},"labels":[],"label_agreement":null},{"id":"W4380321903","doi":"10.1145/3593013.3594088","title":"(Anti)-Intentional Harms: The Conceptual Pitfalls of Emotion AI in Education","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Popularity; Harm; Context (archaeology); Interpretability; Psychology; Cognitive science; Cognition; Affective science; Cognitive psychology; Emotion work; Social psychology; Computer science; Artificial intelligence","score_opus":0.014253900218615563,"score_gpt":0.25987004940799163,"score_spread":0.24561614918937608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380321903","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053324834,0.0107112,0.23039669,0.30048305,0.0017749255,0.00018622821,0.000110880486,0.0002635983,0.40274855],"genre_scores_gemma":[0.9491196,0.0022508805,0.023995753,0.015453228,0.00089495594,0.0004390509,0.000033984008,0.00012812202,0.0076843575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9794095,0.013020415,0.0008460241,0.001996199,0.0037782288,0.0009496568],"domain_scores_gemma":[0.9761027,0.014213061,0.0025286658,0.0037369994,0.0026670517,0.00075153506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017294923,0.0009124476,0.00075580925,0.0032324214,0.005812747,0.014749284,0.0030261127,0.011211247,0.0036432096],"category_scores_gemma":[0.02282631,0.0006387883,0.0008711785,0.0017327532,0.108590305,0.02323633,0.009424472,0.013672105,0.00079626736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003446625,0.0000046572177,0.00008289298,0.000014126311,0.00000159765,0.000014181668,0.0025759474,0.000080235885,0.000028001163,0.99553114,0.0002357333,0.0014280988],"study_design_scores_gemma":[0.000010179052,0.000011681064,0.00019938448,0.00009766193,0.000006421495,0.00007188343,0.0024337375,0.00087722775,0.0001404312,0.9780101,0.018127656,0.000013545542],"about_ca_topic_score_codex":0.0029284176,"about_ca_topic_score_gemma":0.0015291622,"teacher_disagreement_score":0.017294923,"about_ca_system_score_codex":0.006733824,"about_ca_system_score_gemma":0.0027990805,"threshold_uncertainty_score":0.09146541},"labels":[],"label_agreement":null},{"id":"W4381572438","doi":"10.46991/afa/2023.19.1.057","title":"GENDER DIFFERENCES IN VERBAL AND NONVERBAL AGGRESSION","year":2023,"lang":"en","type":"article","venue":"Armenian Folia Anglistika","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aggression; Psychology; Masculinity; Femininity; Nonverbal communication; Social psychology; Context (archaeology); Politics; Personality; Set (abstract data type); Verbal aggression; Developmental psychology; Poison control; Human factors and ergonomics; Political science","score_opus":0.023607525482223942,"score_gpt":0.23838904860934715,"score_spread":0.2147815231271232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381572438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95943975,0.0014283677,0.0015888152,0.00021500747,0.00008866412,0.000037612917,0.00029940213,0.000013025134,0.03688942],"genre_scores_gemma":[0.995615,0.0003299195,0.00033041416,0.00004295796,0.000024440602,0.000021367074,0.000098443415,0.00000975381,0.0035277419],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985384,0.00046544962,0.00011218561,0.00019036228,0.0004926803,0.00020095606],"domain_scores_gemma":[0.99702555,0.0017075762,0.0006105913,0.00014521914,0.00033794873,0.00017320094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011791734,0.00019502334,0.0001865768,0.0009951252,0.00041835516,0.0012018941,0.00017950077,0.00026515292,0.0062623015],"category_scores_gemma":[0.0072721043,0.000096073454,0.00017985179,0.00045766216,0.0007305573,0.0005593496,0.0005650411,0.00026373574,0.0008519735],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081511337,0.00029767762,0.7469124,0.00030036017,0.00016995518,0.0012586686,0.06262314,0.00023587122,0.015074336,0.016144942,0.0020705077,0.15409696],"study_design_scores_gemma":[0.0000061851065,0.000298409,0.9721029,0.000112391725,0.00003343605,0.0014321174,0.01447414,0.0002693298,0.001256972,0.002615351,0.007372098,0.00002675601],"about_ca_topic_score_codex":0.0021037613,"about_ca_topic_score_gemma":0.0027061442,"teacher_disagreement_score":0.0062623015,"about_ca_system_score_codex":0.00030064388,"about_ca_system_score_gemma":0.00024411269,"threshold_uncertainty_score":0.020949483},"labels":[],"label_agreement":null},{"id":"W4381715355","doi":"10.1111/cars.12447","title":"Using YouTube vlogs to study women's experiences of participating in #MeToo","year":2023,"lang":"en","type":"article","venue":"Canadian Review of Sociology/Revue canadienne de sociologie","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Sociology; Humanities; Art","score_opus":0.09903056437021528,"score_gpt":0.32494344169522726,"score_spread":0.22591287732501197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381715355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96365553,0.00084846286,0.0019393209,0.0020600588,0.00015466959,0.00042250636,0.0022367623,0.000038163515,0.02864456],"genre_scores_gemma":[0.98059857,0.0011803692,0.0019849034,0.0007033185,0.00008311309,0.000986845,0.0009937506,0.00005282732,0.013416287],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9987356,0.0007598417,0.000060389997,0.00011673383,0.00015290017,0.00017461382],"domain_scores_gemma":[0.99638736,0.0021665765,0.0004935605,0.00017950214,0.00045909226,0.0003139201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017046388,0.00033233868,0.00033493733,0.0020887794,0.0034329188,0.0020139664,0.0006505294,0.0006482855,0.003899489],"category_scores_gemma":[0.006377319,0.00019429966,0.0001284638,0.0029494758,0.0017910926,0.00292622,0.0023375212,0.00065878377,0.00069449114],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013851331,0.00009104245,0.04823137,0.0006345132,0.000017452481,0.001677248,0.8831935,0.00009012563,0.0023107973,0.0038460847,0.009599572,0.05016979],"study_design_scores_gemma":[0.000011653465,0.00010149779,0.03574952,0.00048490183,0.00001169148,0.00031887344,0.8974799,0.00023678612,0.00069985265,0.00085683906,0.06401258,0.000035949226],"about_ca_topic_score_codex":0.038880948,"about_ca_topic_score_gemma":0.12013197,"teacher_disagreement_score":0.038880948,"about_ca_system_score_codex":0.0018822002,"about_ca_system_score_gemma":0.0013648344,"threshold_uncertainty_score":0.07730925},"labels":[],"label_agreement":null},{"id":"W4384133227","doi":"10.1002/jcpy.1377","title":"<scp>PassivePy</scp>: A tool to automatically identify passive voice in big text data","year":2023,"lang":"en","type":"article","venue":"Journal of Consumer Psychology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Credibility; Attribution; Computer science; Relevance (law); Deception; Voice analysis; Psychology; Python (programming language); Passive voice; Data science; Applied psychology; Social psychology; Speech recognition; Linguistics","score_opus":0.04559678330392432,"score_gpt":0.35054895507893064,"score_spread":0.30495217177500633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384133227","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017137969,0.0002611395,0.13197987,0.0011581251,0.00043427912,0.0015532153,0.17925636,0.6638372,0.004381778],"genre_scores_gemma":[0.15359065,0.0004678168,0.46683908,0.0017320783,0.0005849554,0.009225211,0.26911137,0.08380858,0.014640328],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957569,0.0011301553,0.00056482904,0.0011407717,0.0011823382,0.00022505583],"domain_scores_gemma":[0.9679363,0.019569669,0.003643036,0.0044244695,0.0034668862,0.0009596637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006692471,0.0028131532,0.0009797409,0.006440271,0.0010386212,0.0027575972,0.0014348058,0.0012038987,0.034447838],"category_scores_gemma":[0.042419154,0.0009391878,0.0011753337,0.0036238215,0.0010187377,0.004126511,0.0043748054,0.0018879493,0.027444983],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012313769,0.0003782952,0.016459575,0.003617696,0.00039632915,0.0010549363,0.0031358916,0.002336951,0.014767961,0.0034136055,0.69256014,0.26064727],"study_design_scores_gemma":[0.001119519,0.00074508647,0.11298504,0.0016100453,0.00023878664,0.0015496418,0.0023969482,0.28266945,0.07991975,0.043758113,0.47200134,0.0010062471],"about_ca_topic_score_codex":0.0025009066,"about_ca_topic_score_gemma":0.004571349,"teacher_disagreement_score":0.034447838,"about_ca_system_score_codex":0.00076361495,"about_ca_system_score_gemma":0.0021842301,"threshold_uncertainty_score":0.11523956},"labels":[],"label_agreement":null},{"id":"W4384346102","doi":"10.1007/978-3-031-37249-0_8","title":"Addressing Biases in the Texts Using an End-to-End Pipeline Approach","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Pipeline (software); Popularity; Computer science; Social media; Set (abstract data type); Word (group theory); Natural language processing; Artificial intelligence; Information retrieval; Data science; World Wide Web; Linguistics; Psychology; Social psychology","score_opus":0.20470570782572706,"score_gpt":0.35259485108072064,"score_spread":0.14788914325499358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384346102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029085359,0.0004880759,0.94004005,0.0014987773,0.0003200926,0.00068337296,0.0023913693,0.0139493,0.011543644],"genre_scores_gemma":[0.19065951,0.00039463333,0.7807875,0.0004900927,0.0003765086,0.000431513,0.00513484,0.0018606479,0.019864583],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9940446,0.0015536604,0.0003764081,0.0015536544,0.0019975875,0.00047423659],"domain_scores_gemma":[0.97504145,0.012427384,0.0012062276,0.003298637,0.0074996874,0.0005266413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065987427,0.0020050206,0.0019182821,0.003760203,0.0018294993,0.0066295434,0.0031438745,0.002982395,0.019335996],"category_scores_gemma":[0.028014844,0.001125534,0.0013556851,0.003966614,0.0012809249,0.008928945,0.0056881504,0.00426898,0.01871702],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071048806,0.00050443027,0.0066821147,0.0004902417,0.00012440287,0.00027521345,0.0012214335,0.0042181653,0.044666525,0.010910505,0.014777964,0.9154185],"study_design_scores_gemma":[0.0001387892,0.0009315854,0.017411035,0.00024533743,0.00047834852,0.0009394418,0.0023705729,0.652074,0.1486974,0.10406629,0.0724175,0.0002297376],"about_ca_topic_score_codex":0.0044930573,"about_ca_topic_score_gemma":0.0069553824,"teacher_disagreement_score":0.019335996,"about_ca_system_score_codex":0.0013350034,"about_ca_system_score_gemma":0.00340205,"threshold_uncertainty_score":0.064685285},"labels":[],"label_agreement":null},{"id":"W4385078339","doi":"10.18280/isi.280302","title":"Advancements in Semantic Expansion Techniques for Short Text Classification and Hate Speech Detection","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Voice activity detection; Artificial intelligence; Speech recognition; Speech processing","score_opus":0.020399857584255384,"score_gpt":0.25954873438133674,"score_spread":0.23914887679708136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08224621,0.0039929254,0.8954777,0.00088100904,0.00034877346,0.00039368973,0.0013110494,0.004328209,0.011020352],"genre_scores_gemma":[0.5319041,0.0029252295,0.45005924,0.00037598322,0.0007147738,0.00041436282,0.004413518,0.00029124413,0.008901554],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983962,0.0004509732,0.0001229079,0.00040812147,0.0005265572,0.00009523],"domain_scores_gemma":[0.99705434,0.0011708023,0.00036136416,0.00042321993,0.00088634726,0.00010400779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012726979,0.001192447,0.0008259494,0.0050476743,0.0006790439,0.0010360285,0.00082289544,0.0007964468,0.0027289805],"category_scores_gemma":[0.0037180698,0.00021626448,0.0008782265,0.002588697,0.0007919661,0.0036844327,0.0010290513,0.0014462011,0.002223374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018978864,0.00024518117,0.00492768,0.0003144045,0.00007007737,0.00014096203,0.0005153164,0.0061735464,0.03935206,0.0072283493,0.0055739097,0.93526864],"study_design_scores_gemma":[0.000038037353,0.0005348274,0.032452352,0.00030571115,0.00019883086,0.0011266565,0.0014263329,0.8168584,0.05344746,0.034557655,0.05885237,0.00020145108],"about_ca_topic_score_codex":0.0028147735,"about_ca_topic_score_gemma":0.0038543215,"teacher_disagreement_score":0.0050476743,"about_ca_system_score_codex":0.0006039439,"about_ca_system_score_gemma":0.00091698486,"threshold_uncertainty_score":0.009129286},"labels":[],"label_agreement":null},{"id":"W4385412360","doi":"10.1109/eurospw59978.2023.00072","title":"Work in Progress: A Glance at Social Media Self-Censorship in North America","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Censorship; Social media; Computer science; Internet privacy; Work (physics); Media studies; World Wide Web; Political science; Sociology; Engineering; Law","score_opus":0.016437428654273334,"score_gpt":0.2310214376969902,"score_spread":0.21458400904271688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385412360","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8381286,0.009968915,0.00043809426,0.06988905,0.0004212209,0.000054354245,0.0006129923,0.000041780782,0.08044494],"genre_scores_gemma":[0.9748054,0.0077366284,0.00029676553,0.007281839,0.0002065226,0.00003385617,0.00024966008,0.00003973179,0.009349691],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99798834,0.00074458675,0.000081860315,0.00021795694,0.00044265462,0.0005245337],"domain_scores_gemma":[0.9922097,0.002363894,0.0009620782,0.0003289463,0.0024239027,0.0017114594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003027848,0.00017533517,0.00029555813,0.002993966,0.00964053,0.0042487145,0.0008421672,0.0009861107,0.0047058035],"category_scores_gemma":[0.004573814,0.0001885438,0.00021479391,0.0044294796,0.004005293,0.0029852774,0.002189493,0.0021469255,0.0005140993],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008320402,0.00017742068,0.16970347,0.00036837117,0.000042210246,0.0015286197,0.7003623,0.000051101924,0.0013035671,0.006201987,0.036620207,0.083557546],"study_design_scores_gemma":[0.0000028653621,0.000029417011,0.26251101,0.0004287391,0.000014713618,0.0002472543,0.64810425,0.0000654045,0.0001931454,0.0004956325,0.0878814,0.000026196862],"about_ca_topic_score_codex":0.6272437,"about_ca_topic_score_gemma":0.81260914,"teacher_disagreement_score":0.6272437,"about_ca_system_score_codex":0.0075811683,"about_ca_system_score_gemma":0.009030264,"threshold_uncertainty_score":0.7499031},"labels":[],"label_agreement":null},{"id":"W4385552004","doi":"10.1017/s1351324923000396","title":"SSL-GAN-RoBERTa: A robust semi-supervised model for detecting Anti-Asian COVID-19 hate speech on social media","year":2023,"lang":"en","type":"article","venue":"Natural Language Engineering","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Social media; Transformer; Artificial intelligence; Task (project management); Coronavirus disease 2019 (COVID-19); Machine learning; Speech recognition; Data mining; Voltage","score_opus":0.025940205091409797,"score_gpt":0.26037499805348296,"score_spread":0.23443479296207317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385552004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12166406,0.0027926122,0.85606223,0.001752146,0.00040222373,0.0004246563,0.0020269367,0.009575964,0.0052991556],"genre_scores_gemma":[0.8490756,0.00053716084,0.13234217,0.001404941,0.00032666774,0.0005622672,0.0054491684,0.00037684359,0.009925169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875915,0.00050467654,0.000048994738,0.0004282052,0.00014273172,0.00011612288],"domain_scores_gemma":[0.9975339,0.0014135711,0.00020391727,0.00024639285,0.0004856906,0.00011648056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023119564,0.0017194476,0.001717782,0.0008338332,0.00045560702,0.00084623107,0.0031591903,0.0017447409,0.0014556132],"category_scores_gemma":[0.0041897795,0.0006216286,0.0013876349,0.000531763,0.0010290974,0.0013713561,0.0010852882,0.0027256373,0.0013088208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006537822,0.0006043523,0.005909202,0.0002950033,0.00025689695,0.00028010478,0.00022204801,0.7308505,0.007111074,0.0038897954,0.019588271,0.23033898],"study_design_scores_gemma":[0.000007908388,0.000027695016,0.0001835398,0.0000061547616,0.000007848043,0.000015670697,0.0000070232363,0.9980325,0.00051124254,0.00091537833,0.00027830616,0.000006784386],"about_ca_topic_score_codex":0.008012514,"about_ca_topic_score_gemma":0.011673475,"teacher_disagreement_score":0.008012514,"about_ca_system_score_codex":0.0010410631,"about_ca_system_score_gemma":0.0012642792,"threshold_uncertainty_score":0.015931726},"labels":[],"label_agreement":null},{"id":"W4385569789","doi":"10.18653/v1/2023.woah-1.9","title":"Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Alliance de recherche numérique du Canada; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Perspective (graphical); Generalization; Machine learning; Focus (optics); Domain (mathematical analysis); Artificial intelligence; Task (project management); Natural language processing","score_opus":0.007907210624499677,"score_gpt":0.2476048123054383,"score_spread":0.23969760168093862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385569789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25553977,0.0010734077,0.7279514,0.00053692353,0.0002213512,0.00029607466,0.00052571384,0.0070543685,0.0068009878],"genre_scores_gemma":[0.82560384,0.00013938542,0.1680226,0.00034650255,0.00008256942,0.00018087475,0.0010518117,0.00028145648,0.0042910357],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980672,0.0005428267,0.000088854955,0.00076743634,0.0003355926,0.00019798965],"domain_scores_gemma":[0.9963257,0.0015150545,0.0004618818,0.0008083913,0.0006782021,0.0002108115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024649138,0.0016245572,0.0010743897,0.0016599409,0.00066900445,0.0011146606,0.0019775857,0.00146063,0.0014790883],"category_scores_gemma":[0.006849488,0.00045070553,0.00096468034,0.0007531346,0.0010857038,0.0025247617,0.0034562768,0.0026410776,0.0016068675],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007692796,0.0011834984,0.023865765,0.00031719138,0.00039218034,0.0008182865,0.00081150595,0.119065166,0.061095223,0.0045888214,0.009486819,0.7776062],"study_design_scores_gemma":[0.00002115697,0.00024150053,0.0029694447,0.000022368515,0.00004420316,0.00029359694,0.00016946983,0.9713496,0.018298758,0.0045257965,0.0020268965,0.00003726346],"about_ca_topic_score_codex":0.0019297473,"about_ca_topic_score_gemma":0.003416443,"teacher_disagreement_score":0.0024649138,"about_ca_system_score_codex":0.0006302609,"about_ca_system_score_gemma":0.0008433789,"threshold_uncertainty_score":0.013035893},"labels":[],"label_agreement":null},{"id":"W4385569909","doi":"10.18653/v1/2023.woah-1.12","title":"Aporophobia: An Overlooked Type of Toxic Language Targeting the Poor","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Poverty; Focus (optics); Social media; Phenomenon; Computer science; Poverty reduction; Data science; Internet privacy; Political science; World Wide Web; Epistemology","score_opus":0.017105626615821545,"score_gpt":0.2630096165681824,"score_spread":0.24590398995236085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385569909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.918439,0.0009497486,0.019809188,0.005881367,0.00016019339,0.00013313138,0.0027648818,0.0002846747,0.05157796],"genre_scores_gemma":[0.9935207,0.000469213,0.0023135021,0.00070160796,0.00007722079,0.000053443797,0.00056374335,0.000046778834,0.0022538102],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833184,0.0007446332,0.00012072282,0.00027692664,0.0003371338,0.00018877661],"domain_scores_gemma":[0.98098606,0.010147788,0.0040306295,0.002390026,0.0017622175,0.0006832129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019673402,0.00031544443,0.00033284313,0.0015714989,0.0017373378,0.002336924,0.0005480249,0.00087973074,0.005449789],"category_scores_gemma":[0.016865624,0.00018721333,0.0002350995,0.0013492756,0.0020273584,0.003798201,0.0022966322,0.0010672397,0.0010332833],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044351682,0.00026290875,0.5869215,0.0021482743,0.00012314731,0.002261362,0.08733441,0.00095270795,0.008771887,0.03709722,0.024901312,0.24878173],"study_design_scores_gemma":[0.000026720963,0.0003099457,0.58403,0.0029151314,0.00022706475,0.006277138,0.14383474,0.011072524,0.014400711,0.058061633,0.17858791,0.0002565248],"about_ca_topic_score_codex":0.0045784214,"about_ca_topic_score_gemma":0.007020909,"teacher_disagreement_score":0.005449789,"about_ca_system_score_codex":0.0006636749,"about_ca_system_score_gemma":0.0007555881,"threshold_uncertainty_score":0.018231392},"labels":[],"label_agreement":null},{"id":"W4385569921","doi":"10.18653/v1/2023.woah-1.21","title":"Factoring Hate Speech: A New Annotation Framework to Study Hate Speech in Social Media","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; Hebrew University of Jerusalem","keywords":"Annotation; Computer science; Scheme (mathematics); Construct (python library); Social media; Natural language processing; Artificial intelligence; Voice activity detection; Speech recognition; Speech processing; World Wide Web; Mathematics","score_opus":0.07390689382459951,"score_gpt":0.32009733051542605,"score_spread":0.24619043669082652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385569921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11080206,0.0015761197,0.8556125,0.0020044975,0.00076182117,0.0009806178,0.009997424,0.003906771,0.0143582],"genre_scores_gemma":[0.45063782,0.0008740235,0.51935947,0.0005803235,0.001064784,0.0018969885,0.0142405415,0.00090776564,0.010438303],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9947789,0.001851907,0.0004290687,0.0016667013,0.0009500047,0.00032343372],"domain_scores_gemma":[0.97904676,0.009183172,0.0026596005,0.0033688769,0.004745561,0.0009959539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059224083,0.00157793,0.0009415568,0.008378646,0.0025034205,0.002577694,0.0014046658,0.0016788457,0.003139348],"category_scores_gemma":[0.015420286,0.0005611984,0.00085277914,0.004221856,0.002168088,0.0054270127,0.003886852,0.002754338,0.0023509425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013125576,0.0007357548,0.10008668,0.0017736214,0.00031156372,0.00060256216,0.017121248,0.0072504566,0.11914878,0.037997466,0.031242909,0.68241656],"study_design_scores_gemma":[0.00016353771,0.0009448283,0.23551299,0.00072561426,0.0004908986,0.0019475706,0.011257488,0.32009426,0.0712138,0.08868294,0.2680924,0.0008737534],"about_ca_topic_score_codex":0.0075584925,"about_ca_topic_score_gemma":0.011960094,"teacher_disagreement_score":0.008378646,"about_ca_system_score_codex":0.0012400274,"about_ca_system_score_gemma":0.0021766713,"threshold_uncertainty_score":0.03132105},"labels":[],"label_agreement":null},{"id":"W4385570093","doi":"10.18653/v1/2023.sicon-1.4","title":"What Makes a Good Counter-Stereotype? Evaluating Strategies for Automated Responses to Stereotypical Text","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Stereotype (UML); Correctness; Set (abstract data type); Tone (literature); Computer science; Stereotype threat; Social media; Social psychology; Psychology; Internet privacy; World Wide Web; Algorithm; Linguistics","score_opus":0.05505928854682075,"score_gpt":0.35711072794614634,"score_spread":0.3020514393993256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385570093","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.969044,0.00014399836,0.02502856,0.00011704717,0.00005428021,0.00065542373,0.00020106322,0.001968321,0.0027873246],"genre_scores_gemma":[0.9729864,0.00005927236,0.025510035,0.000095635965,0.000021529824,0.0002685072,0.00021264218,0.000087352935,0.00075854006],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99511886,0.003049569,0.00031102457,0.0006999999,0.0006214031,0.00019920632],"domain_scores_gemma":[0.9169949,0.071599595,0.0048861396,0.0023557988,0.00302761,0.0011360474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050757416,0.0011786445,0.00058301643,0.0012815655,0.00038144246,0.0014912374,0.00095921755,0.0011798415,0.002023246],"category_scores_gemma":[0.04431009,0.00030014096,0.0002620014,0.00025479525,0.0005226207,0.0011994552,0.0008089977,0.00056656834,0.00095006445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008743011,0.0047609126,0.19065587,0.0032567512,0.0005347259,0.000961226,0.011190039,0.013715097,0.15159008,0.0015700872,0.002991255,0.610031],"study_design_scores_gemma":[0.0009663225,0.012432055,0.26960668,0.00051443087,0.0010174707,0.0017620829,0.009144553,0.50870806,0.18522055,0.0044563888,0.005783232,0.000388104],"about_ca_topic_score_codex":0.0009985432,"about_ca_topic_score_gemma":0.0014103069,"teacher_disagreement_score":0.0050757416,"about_ca_system_score_codex":0.00042217135,"about_ca_system_score_gemma":0.00056705566,"threshold_uncertainty_score":0.026843369},"labels":[],"label_agreement":null},{"id":"W4385570854","doi":"10.18653/v1/2023.acl-long.370","title":"SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Korea Advanced Institute of Science and Technology; Institute for Basic Science; Seoul National University","keywords":"Square (algebra); Alice (programming language); Volume (thermodynamics); Scale (ratio); Computer science; Library science; Geography; Cartography; Mathematics; Physics; Programming language; Geometry","score_opus":0.017971828390566706,"score_gpt":0.30658503547694327,"score_spread":0.28861320708637656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385570854","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2931884,0.0029609606,0.019756958,0.00239067,0.0010872881,0.0021656097,0.64958173,0.016032714,0.012835608],"genre_scores_gemma":[0.13683671,0.00039081243,0.030165171,0.0008735028,0.00030915352,0.0022935395,0.8217787,0.0007653615,0.0065870318],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99249166,0.0039059261,0.00061109045,0.0011954795,0.0014485887,0.0003471689],"domain_scores_gemma":[0.97143644,0.014933945,0.001909372,0.0053610983,0.0044499864,0.0019091255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047076843,0.0016942282,0.0009820547,0.004125945,0.0013321679,0.0016412312,0.0019971936,0.0034761159,0.0062834616],"category_scores_gemma":[0.025571853,0.0004944321,0.0010114141,0.0027300618,0.00095332233,0.0021654952,0.005052792,0.0017159007,0.013659951],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033464518,0.002232116,0.06822654,0.0039946577,0.0006770835,0.0018120393,0.008231701,0.0024222436,0.017906085,0.0018302782,0.784099,0.10522184],"study_design_scores_gemma":[0.001355781,0.0017594565,0.3656229,0.00075357157,0.0003745108,0.0031686183,0.014223709,0.030964324,0.019390233,0.007571948,0.5540977,0.00071724603],"about_ca_topic_score_codex":0.0049570207,"about_ca_topic_score_gemma":0.013190899,"teacher_disagreement_score":0.0062834616,"about_ca_system_score_codex":0.00079146517,"about_ca_system_score_gemma":0.0012907984,"threshold_uncertainty_score":0.02489686},"labels":[],"label_agreement":null},{"id":"W4385571787","doi":"10.18653/v1/2023.findings-acl.152","title":"Contrastive Learning of Sociopragmatic Meaning in Social Media","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Atomic Energy of Canada Limited; University of Oxford","keywords":"Computer science; Sarcasm; Natural language processing; Artificial intelligence; Meaning (existential); Task (project management); Code (set theory); Domain (mathematical analysis); Representation (politics); Bridge (graph theory); Linguistics; Psychology","score_opus":0.017660802280077117,"score_gpt":0.24568869191509637,"score_spread":0.22802788963501924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385571787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29607475,0.0012518135,0.689739,0.0014607705,0.00022222403,0.00019281068,0.001255745,0.0031847109,0.0066183237],"genre_scores_gemma":[0.90990967,0.00018483985,0.0849339,0.00036911515,0.000111457855,0.00017460159,0.0016542594,0.00014268681,0.0025194234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886096,0.00049744395,0.000034364337,0.0004255545,0.0001033846,0.00007829784],"domain_scores_gemma":[0.99652964,0.0026632282,0.00016865734,0.00034159448,0.00019830624,0.00009854231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017769184,0.0009025774,0.0005063352,0.0015497338,0.00063135417,0.0013065547,0.0013683672,0.0012826963,0.0020457453],"category_scores_gemma":[0.0079477485,0.00031389523,0.0009180252,0.00093421503,0.0011522032,0.0038596478,0.0024192214,0.0024010488,0.0007007829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007684328,0.0010782846,0.021793103,0.0004733107,0.00030976982,0.0005714048,0.002854908,0.10217946,0.027094902,0.026326,0.015262885,0.80128765],"study_design_scores_gemma":[0.000039952807,0.0001794756,0.0037612095,0.000040920862,0.000039768667,0.00013643176,0.0004605525,0.94009143,0.006390216,0.04516904,0.0036611015,0.000029815568],"about_ca_topic_score_codex":0.0016221575,"about_ca_topic_score_gemma":0.00316726,"teacher_disagreement_score":0.0020457453,"about_ca_system_score_codex":0.00088195107,"about_ca_system_score_gemma":0.00038367766,"threshold_uncertainty_score":0.009397328},"labels":[],"label_agreement":null},{"id":"W4385571819","doi":"10.18653/v1/2023.findings-acl.289","title":"ClaimDiff: Comparing and Contrasting Claims on Contentious Issues","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Misinformation; Argument (complex analysis); Computer science; GRASP; Politics; Epistemology; Data science; Artificial intelligence; Political science; Computer security; Law; Philosophy","score_opus":0.03060270369584239,"score_gpt":0.26072390515212535,"score_spread":0.23012120145628295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385571819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6360116,0.01202694,0.070322186,0.004571265,0.0017932977,0.0013211648,0.20929296,0.012887287,0.05177323],"genre_scores_gemma":[0.5584598,0.0012740745,0.13334946,0.0011089443,0.00065983395,0.00088782323,0.29702377,0.0007068758,0.006529405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9915097,0.002677018,0.0012689136,0.0017084107,0.0025028952,0.0003329478],"domain_scores_gemma":[0.951799,0.026079616,0.0057849223,0.010773502,0.004055,0.0015079908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006666765,0.0015239952,0.0006906718,0.012949672,0.002182839,0.0034937866,0.0019086148,0.0037777796,0.0056455643],"category_scores_gemma":[0.039071597,0.00034193322,0.0009091118,0.004431764,0.001650997,0.0059925537,0.005340755,0.0019396823,0.004083855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003185203,0.0013722788,0.2286735,0.0069033764,0.0007776435,0.0034738726,0.01112317,0.004375048,0.028274681,0.029284341,0.32383835,0.35871854],"study_design_scores_gemma":[0.0005014661,0.0009794298,0.25479364,0.0017188577,0.00036506035,0.009634129,0.015173728,0.07514144,0.04042699,0.06419813,0.5366716,0.00039560223],"about_ca_topic_score_codex":0.0021879154,"about_ca_topic_score_gemma":0.006604031,"teacher_disagreement_score":0.012949672,"about_ca_system_score_codex":0.00082727947,"about_ca_system_score_gemma":0.0010767791,"threshold_uncertainty_score":0.035257638},"labels":[],"label_agreement":null},{"id":"W4385571961","doi":"10.18653/v1/2023.acl-short.17","title":"A Weakly Supervised Classifier and Dataset of White Supremacist Language","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Carnegie Mellon University; University of Pittsburgh","keywords":"Classifier (UML); Computer science; Artificial intelligence; Counterexample; Generalization; Speech recognition; Natural language processing; Mathematics","score_opus":0.01541823408194179,"score_gpt":0.2504659125987392,"score_spread":0.2350476785167974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385571961","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7369323,0.0023934578,0.041984055,0.0037008447,0.0013929828,0.0019083555,0.17983109,0.008044905,0.023811966],"genre_scores_gemma":[0.5244654,0.00046262518,0.06312641,0.001080098,0.0004557848,0.0016761558,0.39553368,0.00030765182,0.012892209],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978023,0.000613955,0.00023191144,0.0005405661,0.0006318385,0.000179394],"domain_scores_gemma":[0.99339044,0.0023434188,0.00065927475,0.0012753711,0.0017177698,0.00061371335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017109106,0.0011966866,0.0005355868,0.0032943708,0.0015276027,0.0010997809,0.0014602474,0.002433943,0.0027098376],"category_scores_gemma":[0.008154204,0.0002323009,0.0006757437,0.0015672648,0.0012535714,0.0014347702,0.0015350927,0.002086412,0.004355648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016306358,0.004516931,0.106419206,0.0018344146,0.00030193749,0.0036420592,0.0025593133,0.020392539,0.057209842,0.0059730313,0.45653665,0.33898348],"study_design_scores_gemma":[0.0006377483,0.0019803084,0.21059182,0.0006137793,0.0002320974,0.0066792993,0.0060779513,0.32213542,0.089102246,0.012303423,0.34915113,0.0004948614],"about_ca_topic_score_codex":0.005711927,"about_ca_topic_score_gemma":0.0108645065,"teacher_disagreement_score":0.005711927,"about_ca_system_score_codex":0.0009090188,"about_ca_system_score_gemma":0.0013810482,"threshold_uncertainty_score":0.011357307},"labels":[],"label_agreement":null},{"id":"W4385573793","doi":"10.18653/v1/2022.emnlp-main.809","title":"Directions for NLP Practices Applied to Online Hate Speech Detection","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"European Social Fund; Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior","keywords":"Computer science; Conceptualization; Task (project management); Artificial intelligence; Generalization; Natural language processing; Situated; Quality (philosophy); Epistemology","score_opus":0.026233301741495664,"score_gpt":0.2818900551993356,"score_spread":0.2556567534578399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385573793","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004819296,0.003912327,0.8934608,0.085105956,0.00051114894,0.00071557437,0.0002854946,0.0011706584,0.010018703],"genre_scores_gemma":[0.0795592,0.0027373533,0.90629554,0.004698634,0.0010049518,0.0026015707,0.0005094213,0.00045941715,0.0021339315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.70360756,0.23433816,0.013111511,0.023451999,0.0232622,0.0022284968],"domain_scores_gemma":[0.38472775,0.44621965,0.023772314,0.07722046,0.062301375,0.0057584224],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.31575662,0.0031003084,0.003518162,0.014100137,0.010254418,0.038503334,0.014713039,0.013333521,0.0097048],"category_scores_gemma":[0.40016434,0.0031914134,0.00282645,0.010382796,0.038073093,0.07615594,0.019467348,0.021476243,0.0051221787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014044085,0.00069673744,0.01739585,0.001487192,0.00018730047,0.0002778953,0.021364953,0.005152036,0.0023695622,0.61179847,0.012963174,0.32616648],"study_design_scores_gemma":[0.00006849951,0.0001372015,0.0031778165,0.0015059722,0.000049968992,0.00033231892,0.011700058,0.027697874,0.0026042862,0.90479934,0.047704924,0.00022180153],"about_ca_topic_score_codex":0.016822312,"about_ca_topic_score_gemma":0.0126473,"teacher_disagreement_score":0.31575662,"about_ca_system_score_codex":0.014221918,"about_ca_system_score_gemma":0.021374563,"threshold_uncertainty_score":0.8437939},"labels":[],"label_agreement":null},{"id":"W4385573884","doi":"10.18653/v1/2022.emnlp-main.819","title":"A Federated Approach to Predicting Emojis in Hindi Tweets","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Alberta","funders":"","keywords":"Emoji; Computer science; Hindi; Artificial intelligence; Machine learning; Task (project management); Natural language processing; World Wide Web; Social media","score_opus":0.012252662949465658,"score_gpt":0.21221018677420805,"score_spread":0.19995752382474238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385573884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6373763,0.0005946705,0.33959407,0.0015929063,0.00023324013,0.00028103736,0.00856092,0.007851159,0.0039156284],"genre_scores_gemma":[0.8740998,0.00009525334,0.11562843,0.000155422,0.000093413204,0.00012703116,0.007203985,0.000048546874,0.0025481333],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993451,0.00017460376,0.000046924906,0.00026363,0.00008638256,0.00008332307],"domain_scores_gemma":[0.9982387,0.00075494894,0.00015825735,0.00037600764,0.00032909168,0.00014309624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001413822,0.0007593496,0.00073097344,0.0016522038,0.0008347195,0.0009328355,0.0012010353,0.001039731,0.00101617],"category_scores_gemma":[0.0038139361,0.00028747338,0.000656778,0.0010430893,0.00038537176,0.0012041819,0.0012366021,0.001360678,0.00072684383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019100145,0.0017027492,0.092058636,0.00031644775,0.00036077312,0.0006561722,0.00096794317,0.30109626,0.020195007,0.0059377686,0.021406945,0.5533913],"study_design_scores_gemma":[0.000018138755,0.00007588647,0.00634042,0.000010832995,0.000021184987,0.00008297058,0.00011611643,0.9860175,0.0030035588,0.003161314,0.0011331561,0.000018921262],"about_ca_topic_score_codex":0.0076071545,"about_ca_topic_score_gemma":0.013854199,"teacher_disagreement_score":0.0076071545,"about_ca_system_score_codex":0.0009399676,"about_ca_system_score_gemma":0.0007906341,"threshold_uncertainty_score":0.0151257515},"labels":[],"label_agreement":null},{"id":"W4385734113","doi":"10.18653/v1/2023.wassa-1.25","title":"Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding Space","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Utterance; Dimension (graph theory); Computer science; Representation (politics); Embedding; Space (punctuation); Identity (music); Natural language processing; Word (group theory); Identification (biology); Speech recognition; Artificial intelligence; Linguistics; Mathematics; Aesthetics","score_opus":0.016908834620384817,"score_gpt":0.2701923306497711,"score_spread":0.25328349602938627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385734113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.326176,0.0015457969,0.6616581,0.00078538276,0.00031182417,0.00012743773,0.0022595944,0.0016873211,0.005448524],"genre_scores_gemma":[0.8721196,0.00079754024,0.116605975,0.00012245125,0.00018921196,0.00017911068,0.0043839146,0.00031306973,0.0052890843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990551,0.0003163248,0.000050407678,0.00024766565,0.00018152641,0.00014899508],"domain_scores_gemma":[0.99732375,0.001272262,0.00026806362,0.00034356394,0.00061107514,0.00018120612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009785201,0.0012696263,0.0007671447,0.0020340115,0.0005829095,0.0018044611,0.0006501398,0.0006190207,0.004528821],"category_scores_gemma":[0.0057888217,0.00030077173,0.00075135403,0.0015685151,0.0010116984,0.0018872395,0.0025253189,0.0013719936,0.0020011931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019698532,0.00042016897,0.024874644,0.00043491487,0.00023329123,0.00048392822,0.001961654,0.0197824,0.043234065,0.009982073,0.017922686,0.87870026],"study_design_scores_gemma":[0.00007395408,0.00058294,0.05672537,0.00019812609,0.0001779794,0.0007637425,0.0035337417,0.8554797,0.018368466,0.051741686,0.012139172,0.00021512088],"about_ca_topic_score_codex":0.0025073243,"about_ca_topic_score_gemma":0.0030077111,"teacher_disagreement_score":0.004528821,"about_ca_system_score_codex":0.00034931587,"about_ca_system_score_gemma":0.000542142,"threshold_uncertainty_score":0.015150428},"labels":[],"label_agreement":null},{"id":"W4385764438","doi":"10.24963/ijcai.2023/694","title":"Evaluating GPT-3 Generated Explanations for Hateful Content Moderation","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Moderation; Fluency; Content (measure theory); Social psychology; Psychology; Quality (philosophy); Cognitive psychology; Computer science; Epistemology; Mathematics education","score_opus":0.2562098803625319,"score_gpt":0.36501066480185435,"score_spread":0.10880078443932245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385764438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7972839,0.00048513993,0.15783033,0.0013879557,0.00021739196,0.0030416967,0.0041363817,0.02114923,0.014468031],"genre_scores_gemma":[0.7868285,0.00022575227,0.19845171,0.0005094633,0.00006473632,0.0021528383,0.0062539806,0.0014672375,0.0040457845],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9877061,0.008961231,0.0005976242,0.00072329334,0.0017500946,0.00026154198],"domain_scores_gemma":[0.7751828,0.1968996,0.007242233,0.010657213,0.008892142,0.0011259974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018172486,0.0012965563,0.00041327078,0.00148567,0.0005717894,0.0020384,0.0014063724,0.0016676863,0.0066662314],"category_scores_gemma":[0.1306393,0.0003991041,0.0008990986,0.0008477023,0.0008936189,0.0023043028,0.002368722,0.0017561995,0.0016160829],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041974112,0.0030914254,0.13819018,0.007634401,0.00047621242,0.0026453016,0.07790876,0.06960807,0.046431266,0.022045359,0.04362992,0.58414173],"study_design_scores_gemma":[0.0019693458,0.005215235,0.06850256,0.001927442,0.0007818418,0.0018569442,0.019674912,0.6725488,0.06062106,0.034530718,0.1317579,0.0006133108],"about_ca_topic_score_codex":0.0014413752,"about_ca_topic_score_gemma":0.0025785214,"teacher_disagreement_score":0.018172486,"about_ca_system_score_codex":0.0016079189,"about_ca_system_score_gemma":0.0015940472,"threshold_uncertainty_score":0.09610647},"labels":[],"label_agreement":null},{"id":"W4385775977","doi":"10.5281/zenodo.8238783","title":"About the Immigration Status of David DePape After the Attempted Murder on Paul Pelosi Dated October 28th 2022 - #How \"Cancel Culture\" in the United State of America (U.S.A) is Really Enabling Racism on Twitter & Elsewhere - #Michael Ayele (a.k.a) W","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Immigration; State (computer science); Culture of the United States; Criminology; History; Sociology; Political science; Economic history; Gerontology; Law; Medicine","score_opus":0.027186504507037978,"score_gpt":0.25381401261933245,"score_spread":0.22662750811229448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385775977","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28430942,0.006184073,0.00039663137,0.31473267,0.011332209,0.00014063058,0.0009319367,0.00014569618,0.38182673],"genre_scores_gemma":[0.5503749,0.0050506294,0.00032357877,0.102645405,0.0026588233,0.000120150566,0.0005760567,0.00008052392,0.3381698],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960965,0.00007323726,0.000017348968,0.00006159654,0.000098813514,0.00013941014],"domain_scores_gemma":[0.99904364,0.00020249806,0.00014911946,0.0000409088,0.0002231291,0.00034066566],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004981288,0.00027648616,0.00019692893,0.00045232294,0.01071162,0.0017924153,0.0005319676,0.0024119779,0.015516768],"category_scores_gemma":[0.004055688,0.00019793172,0.00012840123,0.00026217083,0.0016749741,0.0015511246,0.002266013,0.0056532435,0.0034047232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008876023,0.000080460435,0.024122743,0.00014536249,0.000017184308,0.010473829,0.0766562,0.00007468006,0.0015311148,0.008456752,0.84444475,0.033908263],"study_design_scores_gemma":[0.000011433444,0.00007050564,0.036703065,0.00040719952,0.000015750924,0.0025716494,0.12743756,0.00012270325,0.001078381,0.0005886651,0.8309363,0.000056747118],"about_ca_topic_score_codex":0.07370515,"about_ca_topic_score_gemma":0.1996445,"teacher_disagreement_score":0.98448324,"about_ca_system_score_codex":0.0019561015,"about_ca_system_score_gemma":0.0016468194,"threshold_uncertainty_score":0.14655226},"labels":[],"label_agreement":null},{"id":"W4385785343","doi":"10.25236/ijfs.2023.050811","title":"Social Media Companies’ Moderation of UGC and Journalistic Content Published on Their Platforms","year":2023,"lang":"en","type":"article","venue":"International Journal of Frontiers in Sociology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Vetting; Moderation; Social media optimization; Advertising; Quarter (Canadian coin); User-generated content; Political science; Media studies; Internet privacy; Sociology; Business; History; Psychology; Computer science; Law; Social psychology","score_opus":0.04757185726027149,"score_gpt":0.2785601766416663,"score_spread":0.23098831938139483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385785343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97164816,0.00035720275,0.000252665,0.002103632,0.00013022662,0.000046589423,0.0007237506,0.000031570733,0.024706196],"genre_scores_gemma":[0.9967945,0.00014803294,0.00013602983,0.00020030746,0.00019704012,0.00003372882,0.00022141206,0.000021039852,0.002248011],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953778,0.0022961313,0.00030067746,0.00042030774,0.0012181782,0.00038694983],"domain_scores_gemma":[0.89688253,0.060937244,0.02961466,0.0029088075,0.006298043,0.00335873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045546065,0.00023895713,0.0002712945,0.0029994051,0.001965288,0.004363965,0.00031191294,0.00082000747,0.0055232374],"category_scores_gemma":[0.04290624,0.0002195291,0.00015778358,0.0036771162,0.0014412558,0.0028506184,0.0023182086,0.0014244446,0.0010348989],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010209295,0.00037367066,0.80637515,0.0005281255,0.000231638,0.0008888853,0.11234484,0.00019265208,0.0058977343,0.007457483,0.013110139,0.051578693],"study_design_scores_gemma":[0.000022499993,0.0001562612,0.9460214,0.00010678704,0.000053840908,0.00018044123,0.028542642,0.00067692535,0.0017089618,0.0007227333,0.021769805,0.00003771689],"about_ca_topic_score_codex":0.0031212142,"about_ca_topic_score_gemma":0.0048877033,"teacher_disagreement_score":0.0055232374,"about_ca_system_score_codex":0.0011707786,"about_ca_system_score_gemma":0.0005623118,"threshold_uncertainty_score":0.02408731},"labels":[],"label_agreement":null},{"id":"W4385934913","doi":"10.2139/ssrn.4537139","title":"The Right to Life of Children and Cyberbullying Dominates Human Rights: Society Impacts","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wycliffe College","funders":"","keywords":"Human rights; Right to life; Psychology; Political science; Law","score_opus":0.0046865759932403625,"score_gpt":0.22999017131723073,"score_spread":0.22530359532399039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385934913","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33472702,0.0038574894,0.0015728427,0.1081987,0.00045602408,0.00003356268,0.00020409445,0.000014723163,0.55093557],"genre_scores_gemma":[0.9881273,0.00082574895,0.0001137861,0.002983914,0.00019248755,0.000010767212,0.00002179923,0.000010065333,0.007714099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945962,0.0018827203,0.00009389957,0.0005764289,0.00077689486,0.0020739988],"domain_scores_gemma":[0.9896925,0.0041985656,0.0015802009,0.0004056858,0.00082266936,0.0033003825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035314385,0.00031122498,0.00048496277,0.0011110742,0.005470961,0.0075934227,0.000741649,0.003465641,0.034350574],"category_scores_gemma":[0.011113882,0.00018974057,0.00032244297,0.0010011141,0.020981072,0.0058136065,0.006915241,0.0045578266,0.000964288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089293026,0.00032807415,0.03564722,0.0000816325,0.00003428157,0.0007380204,0.019222511,0.0002141103,0.00081716967,0.90414727,0.0063716397,0.032308713],"study_design_scores_gemma":[0.00006463338,0.00027634075,0.13020201,0.00088254485,0.000090357236,0.0014735925,0.12364704,0.00039027494,0.0015709403,0.52317864,0.21813385,0.000089759895],"about_ca_topic_score_codex":0.012146598,"about_ca_topic_score_gemma":0.020752748,"teacher_disagreement_score":0.034350574,"about_ca_system_score_codex":0.0034675999,"about_ca_system_score_gemma":0.006069854,"threshold_uncertainty_score":0.11491412},"labels":[],"label_agreement":null},{"id":"W4386261907","doi":"10.2139/ssrn.4541590","title":"What are Canadian Politicians Doing on Threads?","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École Nationale d'Administration Publique","funders":"","keywords":"Political science; Business","score_opus":0.00931030325524438,"score_gpt":0.2301909456635429,"score_spread":0.22088064240829852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386261907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45952085,0.0041217464,0.0009326565,0.12335945,0.0017554832,0.00013638478,0.0033039793,0.00018446216,0.40668505],"genre_scores_gemma":[0.9129811,0.0022622442,0.00040714943,0.008102926,0.000262339,0.000038355814,0.00072611525,0.000103491395,0.07511632],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9903564,0.00088312593,0.00015766388,0.00060558313,0.003583036,0.004414297],"domain_scores_gemma":[0.9639239,0.003447596,0.003484644,0.00079090556,0.014557701,0.013795394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037931888,0.00035966976,0.00042133988,0.0043245694,0.01806176,0.013675836,0.0016018716,0.0028457933,0.029471995],"category_scores_gemma":[0.026573706,0.00045671474,0.0005086765,0.0073247342,0.0043389257,0.003957424,0.0015410006,0.0035364276,0.003948142],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044708457,0.00021557845,0.39095306,0.00033812664,0.000112583555,0.00043076725,0.06876264,0.0003106204,0.00091290975,0.066069216,0.30463892,0.16680852],"study_design_scores_gemma":[0.00004242457,0.000070329166,0.40580237,0.0004593956,0.00010197693,0.00013540938,0.14589556,0.00038064245,0.0006408758,0.0042364695,0.44203737,0.00019721255],"about_ca_topic_score_codex":0.98272413,"about_ca_topic_score_gemma":0.98986197,"teacher_disagreement_score":0.063259736,"about_ca_system_score_codex":0.063259736,"about_ca_system_score_gemma":0.12404493,"threshold_uncertainty_score":0.45898366},"labels":[],"label_agreement":null},{"id":"W4386304507","doi":"10.18254/s207054760027323-2","title":"Specifics of Canada&amp;apos;s Approach to Online Safety","year":2023,"lang":"en","type":"article","venue":"Russia and America in the 21st Century","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Obligation; Legislation; Statutory law; Democracy; Government (linguistics); Public relations; Internet privacy; Business; Political science; Public administration; Law; Computer science; Politics","score_opus":0.03623293972873543,"score_gpt":0.2602819707001118,"score_spread":0.22404903097137635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386304507","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017528659,0.0032568541,0.010291691,0.14564516,0.0016295797,0.0005856501,0.0017332103,0.00023627415,0.819093],"genre_scores_gemma":[0.315079,0.005428967,0.0155856935,0.07397731,0.00066344294,0.00044257528,0.0007956094,0.00020649284,0.5878209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9887822,0.00088021584,0.00025288595,0.0012492768,0.005666054,0.0031693163],"domain_scores_gemma":[0.9880101,0.0013575514,0.0003754167,0.00051329215,0.0071755117,0.0025682175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029548148,0.00075696065,0.00043555044,0.0040444266,0.024229122,0.0126090115,0.0027855437,0.0069364863,0.014973957],"category_scores_gemma":[0.008747295,0.0005733149,0.00090609427,0.0041082767,0.013049568,0.0028479928,0.0037723803,0.0077061485,0.0016676927],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026871528,0.00006607487,0.0086982185,0.00019376176,0.000026459644,0.00062628894,0.008915996,0.0007710866,0.0007898663,0.6876325,0.24983017,0.042422812],"study_design_scores_gemma":[0.000009172941,0.00001677273,0.012922425,0.0003038669,0.000026644837,0.00041243742,0.0061942544,0.0007421896,0.0006805341,0.017242048,0.96133673,0.00011283259],"about_ca_topic_score_codex":0.9846771,"about_ca_topic_score_gemma":0.9930133,"teacher_disagreement_score":0.102290966,"about_ca_system_score_codex":0.102290966,"about_ca_system_score_gemma":0.21124879,"threshold_uncertainty_score":0.7421764},"labels":[],"label_agreement":null},{"id":"W4386392229","doi":"10.31235/osf.io/6cts8","title":"Gender biases and hate speech: promoters and targets in the Argentinean political context","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Politics; Popularity; Context (archaeology); Political science; Right wing; Sociology; Law; History","score_opus":0.08500512924136282,"score_gpt":0.2873379786053071,"score_spread":0.20233284936394424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386392229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997682,0.00014392976,0.00012611086,0.00011927929,0.0000042130864,0.000003989852,0.00003991132,8.79288e-7,0.0018797282],"genre_scores_gemma":[0.9995271,0.0000900566,0.000048223734,0.00002272328,0.000005977923,0.0000037557597,0.000029559937,0.0000017311352,0.00027080375],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99908614,0.0005007149,0.000035013134,0.00010111242,0.00015389099,0.0001231257],"domain_scores_gemma":[0.9942737,0.0027785227,0.001930517,0.00021274293,0.0004056077,0.00039887114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017191693,0.00020193392,0.0002148953,0.0007553376,0.0011601101,0.0012014809,0.00018876325,0.00042823315,0.0023376325],"category_scores_gemma":[0.0076290327,0.00015760808,0.000083091196,0.00076082535,0.0010264526,0.0008824054,0.000967969,0.00045348864,0.00027029484],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019365808,0.00008328114,0.9030003,0.000054525928,0.00001653099,0.00016184349,0.08066689,0.000047092948,0.000942723,0.00065933476,0.00035479834,0.013818999],"study_design_scores_gemma":[0.000003141245,0.000061975064,0.92225194,0.00005551395,0.000013282521,0.00017803673,0.07465849,0.0005383594,0.00027117398,0.00026819314,0.0016898004,0.000010094618],"about_ca_topic_score_codex":0.011206142,"about_ca_topic_score_gemma":0.017360942,"teacher_disagreement_score":0.011206142,"about_ca_system_score_codex":0.0005326546,"about_ca_system_score_gemma":0.00025005703,"threshold_uncertainty_score":0.022281826},"labels":[],"label_agreement":null},{"id":"W4386566625","doi":"10.18653/v1/2023.eacl-main.237","title":"A Federated Approach for Hate Speech Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Voice activity detection; Computer science; Task (project management); Social media; Emotion detection; Internet privacy; Speech recognition; Artificial intelligence; Machine learning; Natural language processing; Computer security; Data science; Speech processing; World Wide Web; Emotion recognition; Engineering","score_opus":0.022415163177105547,"score_gpt":0.2433323136491364,"score_spread":0.22091715047203087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386566625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12526165,0.00093240954,0.8583132,0.0012336832,0.00033228524,0.00030323665,0.00081650267,0.008275258,0.004531746],"genre_scores_gemma":[0.84670544,0.00021546533,0.14575306,0.00034091744,0.00015045032,0.00015251542,0.0009337962,0.000091920556,0.005656428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99620074,0.001345135,0.00020935824,0.000983692,0.0008348104,0.0004262032],"domain_scores_gemma":[0.9946255,0.001565431,0.000486965,0.0014413459,0.0015600694,0.0003205837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003894072,0.00090817944,0.00176051,0.0029475035,0.0013196133,0.0019965703,0.0015281753,0.0021917652,0.0018337711],"category_scores_gemma":[0.0071661873,0.00026326443,0.0009269636,0.0011987716,0.0008877114,0.0027956474,0.002628777,0.0016194044,0.0016475102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011554359,0.0018806236,0.01957506,0.00022211496,0.00029836345,0.00062744186,0.0005376757,0.05883849,0.025278797,0.008529486,0.010812767,0.87224376],"study_design_scores_gemma":[0.000021965308,0.0003151014,0.0058507477,0.00004123998,0.00008091239,0.00053677236,0.00023658664,0.9506846,0.018777711,0.019350769,0.0040431973,0.000060399914],"about_ca_topic_score_codex":0.002921735,"about_ca_topic_score_gemma":0.0032065986,"teacher_disagreement_score":0.003894072,"about_ca_system_score_codex":0.0011124426,"about_ca_system_score_gemma":0.0014304379,"threshold_uncertainty_score":0.02059406},"labels":[],"label_agreement":null},{"id":"W4386566992","doi":"10.18653/v1/2023.c3nlp-1.8","title":"Toward Cultural Bias Evaluation Datasets: The Case of Bengali Gender, Religious, and National Identity","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bengali; Unavailability; Nationality; Artifact (error); Identity (music); Process (computing); Cultural identity; Computer science; Natural language processing; Artificial intelligence; Psychology; Social psychology; Political science; Statistics; Mathematics; Immigration; Aesthetics","score_opus":0.14456245023152445,"score_gpt":0.3546193864170398,"score_spread":0.21005693618551532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386566992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7342205,0.0024476615,0.044544313,0.011015443,0.0015327815,0.0038814798,0.14037685,0.0059407544,0.05604026],"genre_scores_gemma":[0.71960825,0.00049980055,0.07663596,0.0015625583,0.00020699833,0.0023861406,0.18839729,0.0005059253,0.01019704],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9876237,0.006975943,0.0012407674,0.0012591528,0.002188454,0.0007120338],"domain_scores_gemma":[0.9742182,0.0061489823,0.0018695613,0.00733204,0.009244106,0.0011871376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011887546,0.00081561424,0.00056944875,0.0037161189,0.003426753,0.0029540367,0.0025843484,0.0019290698,0.0021584365],"category_scores_gemma":[0.028283758,0.00019582456,0.0007068904,0.0038444188,0.0016917476,0.002341501,0.0036243899,0.0016331108,0.0018803799],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002468729,0.003083737,0.2298328,0.0034302636,0.0006342754,0.0024407236,0.01884524,0.011287692,0.023578899,0.033116437,0.3695798,0.30170143],"study_design_scores_gemma":[0.0003548826,0.0006308031,0.27722222,0.0009881456,0.0003218412,0.0016287315,0.030840607,0.04300003,0.045121077,0.014874032,0.58472073,0.0002969034],"about_ca_topic_score_codex":0.04665584,"about_ca_topic_score_gemma":0.07161816,"teacher_disagreement_score":0.04665584,"about_ca_system_score_codex":0.004101466,"about_ca_system_score_gemma":0.0032237268,"threshold_uncertainty_score":0.09276855},"labels":[],"label_agreement":null},{"id":"W4386570977","doi":"10.5539/ijel.v13n5p1","title":"Hate Speech, incitamento all’odio, incitación al odio: EU Parallel Corpora, Legal Discourse, Metadiscourse and Translation","year":2023,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metadiscourse; Linguistics; Dignity; Rhetorical question; Psychology; Sociology; Political science; Law; Philosophy","score_opus":0.023640398661495927,"score_gpt":0.3005813538656628,"score_spread":0.2769409552041669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386570977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6876716,0.03098332,0.023724357,0.0066226823,0.00059820537,0.0014150172,0.017123539,0.000190503,0.23167084],"genre_scores_gemma":[0.92507154,0.006948501,0.035209656,0.00068289175,0.00024237322,0.0024404817,0.012659684,0.00029595368,0.016448913],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9950537,0.0027345582,0.00046023182,0.000625202,0.0009852877,0.00014111305],"domain_scores_gemma":[0.980612,0.013622707,0.0017152432,0.0020238194,0.0018074653,0.00021876016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076661394,0.0002739039,0.00038410455,0.0051692906,0.0028099606,0.0043385243,0.0008097351,0.0009829585,0.0062612],"category_scores_gemma":[0.016736893,0.00033323653,0.00017954988,0.013520297,0.0044718753,0.003680617,0.0024892467,0.0014399908,0.00089902565],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081107655,0.00076861924,0.023179822,0.0059365695,0.000062778236,0.0036298037,0.3591048,0.0012424765,0.0060744802,0.29932323,0.051594798,0.24827147],"study_design_scores_gemma":[0.00012403795,0.00016193559,0.07296057,0.0030003292,0.0000679366,0.0017068865,0.1382134,0.0018426945,0.0070900316,0.021049684,0.7536928,0.00008964596],"about_ca_topic_score_codex":0.006697757,"about_ca_topic_score_gemma":0.009983575,"teacher_disagreement_score":0.0076661394,"about_ca_system_score_codex":0.0034330674,"about_ca_system_score_gemma":0.0020054863,"threshold_uncertainty_score":0.0405429},"labels":[],"label_agreement":null},{"id":"W4386690757","doi":"10.18280/isi.280430","title":"Application of LSTM and GloVe Word Embedding for Hate Speech Detection in Indonesian Twitter Data","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Muhammadiyah Surakarta","keywords":"Indonesian; Computer science; Word (group theory); Speech recognition; Natural language processing; Word embedding; Artificial intelligence; Embedding; Linguistics","score_opus":0.02253170171451098,"score_gpt":0.26714233316004266,"score_spread":0.2446106314455317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386690757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85581446,0.0021087523,0.12743053,0.00083340664,0.00050747866,0.00013037618,0.0040045623,0.0045296866,0.0046407194],"genre_scores_gemma":[0.95584714,0.00047664583,0.03664571,0.00009152585,0.0000534082,0.000083983476,0.0038996008,0.000061518906,0.0028404065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966216,0.000092334136,0.00003358516,0.00010005147,0.000052406795,0.00005944474],"domain_scores_gemma":[0.99934953,0.0003237166,0.000062028645,0.00008166337,0.00015640781,0.00002657043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007806293,0.000970171,0.00039234536,0.0009470995,0.00029079476,0.0005467628,0.00044767783,0.00064823794,0.0010031392],"category_scores_gemma":[0.0025620356,0.0001768962,0.0004934095,0.0006406518,0.0002072154,0.0009882165,0.00070495834,0.00085095485,0.0009133257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009045878,0.0005609123,0.035344936,0.00045856097,0.00025873043,0.0006932879,0.0007492002,0.091373056,0.05006445,0.0012237696,0.011277942,0.8070906],"study_design_scores_gemma":[0.000012186831,0.00014681331,0.010987349,0.00003926375,0.00004600999,0.00012045028,0.00036543916,0.9660896,0.01872082,0.0009682902,0.0024732442,0.000030646777],"about_ca_topic_score_codex":0.0069666286,"about_ca_topic_score_gemma":0.008592441,"teacher_disagreement_score":0.0069666286,"about_ca_system_score_codex":0.00044367352,"about_ca_system_score_gemma":0.0005354283,"threshold_uncertainty_score":0.013852119},"labels":[],"label_agreement":null},{"id":"W4386708306","doi":"10.18280/isi.280410","title":"Hybrid Deep Learning Approach and Word2Vec Feature Expansion for Cyberbullying Detection on Indonesian Twitter","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Word2vec; Indonesian; Feature (linguistics); Computer science; Artificial intelligence; Deep learning; Data science; Psychology; Pattern recognition (psychology)","score_opus":0.013244997479633695,"score_gpt":0.2205470948742381,"score_spread":0.2073020973946044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386708306","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.795984,0.0012734673,0.18679032,0.0006006605,0.00035772708,0.00020587383,0.0023670953,0.0070310417,0.0053899013],"genre_scores_gemma":[0.94793314,0.00028627145,0.04306763,0.0001295829,0.000039163442,0.000110064226,0.0034085237,0.00006168247,0.0049638757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966145,0.00006555776,0.000027895838,0.00009198339,0.00006998882,0.00008315429],"domain_scores_gemma":[0.9997292,0.00007915283,0.000022945771,0.000030330677,0.00011949579,0.000018739298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046952916,0.00106006,0.00046169234,0.0009603405,0.0003228416,0.00039650468,0.000483047,0.00047510467,0.001260349],"category_scores_gemma":[0.0011002515,0.00023660797,0.0005448125,0.0005713326,0.00016471578,0.0009192742,0.0005523649,0.0006298039,0.0008301459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083217997,0.00091433746,0.025735475,0.00023300022,0.00026805836,0.00058494776,0.0002653182,0.104751065,0.043288454,0.0007631413,0.01289567,0.80946827],"study_design_scores_gemma":[0.000011899475,0.00014800488,0.0043985485,0.000012392378,0.0000389024,0.000075844786,0.00010934072,0.9781259,0.015618618,0.00031352064,0.0011303712,0.000016621341],"about_ca_topic_score_codex":0.015026345,"about_ca_topic_score_gemma":0.019147996,"teacher_disagreement_score":0.015026345,"about_ca_system_score_codex":0.000515345,"about_ca_system_score_gemma":0.00055682956,"threshold_uncertainty_score":0.029877722},"labels":[],"label_agreement":null},{"id":"W4386713710","doi":"10.18280/isi.280414","title":"Utilizing K-Means Clustering for the Detection of Cyberbullying Within Instagram Comments","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence","score_opus":0.026043458801169237,"score_gpt":0.24966833643089834,"score_spread":0.2236248776297291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386713710","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6763068,0.0011272525,0.3050239,0.00049440784,0.00030240437,0.0010252388,0.0031380116,0.0057965918,0.0067854007],"genre_scores_gemma":[0.73705035,0.0002580962,0.25601584,0.000053618987,0.000060373473,0.00041302483,0.0037665076,0.00015065822,0.002231583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983197,0.0002458979,0.00018158543,0.00047205057,0.0005603508,0.00022040549],"domain_scores_gemma":[0.9972741,0.00076630054,0.00039044814,0.00024496755,0.0012340876,0.00009008863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235771,0.0009731084,0.0009027047,0.0060259122,0.001315054,0.0012273723,0.0009812148,0.0010402397,0.0008221838],"category_scores_gemma":[0.004445762,0.00022512443,0.0010418077,0.0031883188,0.00048186257,0.0007238662,0.0008322832,0.0006518302,0.0013230621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011643119,0.00073229603,0.07452317,0.000995095,0.0003456986,0.00080618146,0.0051392037,0.034798145,0.07473653,0.002383517,0.013849317,0.7905265],"study_design_scores_gemma":[0.000052230673,0.00034338507,0.14764053,0.00020791219,0.00020987367,0.00089348666,0.009356554,0.74536765,0.07483666,0.004379016,0.016354648,0.00035812016],"about_ca_topic_score_codex":0.027548673,"about_ca_topic_score_gemma":0.029300252,"teacher_disagreement_score":0.027548673,"about_ca_system_score_codex":0.00089183263,"about_ca_system_score_gemma":0.0013068763,"threshold_uncertainty_score":0.05477667},"labels":[],"label_agreement":null},{"id":"W4386766023","doi":"10.26443/arc.v32i.1016","title":"University Student Failed!!!","year":2004,"lang":"en","type":"article","venue":"Arc The Journal of the School of Religious Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics education; Psychology","score_opus":0.010509768318876735,"score_gpt":0.23878891628318236,"score_spread":0.22827914796430562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386766023","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11814086,0.0027991328,0.0016597983,0.23375054,0.024258822,0.0004137463,0.0024094917,0.0012082177,0.6153593],"genre_scores_gemma":[0.13782504,0.0005558865,0.0004000839,0.028100668,0.0006214925,0.00009360914,0.00029631206,0.00026144402,0.8318454],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977385,0.00061684777,0.0000665011,0.00020121412,0.00067777745,0.00069923507],"domain_scores_gemma":[0.99609584,0.00031518607,0.00021571651,0.00035513614,0.001644018,0.0013741028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015085773,0.00058150967,0.00056144234,0.0006908061,0.009876423,0.004388547,0.0009370459,0.0020030674,0.13069741],"category_scores_gemma":[0.008063998,0.00033558538,0.00034305739,0.0005240386,0.0020548843,0.0029798993,0.0048567373,0.0035362206,0.072076514],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103941944,0.0000844276,0.009200503,0.00009395774,0.000010826393,0.0006825412,0.01631301,0.000021156657,0.0007277389,0.010077772,0.8881416,0.07454262],"study_design_scores_gemma":[0.0000044655408,0.000073612486,0.0113277035,0.00007237917,0.0000055073688,0.0006165797,0.025373131,0.00005356853,0.0003879966,0.00065101933,0.9613992,0.000034844077],"about_ca_topic_score_codex":0.02229271,"about_ca_topic_score_gemma":0.041869286,"teacher_disagreement_score":0.13069741,"about_ca_system_score_codex":0.0024593943,"about_ca_system_score_gemma":0.0016636628,"threshold_uncertainty_score":0.4372264},"labels":[],"label_agreement":null},{"id":"W4386803063","doi":"10.1145/3623396","title":"Multimodal Religiously Hateful Social Media Memes Classification Based on Textual and Image Data","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Social media; Computer science; Artificial intelligence; Image (mathematics); Natural language processing; Information retrieval; Pattern recognition (psychology); Linguistics; World Wide Web","score_opus":0.017386022366471538,"score_gpt":0.2582952664607165,"score_spread":0.24090924409424494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386803063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90055966,0.0040229564,0.037599392,0.0010972965,0.00082398107,0.0005935307,0.020940244,0.004982363,0.029380633],"genre_scores_gemma":[0.9162321,0.0010686252,0.037512828,0.00024591672,0.00038612532,0.0003009907,0.03114645,0.00014724661,0.012959754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969673,0.000041062693,0.000019391206,0.00009210022,0.000084274456,0.000066427194],"domain_scores_gemma":[0.9994797,0.00013485223,0.00006674205,0.00009499946,0.0001615921,0.0000622074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039497172,0.0011662657,0.00044100484,0.0027797325,0.0004161962,0.0007381795,0.0004936059,0.00081381743,0.0025093262],"category_scores_gemma":[0.0015684263,0.000099198885,0.0006030067,0.0010193246,0.0003617711,0.0011371874,0.0007944582,0.00058443623,0.0016601699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015260831,0.0008505678,0.052754745,0.0014557433,0.00032889258,0.001872505,0.000928374,0.014451615,0.065616935,0.0020647992,0.062265497,0.7958842],"study_design_scores_gemma":[0.00008559525,0.0007608027,0.18212324,0.000501811,0.00038488946,0.0025644314,0.006563695,0.6083951,0.11300614,0.0041301376,0.081298,0.00018607246],"about_ca_topic_score_codex":0.0035850247,"about_ca_topic_score_gemma":0.007591673,"teacher_disagreement_score":0.0035850247,"about_ca_system_score_codex":0.00041553754,"about_ca_system_score_gemma":0.0002698108,"threshold_uncertainty_score":0.008394539},"labels":[],"label_agreement":null},{"id":"W4386810309","doi":"10.18280/ria.370413","title":"Enhancing Cyberbullying Detection on Indonesian Twitter: Leveraging FastText for Feature Expansion and Hybrid Approach Applying CNN and BiLSTM","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Indonesian; Feature (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.03319803522907172,"score_gpt":0.2519311943709145,"score_spread":0.21873315914184277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386810309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84447366,0.0019410244,0.12843163,0.001265918,0.000562792,0.0002402059,0.0043356414,0.008962863,0.00978626],"genre_scores_gemma":[0.9550351,0.00037983304,0.032003593,0.00027027173,0.000100894205,0.00012028297,0.006101795,0.0000992993,0.005888967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976724,0.000032302974,0.000015401945,0.00006558583,0.000050310387,0.000069189984],"domain_scores_gemma":[0.99971944,0.00007308238,0.000038547278,0.00003646796,0.00010704106,0.000025470541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003398021,0.0012580323,0.00048150535,0.0011289642,0.0003330609,0.0005228818,0.00066656916,0.0005102402,0.0016097764],"category_scores_gemma":[0.0013431618,0.00017919757,0.00052789564,0.0006020026,0.00021859986,0.0011915301,0.00095457985,0.0007678183,0.0011113764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000965851,0.0009931126,0.037816573,0.0003557182,0.00020634456,0.00088442554,0.0003101267,0.072070286,0.050991442,0.00089953543,0.019292116,0.8152145],"study_design_scores_gemma":[0.000026479625,0.0002642026,0.011907819,0.000037683476,0.000066330416,0.00017730992,0.00023852491,0.9580693,0.023909543,0.0011799808,0.0040917573,0.000031095347],"about_ca_topic_score_codex":0.0077274973,"about_ca_topic_score_gemma":0.012502757,"teacher_disagreement_score":0.0077274973,"about_ca_system_score_codex":0.00044839486,"about_ca_system_score_gemma":0.000500688,"threshold_uncertainty_score":0.015365064},"labels":[],"label_agreement":null},{"id":"W4387092975","doi":"10.7202/1106308ar","title":"iTutorGroup: A case study of covert native-speakerism underneath a social justice façade","year":2023,"lang":"en","type":"article","venue":"McGill Journal of Education / Revue des sciences de l éducation de McGill","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Covert; Wage; Economic Justice; Political science; Sociology; Psychology; Media studies; Social psychology; Law; Linguistics","score_opus":0.32489785970724216,"score_gpt":0.4235859500854836,"score_spread":0.09868809037824144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387092975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96889716,0.0002740898,0.00150995,0.004370252,0.00014616142,0.00009415532,0.00004580955,0.000052449923,0.024609964],"genre_scores_gemma":[0.98466533,0.00035576185,0.0014069867,0.0009972968,0.00007730745,0.00009769073,0.000033100376,0.000062964085,0.012303521],"study_design_codex":"qualitative","study_design_gemma":"case_report","domain_scores_codex":[0.99450374,0.003184752,0.00014769075,0.00041353283,0.0005707161,0.0011795236],"domain_scores_gemma":[0.9923308,0.0037654066,0.0010213997,0.000739208,0.00043010575,0.0017130987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005133113,0.0009508604,0.0005392154,0.0016710375,0.024199748,0.004561214,0.001846468,0.00514015,0.0066448795],"category_scores_gemma":[0.011817959,0.0006997682,0.00057814905,0.0014394445,0.0075677442,0.0032104251,0.0063052187,0.006055504,0.0009866162],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011761008,0.00046574054,0.020576613,0.00012886795,0.000018602244,0.057959177,0.8819265,0.00015237433,0.0015373969,0.011067929,0.0082038855,0.017845279],"study_design_scores_gemma":[0.000013491274,0.00025852805,0.008345749,0.00018045085,0.000018157196,0.030600552,0.926925,0.00062369724,0.0012543984,0.0011525368,0.030576395,0.000051038973],"about_ca_topic_score_codex":0.015927767,"about_ca_topic_score_gemma":0.04537913,"teacher_disagreement_score":0.024199748,"about_ca_system_score_codex":0.0034108853,"about_ca_system_score_gemma":0.0027024301,"threshold_uncertainty_score":0.031670094},"labels":[],"label_agreement":null},{"id":"W4387133783","doi":"10.18280/ijsse.130413","title":"Detecting Cyberbullying on Social Media Using Support Vector Machine: A Case Study on Twitter","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Urmia University of Medical Sciences","keywords":"Social media; Support vector machine; Computer science; Internet privacy; Computer security; Psychology; World Wide Web; Artificial intelligence","score_opus":0.029695639814743414,"score_gpt":0.2819665288578697,"score_spread":0.2522708890431263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387133783","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963404,0.000079976,0.0015959324,0.0003345915,0.00001743758,0.000064272324,0.00046687172,0.000038885,0.0010615912],"genre_scores_gemma":[0.9934104,0.0001755256,0.004184939,0.00005496428,0.000037084366,0.00006771175,0.0007648087,0.000015092462,0.0012893906],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991799,0.00030196737,0.000080768936,0.000107162086,0.00022305718,0.00010704442],"domain_scores_gemma":[0.9979989,0.0011917787,0.00022884362,0.00014255047,0.00031925907,0.000118521944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007961884,0.00049774646,0.00044828653,0.0012898367,0.0011421492,0.0008695224,0.0005903164,0.0012500023,0.00072947895],"category_scores_gemma":[0.0026822607,0.0001600039,0.00037626646,0.0010810245,0.0005959575,0.0012549446,0.0007305897,0.000519644,0.0003692827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015889263,0.0036645497,0.6328455,0.001136409,0.00026465804,0.033161283,0.019394659,0.032567807,0.036481038,0.0034122183,0.014587114,0.22089572],"study_design_scores_gemma":[0.000107832915,0.0020221886,0.42998824,0.00027119342,0.0002500124,0.007838566,0.055250358,0.43316224,0.045779906,0.00275421,0.02236881,0.0002064553],"about_ca_topic_score_codex":0.006206969,"about_ca_topic_score_gemma":0.010057129,"teacher_disagreement_score":0.006206969,"about_ca_system_score_codex":0.00051088963,"about_ca_system_score_gemma":0.00029062762,"threshold_uncertainty_score":0.012341678},"labels":[],"label_agreement":null},{"id":"W4387696100","doi":"10.3389/fcomm.2023.1260540","title":"Social media attacks against female Canadian journalists","year":2023,"lang":"en","type":"article","venue":"Frontiers in Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; University of British Columbia; Government of Canada","keywords":"Disinformation; Social media; Media studies; Political science; Nonprobability sampling; Digital media; Journalism; Criminology; Internet privacy; Sociology; Advertising; Computer science; Law; Business","score_opus":0.027736197768941134,"score_gpt":0.26588398417883036,"score_spread":0.23814778640988923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387696100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94419396,0.001164989,0.0002402941,0.0033390324,0.00011710118,0.00012391536,0.00058150565,0.00001712876,0.05022214],"genre_scores_gemma":[0.98810184,0.0010272784,0.00028268187,0.0009881266,0.00007332052,0.00005582971,0.00014291439,0.000007696059,0.009320384],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9959608,0.0004822045,0.0000712891,0.0003611128,0.0017469862,0.0013777136],"domain_scores_gemma":[0.9906516,0.002290387,0.002578513,0.00030006372,0.0028109306,0.0013685359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017384108,0.00059453375,0.00045752674,0.0037746334,0.018648712,0.0047716885,0.0010627522,0.0012998416,0.005187867],"category_scores_gemma":[0.011226391,0.0003852737,0.00022447757,0.0032368246,0.005314627,0.0014148544,0.0023443196,0.0014679004,0.00062639115],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003304315,0.00021982839,0.3408244,0.0005075493,0.000071953305,0.0021421034,0.5558913,0.0001525969,0.004530108,0.011700585,0.02100349,0.06262565],"study_design_scores_gemma":[0.000015757252,0.00014574753,0.36793506,0.00040713907,0.00005931167,0.0010373475,0.5134276,0.00031900225,0.0020019207,0.00048306785,0.114042714,0.00012535868],"about_ca_topic_score_codex":0.9072065,"about_ca_topic_score_gemma":0.956375,"teacher_disagreement_score":0.092793524,"about_ca_system_score_codex":0.022839343,"about_ca_system_score_gemma":0.023575772,"threshold_uncertainty_score":0.18667996},"labels":[],"label_agreement":null},{"id":"W4387776273","doi":"10.5209/cjes.89255","title":"Guillén-Nieto, Victoria. 2023. Hate speech: Linguistic perspectives. Berlin: De Gruyter. 211 pp. ISBN: 9783110672466","year":2023,"lang":"en","type":"article","venue":"Complutense Journal of English Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sociology; Linguistics; Humanities; Art; Philosophy","score_opus":0.029735663451770863,"score_gpt":0.28466612979690376,"score_spread":0.2549304663451329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387776273","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007617046,0.7825143,0.0017603422,0.0075839646,0.0032340768,0.00002365925,0.0014983799,0.00021078937,0.20241275],"genre_scores_gemma":[0.015254901,0.49576086,0.0039458266,0.0024862261,0.0006921692,0.00014079876,0.001817234,0.00075854216,0.47914347],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99946266,0.00010039045,0.000047740177,0.000103019505,0.00022785776,0.00005835175],"domain_scores_gemma":[0.9995165,0.00023172481,0.000084613406,0.000027665477,0.00007975894,0.000059859296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000494295,0.0016494415,0.0010338603,0.002478611,0.0014183242,0.0044882973,0.0010845838,0.0025659672,0.123890646],"category_scores_gemma":[0.0013075729,0.0015196139,0.00043750872,0.0063302806,0.0016636084,0.00703654,0.0019252854,0.003146896,0.06077724],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027714848,0.000013480552,0.00016466145,0.0009451407,0.000011100756,0.00021290638,0.0018618567,0.0002775615,0.00031145237,0.0329081,0.7292413,0.2340248],"study_design_scores_gemma":[0.0000016016172,0.000003455823,0.0005076995,0.00089067296,0.0000024455308,0.0001964597,0.0005069895,0.000045605797,0.000045653185,0.002759676,0.9950315,0.00000823135],"about_ca_topic_score_codex":0.027023487,"about_ca_topic_score_gemma":0.059500292,"teacher_disagreement_score":0.123890646,"about_ca_system_score_codex":0.003213425,"about_ca_system_score_gemma":0.0020260466,"threshold_uncertainty_score":0.41445547},"labels":[],"label_agreement":null},{"id":"W4388044495","doi":"10.1080/23311886.2023.2274430","title":"Hate speech and the harm in Indonesian judicial decisions","year":2023,"lang":"en","type":"article","venue":"Cogent Social Sciences","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi; Lembaga Pengelola Dana Pendidikan; Universitas Gadjah Mada; Ministry of Education","keywords":"Indonesian; Harm; Political science; Law and economics; Judicial opinion; Law; Sociology; Linguistics; Philosophy","score_opus":0.045680707737695,"score_gpt":0.3065063708913793,"score_spread":0.2608256631536843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388044495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95515156,0.0006065894,0.00092960894,0.0016163406,0.00003660308,0.000035528017,0.000026331647,0.000003344177,0.04159419],"genre_scores_gemma":[0.9985115,0.00017625367,0.00019370367,0.00017831226,0.0000097678685,0.000008391112,0.000006156811,0.0000015525877,0.0009143842],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9889682,0.006267342,0.00078456127,0.00063404866,0.002429418,0.0009163506],"domain_scores_gemma":[0.96932656,0.01963158,0.0074670194,0.0010805406,0.0019643737,0.000529895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008536393,0.00022160288,0.00040621203,0.0015890094,0.00609677,0.004973892,0.00069459766,0.0020472247,0.001537986],"category_scores_gemma":[0.029042952,0.00031017757,0.00022946054,0.0015274785,0.008883316,0.0030943246,0.00356522,0.0034709862,0.00019183457],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032830428,0.00063387916,0.28595635,0.00039762663,0.000111097055,0.014190819,0.36712244,0.0027028208,0.0020207942,0.24805664,0.0047122994,0.07376699],"study_design_scores_gemma":[0.000034357916,0.00032448137,0.40205762,0.0011318949,0.0001876302,0.007058409,0.45110384,0.008320452,0.0052836384,0.08479962,0.039518405,0.00017961104],"about_ca_topic_score_codex":0.00887146,"about_ca_topic_score_gemma":0.017138762,"teacher_disagreement_score":0.00887146,"about_ca_system_score_codex":0.0036974084,"about_ca_system_score_gemma":0.0023282599,"threshold_uncertainty_score":0.045145333},"labels":[],"label_agreement":null},{"id":"W4388102658","doi":"10.5267/j.ijdns.2023.10.021","title":"Potential cyberbullying detection in social media platforms based on a multi-task learning framework","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Identification (biology); Word embedding; Task (project management); Generalization; Artificial intelligence; Focus (optics); Word (group theory); Embedding; Social media; Natural language processing; Deep learning; Contrast (vision); Machine learning; World Wide Web; Linguistics","score_opus":0.031011497328394456,"score_gpt":0.30401353618262983,"score_spread":0.27300203885423535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388102658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36365506,0.00095482206,0.6295541,0.0013205585,0.00015394074,0.00032945274,0.00038614907,0.0011306701,0.002515301],"genre_scores_gemma":[0.9448579,0.00023229948,0.050847426,0.00021222005,0.00014387944,0.0002194678,0.00044102583,0.000034302197,0.0030114036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992095,0.00027366236,0.000047638685,0.000204544,0.00011023088,0.00015443118],"domain_scores_gemma":[0.9983557,0.0009233724,0.00017268503,0.00007212932,0.00035012153,0.00012601791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019239758,0.0011008059,0.0010534077,0.0014679646,0.0006203549,0.0009790331,0.0014396056,0.0012969021,0.0010988902],"category_scores_gemma":[0.0027465771,0.0003964637,0.0007679024,0.0007351944,0.00064088753,0.0011611931,0.0014066296,0.0015713113,0.00047482055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010132237,0.0019592762,0.017828397,0.00030899572,0.00026091738,0.0005875107,0.0004832872,0.57328796,0.012349654,0.004285914,0.0047199912,0.38291493],"study_design_scores_gemma":[0.0000039124716,0.00004431587,0.00053857494,0.0000027129342,0.000008599853,0.000009926654,0.000017433153,0.99825484,0.00044572068,0.0005861646,0.00008310811,0.0000046235878],"about_ca_topic_score_codex":0.007381748,"about_ca_topic_score_gemma":0.007476464,"teacher_disagreement_score":0.007381748,"about_ca_system_score_codex":0.00074346445,"about_ca_system_score_gemma":0.0009036461,"threshold_uncertainty_score":0.014677525},"labels":[],"label_agreement":null},{"id":"W4388527109","doi":"10.36948/ijfmr.2023.v05i05.8248","title":"Improvement of Accuracy for Hate Speech Detection Using Modified Feature Extraction","year":2023,"lang":"en","type":"article","venue":"International Journal For Multidisciplinary Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"tf–idf; Offensive; Computer science; Gradient boosting; Boosting (machine learning); Logistic regression; Artificial intelligence; Feature extraction; Identification (biology); Classifier (UML); The Internet; Natural language processing; Machine learning; Speech recognition; Term (time); Random forest; World Wide Web; Mathematics","score_opus":0.1410024468009056,"score_gpt":0.4661950302775791,"score_spread":0.32519258347667346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388527109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6434302,0.0042164186,0.31173843,0.0011230415,0.001060898,0.0003858556,0.0031762493,0.022973282,0.011895584],"genre_scores_gemma":[0.8936372,0.00060697115,0.09561634,0.00018048107,0.00013663905,0.00007339131,0.003644351,0.0002266866,0.005877905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736375,0.000654174,0.00016835977,0.0006921752,0.0007286969,0.0003928304],"domain_scores_gemma":[0.9947748,0.0022244952,0.00028914068,0.00091614836,0.001651442,0.00014395337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003404003,0.0014622186,0.0016390294,0.0020762298,0.00058656774,0.0013029862,0.001170005,0.0014416663,0.0025516504],"category_scores_gemma":[0.010472978,0.0002603646,0.0010732174,0.001021855,0.00030341392,0.0019735037,0.0011396935,0.0010918431,0.0059277723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012535381,0.00072217075,0.039750718,0.00031793673,0.00021794427,0.0003435574,0.00019261744,0.016532535,0.044702735,0.00056860363,0.015054813,0.88034284],"study_design_scores_gemma":[0.000060050417,0.00069290627,0.044643227,0.00008980091,0.00019422875,0.000984362,0.00039019325,0.8748993,0.06875751,0.0011300618,0.008055453,0.00010292809],"about_ca_topic_score_codex":0.006206093,"about_ca_topic_score_gemma":0.0052497624,"teacher_disagreement_score":0.006206093,"about_ca_system_score_codex":0.00043034618,"about_ca_system_score_gemma":0.0007228171,"threshold_uncertainty_score":0.018002331},"labels":[],"label_agreement":null},{"id":"W4389042773","doi":"10.2139/ssrn.4615473","title":"Victim-related Assumptions Underlying Plea-based Sentence Reductions: A Communicative and Experiential Framework","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Plea; Sentence; Experiential learning; Psychology; Linguistics; Cognitive psychology; Natural language processing; Computer science; Political science; Philosophy; Law; Mathematics education","score_opus":0.02218523758591843,"score_gpt":0.28828674034734764,"score_spread":0.2661015027614292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389042773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19438699,0.00033721537,0.49908555,0.012427574,0.00025368552,0.00060250825,0.00034395803,0.00061060884,0.29195192],"genre_scores_gemma":[0.9695255,0.00008199209,0.025714502,0.00040241508,0.00007234803,0.00022834432,0.0001241045,0.00009909034,0.0037517045],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99069303,0.0050033564,0.00048989116,0.0011450191,0.0019383862,0.0007303121],"domain_scores_gemma":[0.97756094,0.014838432,0.0014221099,0.0029018042,0.002676516,0.0006002082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008792243,0.000732414,0.00051237777,0.002124678,0.0026332263,0.0063239634,0.004502789,0.0044561764,0.012922716],"category_scores_gemma":[0.0360929,0.00097525516,0.0010447695,0.0005744047,0.013249876,0.014446236,0.0041764053,0.004697509,0.0015141612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011962361,0.00018022294,0.0012613047,0.00012501632,0.000027734455,0.0004002885,0.016873552,0.001705047,0.0036923261,0.96465856,0.00095943885,0.009996978],"study_design_scores_gemma":[0.00008428843,0.000214772,0.005162616,0.00013861673,0.00007283914,0.0012046348,0.013156105,0.0316256,0.0066084163,0.93182063,0.009789027,0.00012241202],"about_ca_topic_score_codex":0.0015408063,"about_ca_topic_score_gemma":0.0010627477,"teacher_disagreement_score":0.012922716,"about_ca_system_score_codex":0.0016289566,"about_ca_system_score_gemma":0.0019986855,"threshold_uncertainty_score":0.04649836},"labels":[],"label_agreement":null},{"id":"W4389125280","doi":"10.2139/ssrn.4624575","title":"Laughing to Keep From [User Input Undefined]: ChatGPT, Jewish Humor, and Cultural Erasure","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Judaism; Erasure; Psychology; Computer science; Internet privacy; History; Archaeology","score_opus":0.00928652410865471,"score_gpt":0.2331556940196623,"score_spread":0.22386916991100758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389125280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9671126,0.00042649175,0.0022214733,0.0012634743,0.000045984845,0.000026479023,0.00006324623,0.00007371872,0.028766485],"genre_scores_gemma":[0.9977055,0.00007185442,0.00047079052,0.00022850164,0.000014762963,0.00000711299,0.00002634972,0.000023692517,0.0014515342],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981805,0.00088768103,0.00006706785,0.00019139153,0.00044693332,0.00022638703],"domain_scores_gemma":[0.98537874,0.0084771635,0.00281587,0.0012062839,0.0012742574,0.0008476973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028336628,0.0005138201,0.0002836196,0.0013173198,0.0022433067,0.0039461716,0.00072944147,0.0014523144,0.0048484183],"category_scores_gemma":[0.027969887,0.0004014883,0.00019577004,0.0007569271,0.0038167431,0.0021764797,0.0024609214,0.0020400234,0.00060873205],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010330747,0.00056765083,0.6248268,0.00026200377,0.00015263553,0.0041812346,0.22703876,0.00048331812,0.008197687,0.020256456,0.0043527973,0.10864765],"study_design_scores_gemma":[0.00003543057,0.00039862527,0.76290375,0.00048525576,0.00029833696,0.009124666,0.18729417,0.007305887,0.00641158,0.010068124,0.01546546,0.00020873867],"about_ca_topic_score_codex":0.008445659,"about_ca_topic_score_gemma":0.015783044,"teacher_disagreement_score":0.008445659,"about_ca_system_score_codex":0.00091016013,"about_ca_system_score_gemma":0.0010171682,"threshold_uncertainty_score":0.016792953},"labels":[],"label_agreement":null},{"id":"W4389493777","doi":"10.31577/cai_2023_4_993","title":"Deep Learning Based Misogynistic Bangla Text Identification from Social Media","year":2023,"lang":"en","type":"article","venue":"Computing and Informatics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bengali; Hatred; Social media; Artificial intelligence; Computer science; Identification (biology); Deep learning; Confusion matrix; Hostility; Intimidation; Machine learning; Natural language processing; Psychology; World Wide Web; Social psychology; Political science","score_opus":0.013661582061727255,"score_gpt":0.23206206428146933,"score_spread":0.21840048221974206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389493777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9033876,0.0011892313,0.0732652,0.00093536277,0.00044438522,0.00025409056,0.005486326,0.002757099,0.012280821],"genre_scores_gemma":[0.9405025,0.00042665043,0.03613273,0.00018864128,0.00012192683,0.000115050476,0.008256039,0.00007667617,0.01417982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996275,0.00007889978,0.00003412692,0.00010967749,0.00006808706,0.00008174858],"domain_scores_gemma":[0.9992448,0.00032312292,0.00009142175,0.00006322841,0.00022817141,0.00004942941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003664435,0.0008743744,0.0003474859,0.0014513638,0.00054302276,0.00080874143,0.00048134313,0.0007135546,0.0018308695],"category_scores_gemma":[0.0013718134,0.00014987156,0.00041558847,0.0008131008,0.0003003952,0.0009769351,0.0007082816,0.0006600432,0.001805927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009892181,0.0006721296,0.04536572,0.0005622753,0.00015591142,0.0022300093,0.0015609271,0.024405776,0.06542461,0.0018117299,0.028089335,0.8287324],"study_design_scores_gemma":[0.000028176952,0.00030661328,0.043401774,0.000103613085,0.00011220439,0.0008484811,0.0028166461,0.8934308,0.042201996,0.0025331024,0.0141520435,0.0000645668],"about_ca_topic_score_codex":0.005269161,"about_ca_topic_score_gemma":0.011085478,"teacher_disagreement_score":0.005269161,"about_ca_system_score_codex":0.00053796225,"about_ca_system_score_gemma":0.00042414872,"threshold_uncertainty_score":0.010477006},"labels":[],"label_agreement":null},{"id":"W4389518337","doi":"10.18653/v1/2023.banglalp-1.28","title":"Semantics Squad at BLP-2023 Task 1: Violence Inciting Bangla Text Detection with Fine-Tuned Transformer-Based Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Transformer; Baseline (sea); Computer science; Macro; Task (project management); F1 score; Artificial intelligence; Natural language processing; Voltage; Programming language; Engineering","score_opus":0.01369728491858575,"score_gpt":0.21088242187992906,"score_spread":0.19718513696134332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389518337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68573225,0.005451686,0.18305106,0.0031035615,0.0035129744,0.0016257329,0.025724938,0.053506747,0.038291134],"genre_scores_gemma":[0.8786564,0.00046336936,0.069895096,0.0008366999,0.00027155367,0.00037565935,0.035317197,0.0009754043,0.013208526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972145,0.0009675463,0.00018281487,0.000969966,0.00040005182,0.0002651863],"domain_scores_gemma":[0.99647874,0.0015782339,0.00015355948,0.0008178027,0.0006312213,0.000340381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035192322,0.0034209397,0.0013982514,0.0016480915,0.0010746005,0.0021311024,0.0016702835,0.0022377167,0.0067983232],"category_scores_gemma":[0.00903562,0.00045217964,0.0018621811,0.00086694903,0.00062437746,0.0035111164,0.002913706,0.0028940386,0.008827711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038189162,0.0022439547,0.03493636,0.0021151411,0.0009054532,0.0014334135,0.0012524639,0.066242635,0.05837082,0.0028158328,0.11901362,0.7068514],"study_design_scores_gemma":[0.0002811177,0.001765617,0.022419099,0.00021846665,0.00039745634,0.0013698885,0.0016764237,0.8919632,0.03655167,0.0077284896,0.035414547,0.000214138],"about_ca_topic_score_codex":0.0102520315,"about_ca_topic_score_gemma":0.016510785,"teacher_disagreement_score":0.0102520315,"about_ca_system_score_codex":0.0012101774,"about_ca_system_score_gemma":0.0017691583,"threshold_uncertainty_score":0.022742689},"labels":[],"label_agreement":null},{"id":"W4389518407","doi":"10.18653/v1/2023.banglalp-1.9","title":"Vio-Lens: A Novel Dataset of Annotated Social Network Posts Leading to Different Forms of Communal Violence and its Evaluation","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bengali; Computer science; Artificial intelligence","score_opus":0.049725974081649686,"score_gpt":0.30801025010299005,"score_spread":0.25828427602134035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389518407","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2714337,0.0035133304,0.008426273,0.0011105436,0.0010339171,0.001130209,0.69136643,0.0043396875,0.017646011],"genre_scores_gemma":[0.16440304,0.0010817188,0.016884426,0.0003651867,0.00034400073,0.0011620317,0.8068038,0.00027058643,0.008685247],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989574,0.000260538,0.000117610325,0.0002779243,0.0002641603,0.00012236655],"domain_scores_gemma":[0.9981178,0.0005456787,0.00022042608,0.00035801108,0.00040790188,0.00035017598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006880665,0.0011250253,0.0005973775,0.004683823,0.000966043,0.0010279439,0.0008880418,0.0015587274,0.0035749513],"category_scores_gemma":[0.003299103,0.00022052918,0.00063565216,0.002543688,0.00035724757,0.0012808535,0.0019921085,0.0007180688,0.0051178867],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002716762,0.0015078742,0.17632268,0.006211822,0.00084250787,0.0040087053,0.0042074504,0.0038721943,0.031891074,0.0030448558,0.51591194,0.24946219],"study_design_scores_gemma":[0.0002751022,0.0009551222,0.48716092,0.0007712887,0.00034393286,0.004216882,0.008897766,0.027002757,0.011239075,0.0029872658,0.4558449,0.00030493463],"about_ca_topic_score_codex":0.011984101,"about_ca_topic_score_gemma":0.041664336,"teacher_disagreement_score":0.011984101,"about_ca_system_score_codex":0.0006297574,"about_ca_system_score_gemma":0.0007943878,"threshold_uncertainty_score":0.023828685},"labels":[],"label_agreement":null},{"id":"W4389518882","doi":"10.18653/v1/2023.findings-emnlp.730","title":"Legally Enforceable Hate Speech Detection for Public Forums","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Task (project management); Law enforcement; Computer science; Perspective (graphical); Enforcement; Set (abstract data type); Speech act; Free speech; Computer security; Internet privacy; Law; Political science; Artificial intelligence; Linguistics; Engineering","score_opus":0.029372632656293626,"score_gpt":0.24699148754473654,"score_spread":0.2176188548884429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389518882","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8828758,0.002302776,0.07393511,0.0024005652,0.0009204571,0.0006405867,0.014282993,0.0124542955,0.010187341],"genre_scores_gemma":[0.9117871,0.00025734215,0.05194007,0.00049523567,0.000190088,0.00030328124,0.029751807,0.00034865204,0.0049263947],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960751,0.0019828696,0.00018583164,0.0010599548,0.00043798194,0.00025822668],"domain_scores_gemma":[0.9884,0.0074405433,0.000761734,0.0015092995,0.0013146078,0.00057379546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034639898,0.0014786497,0.00077685283,0.0016174128,0.001405482,0.0016282256,0.0012161824,0.002276619,0.0031519097],"category_scores_gemma":[0.013835177,0.0003372393,0.0008881794,0.0005927518,0.00086646137,0.0033066517,0.0020250864,0.0029447637,0.00250235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033119896,0.003528805,0.10094271,0.003004186,0.00036635704,0.0023070145,0.012142236,0.04781519,0.066985376,0.007159852,0.15815844,0.59427786],"study_design_scores_gemma":[0.00023674476,0.0011504677,0.07372639,0.00035693467,0.00014987387,0.0017947701,0.008363237,0.7878338,0.04962705,0.0120647,0.06441166,0.00028431692],"about_ca_topic_score_codex":0.008234229,"about_ca_topic_score_gemma":0.015746117,"teacher_disagreement_score":0.008234229,"about_ca_system_score_codex":0.0014249675,"about_ca_system_score_gemma":0.0012251781,"threshold_uncertainty_score":0.018319488},"labels":[],"label_agreement":null},{"id":"W4389520181","doi":"10.18653/v1/2023.findings-emnlp.365","title":"HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Generalization; Voice activity detection; Generative grammar; Code (set theory); Natural language processing; Artificial intelligence; Language model; Social media; Quality (philosophy); Speech processing; World Wide Web; Epistemology","score_opus":0.008275823746758441,"score_gpt":0.20994694141360548,"score_spread":0.20167111766684703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389520181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044288225,0.0017175045,0.7669281,0.0016817474,0.00040218388,0.00090156804,0.016304145,0.16032262,0.0074539],"genre_scores_gemma":[0.34512457,0.00048392254,0.6102843,0.0010177149,0.00014379513,0.00058518123,0.03265735,0.002029685,0.0076733646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977842,0.0006970955,0.000113574686,0.0008150283,0.00043096315,0.00015920066],"domain_scores_gemma":[0.99490154,0.003191783,0.00027517907,0.0009482435,0.0005214283,0.00016191156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027061289,0.002786943,0.0008738284,0.0023631004,0.0008478364,0.0020900746,0.0032465174,0.002575439,0.007907149],"category_scores_gemma":[0.013904544,0.0007746477,0.0018114825,0.0008043982,0.0009975999,0.0033232933,0.003093454,0.003562521,0.0046861153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078367133,0.00096531765,0.018438581,0.0011601711,0.0005268096,0.0010403228,0.0011628357,0.070892625,0.014921188,0.01062131,0.12925357,0.7502336],"study_design_scores_gemma":[0.00009106638,0.00012258458,0.002941142,0.00008470651,0.00007600634,0.0002740876,0.00029072317,0.94453704,0.0129467165,0.020136153,0.018423568,0.00007618588],"about_ca_topic_score_codex":0.01130064,"about_ca_topic_score_gemma":0.026105048,"teacher_disagreement_score":0.01130064,"about_ca_system_score_codex":0.0011901141,"about_ca_system_score_gemma":0.0017918675,"threshold_uncertainty_score":0.026452065},"labels":[],"label_agreement":null},{"id":"W4389523803","doi":"10.18653/v1/2023.findings-emnlp.983","title":"GTA: Gated Toxicity Avoidance for LM Performance Preservation","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Offensive; Perplexity; Computer science; Consistency (knowledge bases); Artificial intelligence; Grammar; Generative grammar; Natural language processing; Machine learning; Language model; Operations research; Engineering; Linguistics","score_opus":0.026418551701207854,"score_gpt":0.24704164962242994,"score_spread":0.2206230979212221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389523803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07083689,0.0031801097,0.809918,0.001279743,0.0011892936,0.00051825127,0.0021157207,0.10256686,0.008395255],"genre_scores_gemma":[0.5266415,0.00069704803,0.4428727,0.0016354485,0.00044343466,0.0005691639,0.006942438,0.0065272455,0.013671096],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876213,0.00040089016,0.00010534521,0.00031888895,0.00029856252,0.00011410038],"domain_scores_gemma":[0.9974648,0.0010461074,0.00015287349,0.000840971,0.0003745579,0.00012068862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015149169,0.002171777,0.0009988782,0.0012789737,0.0008663701,0.0015115488,0.002518502,0.0018773145,0.011124378],"category_scores_gemma":[0.006957088,0.00050707796,0.0010398121,0.0009927214,0.0008212399,0.0017309792,0.002141338,0.002006841,0.0071188062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006578675,0.00018922352,0.001971397,0.0006030217,0.0001958567,0.00074399635,0.00029070425,0.052217297,0.11553886,0.0041443333,0.041700847,0.78174657],"study_design_scores_gemma":[0.0002833123,0.0005484291,0.0025084994,0.00007099917,0.00017445513,0.0009534032,0.0002046578,0.815977,0.13575198,0.01151488,0.03190439,0.00010796121],"about_ca_topic_score_codex":0.003068563,"about_ca_topic_score_gemma":0.0052561057,"teacher_disagreement_score":0.011124378,"about_ca_system_score_codex":0.0005754212,"about_ca_system_score_gemma":0.0016729063,"threshold_uncertainty_score":0.037214756},"labels":[],"label_agreement":null},{"id":"W4389524498","doi":"10.18653/v1/2023.emnlp-industry.26","title":"Unveiling Identity Biases in Toxicity Detection : A Game-Focused Dataset and Reactivity Analysis Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ubisoft (Canada)","funders":"Mitacs","keywords":"Computer science; Conversation; Identity (music); Harm; Class (philosophy); Benchmarking; Perspective (graphical); Artificial intelligence; Natural language processing; Machine learning; Psychology; Social psychology; Communication","score_opus":0.03807695971279029,"score_gpt":0.2792286478311847,"score_spread":0.24115168811839438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389524498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71893924,0.0021695604,0.08129027,0.0030860472,0.00074973673,0.0015042565,0.16003093,0.012925561,0.019304443],"genre_scores_gemma":[0.70925444,0.00037730968,0.06252516,0.0013721935,0.00017026869,0.0014178662,0.21599443,0.000821229,0.008067068],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99532497,0.0018998834,0.0004076634,0.0009956533,0.0010941804,0.00027756492],"domain_scores_gemma":[0.99060726,0.0045116376,0.00078212615,0.0018399688,0.001842761,0.00041623865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039796988,0.0013359128,0.0005587403,0.002902323,0.0012092153,0.0016115252,0.0014157224,0.0021288563,0.001709386],"category_scores_gemma":[0.014633055,0.00029265406,0.0010350164,0.001661999,0.0009905872,0.0023517613,0.002478302,0.002363912,0.0020213202],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024725564,0.0034164388,0.34896284,0.0027980884,0.00061581086,0.0020053585,0.0069688717,0.042717688,0.03765283,0.01864866,0.2892607,0.24448018],"study_design_scores_gemma":[0.00041074745,0.0015512642,0.23316462,0.00048975524,0.0003640538,0.0024475246,0.0066941185,0.41188568,0.053934813,0.025266286,0.26328734,0.0005038389],"about_ca_topic_score_codex":0.009504809,"about_ca_topic_score_gemma":0.020637143,"teacher_disagreement_score":0.009504809,"about_ca_system_score_codex":0.0013433076,"about_ca_system_score_gemma":0.0011113823,"threshold_uncertainty_score":0.021046877},"labels":[],"label_agreement":null},{"id":"W4390143048","doi":"10.1007/978-3-031-29616-1_18","title":"Techlash, Platformization and the Struggle to Govern Online Content","year":2023,"lang":"en","type":"book-chapter","venue":"Global transformations in media and communication/Global transformations in media and communication research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transparency (behavior); Political science; Compromise; Social media; Public relations; Civil society; Function (biology); Business; Law","score_opus":0.0822778980106377,"score_gpt":0.3267014073164016,"score_spread":0.24442350930576393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390143048","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025621241,0.002057025,0.011420562,0.008305198,0.00032730337,0.0000361544,0.000064105836,0.0001534189,0.95201504],"genre_scores_gemma":[0.75089973,0.0020316842,0.003088132,0.0019648015,0.00055122847,0.000118176016,0.00008831759,0.00045424272,0.24080373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99859077,0.00041498887,0.00005021066,0.00029009735,0.0004004489,0.00025344302],"domain_scores_gemma":[0.9978186,0.00092703116,0.0002028569,0.0005253685,0.00024375747,0.0002824841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001868263,0.00042296466,0.00019354801,0.0013664269,0.0037304352,0.014008039,0.0007886479,0.0022448325,0.012601557],"category_scores_gemma":[0.0049591097,0.00032177995,0.00025178047,0.0015232502,0.018645845,0.016483389,0.0046884934,0.0042626145,0.0023625055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061893265,0.0000069022108,0.00017970036,0.000011980771,0.0000013119478,0.00003376083,0.0019068825,0.00008903486,0.00019318696,0.9867949,0.0027770507,0.007999063],"study_design_scores_gemma":[0.000008657222,0.000025823616,0.0011730554,0.00012979562,0.0000059515587,0.00014623879,0.0032741928,0.00064080954,0.001285413,0.74391776,0.24937165,0.000020733578],"about_ca_topic_score_codex":0.0036997842,"about_ca_topic_score_gemma":0.004184133,"teacher_disagreement_score":0.014008039,"about_ca_system_score_codex":0.003363093,"about_ca_system_score_gemma":0.003547717,"threshold_uncertainty_score":0.0421564},"labels":[],"label_agreement":null},{"id":"W4390143988","doi":"10.17159/obiter.v44i4.17505","title":"PROVIDING A LEGAL DEFINITION FOR CYBERBULLYING IN SOUTH AFRICA","year":2023,"lang":"en","type":"article","venue":"Obiter","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Harm; CLARITY; Interpretation (philosophy); The Internet; Political science; Confusion; Phenomenon; Law enforcement; Enforcement; Public relations; Internet privacy; Criminology; Psychology; Law; Computer science; Epistemology","score_opus":0.04178065195820064,"score_gpt":0.24337168207147306,"score_spread":0.20159103011327242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390143988","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17967719,0.03231786,0.043428544,0.22262396,0.0035483986,0.0016250145,0.0010401187,0.00010117603,0.51563776],"genre_scores_gemma":[0.90438706,0.012382452,0.033219624,0.015430998,0.00045470247,0.0010026821,0.00039627796,0.000059960108,0.032666232],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954066,0.0018341294,0.001008942,0.00027939075,0.00074351736,0.0007273501],"domain_scores_gemma":[0.99341935,0.0028251286,0.0015149018,0.00036924978,0.0014434555,0.00042785308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004264987,0.000403495,0.00040424636,0.0032163847,0.005528856,0.005676466,0.0012508383,0.002877229,0.005948621],"category_scores_gemma":[0.010085101,0.0003622294,0.00037526523,0.0017867441,0.007249182,0.004984445,0.005523429,0.0037974091,0.0006511121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001656546,0.000060550497,0.0049339253,0.0005900742,0.000005348985,0.0027945489,0.05145882,0.00020322893,0.001992421,0.8964981,0.013518777,0.027927564],"study_design_scores_gemma":[0.000025914158,0.000056845376,0.019943798,0.006570635,0.00003217653,0.0055897343,0.09838397,0.0010429616,0.0027529253,0.12182187,0.7436611,0.00011804736],"about_ca_topic_score_codex":0.023513865,"about_ca_topic_score_gemma":0.022329278,"teacher_disagreement_score":0.023513865,"about_ca_system_score_codex":0.005581447,"about_ca_system_score_gemma":0.013023562,"threshold_uncertainty_score":0.046754003},"labels":[],"label_agreement":null},{"id":"W4390244963","doi":"10.18280/ria.370611","title":"Short Text Classification Based on Hybrid Semantic Expansion and Bidirectional GRU (BiGRU) based Method to Improve Hate Speech Detection","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Voice activity detection; Speech recognition; Artificial intelligence; Natural language processing; Speech processing","score_opus":0.03859458254636253,"score_gpt":0.2889214900949905,"score_spread":0.25032690754862796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390244963","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4960995,0.0049241246,0.45534503,0.0009892443,0.0011254038,0.00067743077,0.005097752,0.014231163,0.021510355],"genre_scores_gemma":[0.8659956,0.0009503505,0.107215844,0.00024518513,0.00039371764,0.0002654818,0.007039748,0.00023529309,0.017658764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937445,0.00009558694,0.000068958434,0.00018419947,0.00018444679,0.000092353606],"domain_scores_gemma":[0.99903536,0.00024047111,0.00013291111,0.00012873762,0.00039814875,0.00006440827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005288893,0.0009788959,0.0007220071,0.0032983723,0.00042551427,0.00063542556,0.00066006643,0.00059514947,0.0034545315],"category_scores_gemma":[0.0014333895,0.00012970167,0.0007400672,0.0014527666,0.0003822795,0.0016938129,0.00079951965,0.0006386586,0.002917583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005513501,0.00037826147,0.009636088,0.00030046765,0.0000901494,0.00042859063,0.0002601095,0.00953441,0.04938386,0.0022099987,0.013380245,0.9138465],"study_design_scores_gemma":[0.000044340428,0.0006520123,0.024569785,0.00009549961,0.0001583464,0.00074716524,0.0005044917,0.8962039,0.057334147,0.004781481,0.014816229,0.000092643524],"about_ca_topic_score_codex":0.0032768974,"about_ca_topic_score_gemma":0.0059400867,"teacher_disagreement_score":0.0034545315,"about_ca_system_score_codex":0.00045329807,"about_ca_system_score_gemma":0.0005938958,"threshold_uncertainty_score":0.011556566},"labels":[],"label_agreement":null},{"id":"W4390266861","doi":"10.1016/j.chb.2023.108123","title":"Towards comprehensive cyberbullying detection: A dataset incorporating aggressive texts, repetition, peerness, and intent to harm","year":2023,"lang":"en","type":"article","venue":"Computers in Human Behavior","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Harm; Repetition (rhetorical device); Computer science; Flexibility (engineering); Aggression; Social media; Psychology; Internet privacy; Social psychology; World Wide Web; Mathematics","score_opus":0.035641506001030825,"score_gpt":0.30207476656083937,"score_spread":0.26643326055980854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390266861","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31050795,0.0018492021,0.009911606,0.0015969101,0.0006762884,0.0012783939,0.66132736,0.003809621,0.009042699],"genre_scores_gemma":[0.107573025,0.00045835876,0.015468334,0.00048083207,0.0001792198,0.00091840327,0.86788785,0.00016024579,0.0068736677],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989095,0.00016575873,0.00010734704,0.00030825136,0.00033768665,0.00017149706],"domain_scores_gemma":[0.9973055,0.00044696868,0.00030465488,0.00047287162,0.0008710394,0.00059888704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008492347,0.0016273389,0.0009921243,0.0032135074,0.0012255476,0.0011320319,0.0017498212,0.0029214434,0.0033460923],"category_scores_gemma":[0.0032780827,0.00043685545,0.0014225412,0.001651052,0.00053055404,0.0011326175,0.0022925779,0.0018818957,0.0072156265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014292999,0.003606207,0.18327627,0.002192513,0.0007901022,0.0012942022,0.0015835587,0.005304837,0.030040063,0.0018685505,0.60694355,0.16167086],"study_design_scores_gemma":[0.00039642083,0.0009399927,0.6254514,0.00055891456,0.0006007961,0.0029095784,0.0037066517,0.034407835,0.016474197,0.002433303,0.31171462,0.00040625499],"about_ca_topic_score_codex":0.022240033,"about_ca_topic_score_gemma":0.06195585,"teacher_disagreement_score":0.022240033,"about_ca_system_score_codex":0.00091511884,"about_ca_system_score_gemma":0.0015614771,"threshold_uncertainty_score":0.044221163},"labels":[],"label_agreement":null},{"id":"W4390271731","doi":"10.18280/ria.370630","title":"A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Natural language processing","score_opus":0.0666959693140477,"score_gpt":0.2879931256328182,"score_spread":0.2212971563187705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390271731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1913192,0.0019850635,0.7863564,0.00076574826,0.00060943095,0.0004015298,0.0010229084,0.009102292,0.008437368],"genre_scores_gemma":[0.7246676,0.00070939807,0.25345767,0.0005844383,0.00016017488,0.00043721736,0.0021833691,0.00020007965,0.017600102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970824,0.000034553268,0.000021602478,0.00009086209,0.00006869576,0.000076065015],"domain_scores_gemma":[0.99981326,0.000042292053,0.000023917422,0.000016330046,0.0000900227,0.000014190958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004321548,0.0010401466,0.0007682227,0.0014167096,0.00050702435,0.0006891316,0.0013950818,0.0009602998,0.0022072638],"category_scores_gemma":[0.00070245354,0.00040511528,0.0006211749,0.001418495,0.00031017995,0.0011569859,0.0005679075,0.00075410306,0.0011307503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073820376,0.0005115141,0.0052412297,0.00027529575,0.00025848893,0.00029959116,0.00015014592,0.12554407,0.024847439,0.0022746539,0.008890326,0.830969],"study_design_scores_gemma":[0.000019285097,0.00011916274,0.0011475884,0.000021016169,0.000038352046,0.00007631209,0.000057630426,0.98732704,0.007941873,0.0009763934,0.002260684,0.000014692107],"about_ca_topic_score_codex":0.011851763,"about_ca_topic_score_gemma":0.017045956,"teacher_disagreement_score":0.011851763,"about_ca_system_score_codex":0.00077602046,"about_ca_system_score_gemma":0.0009933491,"threshold_uncertainty_score":0.02356553},"labels":[],"label_agreement":null},{"id":"W4390548110","doi":"10.1007/978-3-031-41237-0_16","title":"Far-Right Political Extremism and the Radicalisation of the Anti-vaccine Movement in Canada","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Disinformation; Politics; Social media; Political science; Appropriation; Realm; Political communication; Public relations; Media studies; Sociology; Internet privacy; Law; Computer science; Epistemology","score_opus":0.0056689301725368485,"score_gpt":0.1773214838295071,"score_spread":0.17165255365697024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390548110","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20309435,0.01588709,0.0009458698,0.11852259,0.0018296294,0.000076733544,0.0006091409,0.0000597267,0.6589749],"genre_scores_gemma":[0.840275,0.0058445013,0.00038199226,0.010622566,0.00017532383,0.000018783963,0.00012565225,0.0000657186,0.14249052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9963821,0.00024687423,0.00003348588,0.0001990679,0.0010168699,0.0021215954],"domain_scores_gemma":[0.99768686,0.00044846197,0.00014711956,0.00005124071,0.0007579891,0.00090840185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015076625,0.00028192438,0.000372047,0.0013472827,0.019839417,0.009140314,0.0011951978,0.002880959,0.008865098],"category_scores_gemma":[0.0039730356,0.00030482796,0.00035230105,0.0023218486,0.010249439,0.0013687224,0.002013471,0.0061968532,0.00040370165],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012420803,0.0000570597,0.010782107,0.00015992414,0.00004410014,0.0007507183,0.04589314,0.0010258813,0.0006633193,0.7440712,0.13467528,0.06175304],"study_design_scores_gemma":[0.000025579162,0.000029789842,0.058012456,0.00035473108,0.000040638093,0.00017549907,0.046975505,0.00085613097,0.00079655607,0.020601317,0.87201416,0.00011758416],"about_ca_topic_score_codex":0.99564916,"about_ca_topic_score_gemma":0.9985209,"teacher_disagreement_score":0.13915963,"about_ca_system_score_codex":0.13915963,"about_ca_system_score_gemma":0.18688592,"threshold_uncertainty_score":0.9984528},"labels":[],"label_agreement":null},{"id":"W4390661940","doi":"10.51685/jqd.2024.003","title":"Proud Boys on Telegram","year":2024,"lang":"en","type":"article","venue":"Journal of Quantitative Description Digital Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Moonshot Research and Development Program; George Washington University; John S. and James L. Knight Foundation","keywords":"Misinformation; Social media; Opposition (politics); Social connectedness; Media studies; Exploratory analysis; Feminism; Political science; Sociology; Criminology; Law; Psychology; Social psychology; Computer science","score_opus":0.04361547790318788,"score_gpt":0.28029670626826225,"score_spread":0.23668122836507438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390661940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97967863,0.000125377,0.0003498337,0.00026664374,0.000029391838,0.00005693297,0.00820369,0.000034301378,0.011255107],"genre_scores_gemma":[0.9857013,0.00022970117,0.0007463367,0.00012052808,0.000067907575,0.00019935859,0.0065692547,0.00003416037,0.006331345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99886876,0.00027690068,0.00006156992,0.00017704548,0.00035782185,0.00025787496],"domain_scores_gemma":[0.99393916,0.0019162038,0.002106311,0.00045504433,0.0008231035,0.000760086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006056149,0.00028534376,0.00027540565,0.0027097433,0.0011102288,0.001815662,0.00044923023,0.00047861197,0.008746183],"category_scores_gemma":[0.004347675,0.0001783218,0.00018519202,0.0031651228,0.00061764667,0.0017512678,0.0019155242,0.0007395698,0.0019028634],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031702267,0.00013975715,0.8964818,0.000274698,0.000048667007,0.00062486104,0.03450261,0.00026957708,0.0016273789,0.0034352266,0.018984957,0.043293405],"study_design_scores_gemma":[0.000008178999,0.000074718235,0.9302917,0.00008307044,0.000016930351,0.00035990673,0.034144424,0.0005102779,0.00059092644,0.0003184804,0.033577412,0.000023925677],"about_ca_topic_score_codex":0.0065938933,"about_ca_topic_score_gemma":0.011816138,"teacher_disagreement_score":0.008746183,"about_ca_system_score_codex":0.0005848316,"about_ca_system_score_gemma":0.0003987987,"threshold_uncertainty_score":0.029258847},"labels":[],"label_agreement":null},{"id":"W4390722673","doi":"10.48550/arxiv.2401.02974","title":"Efficacy of Utilizing Large Language Models to Detect Public Threat Posted Online","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centennial College","funders":"California State University Long Beach","keywords":"Moderation; Transparency (behavior); Identification (biology); Scale (ratio); Computer security; Internet privacy; Advertising; Computer science; Psychology; Business; Social psychology; Geography","score_opus":0.1278831013225399,"score_gpt":0.22676681601446239,"score_spread":0.09888371469192248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390722673","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8222587,0.0011573033,0.15200993,0.0034984252,0.00031916346,0.00092533394,0.0022428,0.0054666107,0.012121746],"genre_scores_gemma":[0.9453281,0.00018492901,0.051012963,0.00038544208,0.000118716314,0.000225247,0.0013987406,0.00012215988,0.0012236971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9779545,0.01750032,0.0006731495,0.0019553315,0.001549144,0.00036760108],"domain_scores_gemma":[0.8196026,0.16434783,0.0048040627,0.0059178052,0.00398191,0.0013458757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030137764,0.0020438256,0.0011181607,0.0031959703,0.00086557143,0.003247295,0.001164708,0.0014844362,0.0019961095],"category_scores_gemma":[0.114009246,0.0006237859,0.0012167861,0.0010016761,0.00083037495,0.0059620156,0.0023704737,0.0023591062,0.0020182503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050133425,0.003457427,0.29902568,0.0010062617,0.0017991186,0.0003806804,0.0039907005,0.08543344,0.012186152,0.0039778044,0.0121701,0.57155925],"study_design_scores_gemma":[0.00017891692,0.001445206,0.03148351,0.00015670208,0.00054305163,0.0002513078,0.0015211477,0.9418034,0.00926084,0.008911688,0.0042492114,0.00019503936],"about_ca_topic_score_codex":0.0040244884,"about_ca_topic_score_gemma":0.0060734996,"teacher_disagreement_score":0.030137764,"about_ca_system_score_codex":0.00093805796,"about_ca_system_score_gemma":0.0015006441,"threshold_uncertainty_score":0.15938562},"labels":[],"label_agreement":null},{"id":"W4390840431","doi":"10.29173/cjfy30026","title":"A Discourse of Hate: A Content Analysis of Responses to Queer Representation in Online Social Media","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Family and Youth / Le Journal Canadien de Famille et de la Jeunesse","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Queer; Ignorance; Lesbian; Transgender; Sexual orientation; Gender studies; Theme (computing); Representation (politics); Sociology; Rhetoric; Critical discourse analysis; Sexual identity; Public sphere; Identity (music); Social media; Heterosexism; Homosexuality; Queer theory; Human sexuality; Political science; Aesthetics; Law; Art; Politics; Linguistics","score_opus":0.039102198326054285,"score_gpt":0.3019829754625674,"score_spread":0.26288077713651314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390840431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99126804,0.00023419369,0.0011124571,0.0012491486,0.00005241149,0.00008164109,0.00016583745,0.00002193793,0.005814254],"genre_scores_gemma":[0.9954867,0.00046030915,0.0007826938,0.00044130927,0.00006951061,0.00012698573,0.00015880863,0.000036588524,0.002436981],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9935759,0.0046801185,0.00022880665,0.0002837044,0.00079021865,0.00044122376],"domain_scores_gemma":[0.96460617,0.02889457,0.0029816285,0.00060517946,0.002188495,0.00072405156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057301894,0.0005129427,0.00040050055,0.0043544755,0.00510847,0.0033924116,0.00060133706,0.0011945143,0.0018095865],"category_scores_gemma":[0.024794878,0.0002962329,0.00021732155,0.0027043303,0.004773593,0.003654601,0.0037315022,0.0013744681,0.00023898836],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051874096,0.000021777028,0.00851905,0.000122044235,0.0000058704154,0.0003284753,0.9814167,0.00002024603,0.002169569,0.00082160736,0.00044204172,0.0060807997],"study_design_scores_gemma":[0.000002690964,0.000032111933,0.013591339,0.00014617814,0.000006623159,0.00021241103,0.97508913,0.00018119114,0.00087968324,0.00020186881,0.009640571,0.000016173277],"about_ca_topic_score_codex":0.006037676,"about_ca_topic_score_gemma":0.0077202683,"teacher_disagreement_score":0.006037676,"about_ca_system_score_codex":0.00251361,"about_ca_system_score_gemma":0.0013686552,"threshold_uncertainty_score":0.030304551},"labels":[],"label_agreement":null},{"id":"W4390952812","doi":"10.31235/osf.io/qfaj3","title":"How October 7th, 2023, changed fear and exposure to hate amongst Jewish and Israeli members of universities","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Employment and Social Development Canada","funders":"","keywords":"Antisemitism; Judaism; Respondent; Psychology; Social psychology; Demography; Political science; History; Sociology; Law","score_opus":0.011645800266004343,"score_gpt":0.21529298863663257,"score_spread":0.20364718837062823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390952812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99859804,0.000033226162,0.00001684244,0.00030716535,0.000022339113,0.000005403658,0.000030167024,0.0000013968179,0.000985339],"genre_scores_gemma":[0.9988501,0.000079995756,0.000022517019,0.0002603531,0.000028092025,0.000010334004,0.00005392781,0.0000013763557,0.0006932613],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992453,0.00029495533,0.000030786414,0.000059470633,0.00013679282,0.00023286759],"domain_scores_gemma":[0.99645007,0.0006143108,0.0011354875,0.00018528671,0.0004297147,0.0011850408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020103727,0.00022072902,0.00023683094,0.0005195475,0.002697442,0.0030075253,0.00036802085,0.0015262484,0.0017591404],"category_scores_gemma":[0.0049931724,0.0003057113,0.0002565332,0.00042551936,0.0015350239,0.000939293,0.0011306448,0.0015846096,0.0005636874],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049107097,0.00053192605,0.8988908,0.00004315788,0.000043245305,0.0005837741,0.082305916,0.00007816954,0.002879255,0.00021506786,0.0022034673,0.011734248],"study_design_scores_gemma":[0.000003998504,0.00025329145,0.9264749,0.000022012548,0.000008450783,0.00011455108,0.071199216,0.00004324393,0.0002744593,0.000033981774,0.001553873,0.000017990275],"about_ca_topic_score_codex":0.033918306,"about_ca_topic_score_gemma":0.090120986,"teacher_disagreement_score":0.033918306,"about_ca_system_score_codex":0.0013972184,"about_ca_system_score_gemma":0.00082402816,"threshold_uncertainty_score":0.06744176},"labels":[],"label_agreement":null},{"id":"W4391020680","doi":"10.1109/mts.2023.3340235","title":"Technological Solutions to Online Toxicity: Potential and Pitfalls","year":2023,"lang":"en","type":"article","venue":"IEEE Technology and Society Magazine","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Misinformation; Disinformation; Context (archaeology); Social media; Computer science; Process (computing); Internet privacy; Data science; Risk analysis (engineering); Computer security; Business; World Wide Web","score_opus":0.014332573194151432,"score_gpt":0.241260016478174,"score_spread":0.22692744328402256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391020680","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010986944,0.29915443,0.05144037,0.58005977,0.0062012533,0.0002466635,0.00030936926,0.0009413835,0.050659716],"genre_scores_gemma":[0.19110033,0.51824886,0.13696004,0.11206287,0.012696815,0.0009150025,0.00052322354,0.00056180015,0.026931006],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98302335,0.008056447,0.00092220586,0.0013286959,0.0058668857,0.0008024307],"domain_scores_gemma":[0.92873186,0.043951746,0.004064846,0.0047721965,0.016033536,0.0024458277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025540695,0.0014293949,0.0011256702,0.005072783,0.0036724566,0.009456301,0.004367627,0.008332456,0.015007203],"category_scores_gemma":[0.048470195,0.00070197607,0.001716828,0.0032237372,0.010479213,0.022061357,0.0075318348,0.009897638,0.0067346855],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010774519,0.00031302145,0.004788703,0.0052641053,0.00013162209,0.00067089027,0.00303112,0.0013172965,0.0015939646,0.12190758,0.06728372,0.7935903],"study_design_scores_gemma":[0.00003651366,0.0003944641,0.0039081774,0.010257893,0.00015900959,0.002265177,0.01566731,0.0030527455,0.003609568,0.25428003,0.706192,0.00017720374],"about_ca_topic_score_codex":0.0018226433,"about_ca_topic_score_gemma":0.0041280733,"teacher_disagreement_score":0.025540695,"about_ca_system_score_codex":0.0024989115,"about_ca_system_score_gemma":0.0053196885,"threshold_uncertainty_score":0.13507372},"labels":[],"label_agreement":null},{"id":"W4391164676","doi":"10.2139/ssrn.4701228","title":"Proposing Amendments Against Cyberbullying in Sri Lanka: Recommendations from United Kingdom, Canada and Fiji.","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sri lanka; Kingdom; Political science; Geography; Socioeconomics; Sociology; Environmental planning; Tanzania; Biology","score_opus":0.011052713967516541,"score_gpt":0.2379009683416379,"score_spread":0.22684825437412137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391164676","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050397865,0.025805762,0.005256581,0.7073295,0.028453302,0.005021216,0.0044118585,0.0015255259,0.17179845],"genre_scores_gemma":[0.20157565,0.012457463,0.02180353,0.63578254,0.0040074745,0.003521446,0.0024967466,0.00030682713,0.11804829],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.95854163,0.009955284,0.0048713023,0.002156993,0.010812451,0.013662323],"domain_scores_gemma":[0.8709218,0.046482187,0.007580841,0.0059677055,0.05204609,0.017001376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039376903,0.0017856482,0.0014148079,0.008236503,0.010587913,0.012559453,0.009948571,0.049116064,0.017470177],"category_scores_gemma":[0.118252,0.0016300404,0.002870835,0.004346344,0.009490123,0.0071496367,0.006966718,0.020666424,0.0035465239],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052051834,0.00087057054,0.02041446,0.0033648978,0.0002930859,0.0028599612,0.010590201,0.0012429124,0.0045694294,0.0683005,0.8013227,0.08565074],"study_design_scores_gemma":[0.00086819613,0.0006912285,0.07600484,0.007615106,0.0013097356,0.0007028963,0.021337444,0.00083350285,0.0039417497,0.009301537,0.8766482,0.00074555166],"about_ca_topic_score_codex":0.5924312,"about_ca_topic_score_gemma":0.72640127,"teacher_disagreement_score":0.4075688,"about_ca_system_score_codex":0.013306016,"about_ca_system_score_gemma":0.13857412,"threshold_uncertainty_score":0.8199381},"labels":[],"label_agreement":null},{"id":"W4391539955","doi":"10.5430/wjel.v14n2p358","title":"COVID-19 and Advice Speech Act: A Syntactic-Pragmatic Study","year":2024,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Advice (programming); Speech act; Coronavirus disease 2019 (COVID-19); Computer science; Linguistics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Indirect speech; Natural language processing; Virology; Medicine; Philosophy; Programming language","score_opus":0.008461763084251566,"score_gpt":0.27255495145125547,"score_spread":0.2640931883670039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391539955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96614856,0.00040166485,0.0027227022,0.0011268902,0.000052115218,0.00038233525,0.00021210019,0.00003153559,0.028922202],"genre_scores_gemma":[0.9956474,0.00014251613,0.001484064,0.0003608935,0.000038813876,0.00043060366,0.00023923499,0.00005625852,0.001600359],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98688775,0.0077091297,0.0009994461,0.001191983,0.0024695802,0.0007420896],"domain_scores_gemma":[0.91772485,0.06754551,0.006129623,0.0019393992,0.0051481947,0.0015124381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011205561,0.00091916387,0.0011773317,0.0018435417,0.0036086591,0.006149328,0.0012409313,0.00208729,0.01064134],"category_scores_gemma":[0.06079096,0.0016387127,0.0008848209,0.001403165,0.0059861294,0.00876374,0.0039551463,0.0052551087,0.0013892555],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010254052,0.0024124067,0.08469353,0.0014914408,0.00013594216,0.0023816503,0.8549549,0.00032881869,0.005027156,0.026438868,0.002089418,0.019020487],"study_design_scores_gemma":[0.0009649099,0.0016806115,0.3386091,0.0010505209,0.00049879996,0.0056110187,0.5896215,0.009346879,0.002654696,0.026129272,0.023308944,0.0005238183],"about_ca_topic_score_codex":0.011608163,"about_ca_topic_score_gemma":0.008641468,"teacher_disagreement_score":0.011608163,"about_ca_system_score_codex":0.0043620155,"about_ca_system_score_gemma":0.003605504,"threshold_uncertainty_score":0.05926144},"labels":[],"label_agreement":null},{"id":"W4391594738","doi":"10.32920/25176287.v1","title":"“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; Centre for Social Innovation; McMaster University; York University","funders":"York University","keywords":"Influencer marketing; Intermediary; Enforcement; Compliance (psychology); Public relations; Outreach; Scholarship; Business; Law enforcement; Social media; Marketing; Political science; Law; Relationship marketing; Marketing management; Psychology","score_opus":0.03186351317316094,"score_gpt":0.2666527222907835,"score_spread":0.23478920911762258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391594738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8884148,0.0020572855,0.0015047488,0.033472206,0.00016879932,0.00024238028,0.00010899023,0.000048350656,0.07398251],"genre_scores_gemma":[0.9908903,0.0007036559,0.0003845093,0.0025421495,0.00002765825,0.0000270343,0.000040527055,0.000020855754,0.0053632446],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97260183,0.0065635373,0.00061860983,0.0024642083,0.0118691,0.0058827787],"domain_scores_gemma":[0.9602915,0.016353296,0.0060112756,0.0013977534,0.009069229,0.0068769986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02316325,0.00041588984,0.0006069848,0.003966358,0.04365008,0.012545352,0.0035560152,0.003372156,0.0044391337],"category_scores_gemma":[0.04082513,0.00071325304,0.00040794435,0.0044774525,0.032238435,0.0041945586,0.007355502,0.0055919453,0.000254051],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064963446,0.00020683021,0.06514989,0.00018787294,0.000030223488,0.0010569385,0.8400469,0.00013111204,0.00078043505,0.038703088,0.0099968305,0.043644927],"study_design_scores_gemma":[0.000023717394,0.000078416495,0.12108621,0.00046205748,0.000041050585,0.00020910488,0.77647346,0.0008913422,0.0006033012,0.0029977125,0.096978076,0.00015560887],"about_ca_topic_score_codex":0.9741911,"about_ca_topic_score_gemma":0.9803498,"teacher_disagreement_score":0.121077284,"about_ca_system_score_codex":0.121077284,"about_ca_system_score_gemma":0.19398746,"threshold_uncertainty_score":0.87848127},"labels":[],"label_agreement":null},{"id":"W4391594745","doi":"10.32920/25176287","title":"“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; Centre for Social Innovation; McMaster University; York University","funders":"York University","keywords":"Influencer marketing; Intermediary; Enforcement; Outreach; Compliance (psychology); Scholarship; Public relations; Business; Law enforcement; Social media; Marketing; Competition (biology); Political science; Law; Relationship marketing; Marketing management; Psychology","score_opus":0.03186351317316094,"score_gpt":0.2666527222907835,"score_spread":0.23478920911762258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391594745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8884148,0.0020572855,0.0015047488,0.033472206,0.00016879932,0.00024238028,0.00010899023,0.000048350656,0.07398251],"genre_scores_gemma":[0.9908903,0.0007036559,0.0003845093,0.0025421495,0.00002765825,0.0000270343,0.000040527055,0.000020855754,0.0053632446],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97260183,0.0065635373,0.00061860983,0.0024642083,0.0118691,0.0058827787],"domain_scores_gemma":[0.9602915,0.016353296,0.0060112756,0.0013977534,0.009069229,0.0068769986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02316325,0.00041588984,0.0006069848,0.003966358,0.04365008,0.012545352,0.0035560152,0.003372156,0.0044391337],"category_scores_gemma":[0.04082513,0.00071325304,0.00040794435,0.0044774525,0.032238435,0.0041945586,0.007355502,0.0055919453,0.000254051],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064963446,0.00020683021,0.06514989,0.00018787294,0.000030223488,0.0010569385,0.8400469,0.00013111204,0.00078043505,0.038703088,0.0099968305,0.043644927],"study_design_scores_gemma":[0.000023717394,0.000078416495,0.12108621,0.00046205748,0.000041050585,0.00020910488,0.77647346,0.0008913422,0.0006033012,0.0029977125,0.096978076,0.00015560887],"about_ca_topic_score_codex":0.9741911,"about_ca_topic_score_gemma":0.9803498,"teacher_disagreement_score":0.121077284,"about_ca_system_score_codex":0.121077284,"about_ca_system_score_gemma":0.19398746,"threshold_uncertainty_score":0.87848127},"labels":[],"label_agreement":null},{"id":"W4391737492","doi":"10.53771/ijstra.2024.6.1.0111","title":"Cyberbullying of women on Twitter","year":2024,"lang":"en","type":"article","venue":"International Journal of Science and Technology Research Archive","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Child and Family Research Institute","funders":"","keywords":"Harassment; The Internet; Psychology; Verbal abuse; Social psychology; Internet privacy; Computer science; Human factors and ergonomics; Poison control; World Wide Web; Medicine","score_opus":0.024926903528972564,"score_gpt":0.3427822901308036,"score_spread":0.317855386601831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391737492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947856,0.00013989658,0.000064775886,0.00041085668,0.000031186697,0.000021433058,0.00015976295,0.000005642441,0.004380844],"genre_scores_gemma":[0.996229,0.0003122029,0.000048241505,0.00022421262,0.00004561494,0.000035606965,0.00009767764,0.000004915319,0.0030024278],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996853,0.00010060288,0.000019423907,0.000040323957,0.000056383236,0.00009797868],"domain_scores_gemma":[0.99904734,0.00023242945,0.00036323405,0.00006291226,0.00011622908,0.00017782819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003391315,0.0002691592,0.00022972828,0.00067950605,0.0013577292,0.0012030338,0.00017847405,0.00036858665,0.007555271],"category_scores_gemma":[0.002793771,0.00017517942,0.00017202793,0.0004964704,0.00051317253,0.001286578,0.0012230375,0.00042262432,0.0012610868],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023712858,0.00021965243,0.8879299,0.00009955073,0.00003192215,0.00084370375,0.05373582,0.000029992574,0.0024032283,0.0004230343,0.0026513615,0.051394816],"study_design_scores_gemma":[0.000008584884,0.00027949613,0.87000054,0.00012637179,0.000034161905,0.0008691325,0.1181805,0.00016818882,0.0006612579,0.0003394693,0.009312013,0.000020264384],"about_ca_topic_score_codex":0.0039002355,"about_ca_topic_score_gemma":0.0074731247,"teacher_disagreement_score":0.007555271,"about_ca_system_score_codex":0.00031130778,"about_ca_system_score_gemma":0.00029968863,"threshold_uncertainty_score":0.025274932},"labels":[],"label_agreement":null},{"id":"W4391913134","doi":"10.1002/bsl.2648","title":"From Reddit to manifestos: Forensic evaluation of incel online activity","year":2024,"lang":"en","type":"article","venue":"Behavioral Sciences & the Law","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Context (archaeology); Social media; Collateral; Internet privacy; Interpretation (philosophy); Forensic science; World Wide Web; Computer science; Empirical research; Medicine; Political science; History","score_opus":0.09750501899434923,"score_gpt":0.37236932954316165,"score_spread":0.27486431054881244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391913134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6572964,0.011730051,0.08680243,0.014684812,0.0019157734,0.0022833794,0.0017757624,0.0012181089,0.22229324],"genre_scores_gemma":[0.94002265,0.005200814,0.03194052,0.0016455702,0.0003065336,0.00036925238,0.00064407755,0.00023746499,0.019633146],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98723954,0.007120173,0.00075430697,0.00046114367,0.004033112,0.0003917608],"domain_scores_gemma":[0.9445768,0.026874313,0.007547092,0.0054243663,0.0140768355,0.0015006415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017819447,0.00047948913,0.00036187284,0.0078107505,0.0022451913,0.0047411704,0.0011770944,0.0015014268,0.0047608493],"category_scores_gemma":[0.07846377,0.00026925828,0.00024210307,0.0025333816,0.0027823814,0.0063340184,0.0037177345,0.0018205339,0.0018615732],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006366961,0.00053417723,0.057226896,0.0021532292,0.00008524873,0.0064917123,0.11901342,0.0009171874,0.010633941,0.05085269,0.042690963,0.70876384],"study_design_scores_gemma":[0.00012592215,0.001725888,0.13243686,0.012318038,0.00020883267,0.021937247,0.289808,0.014260813,0.0454354,0.07035249,0.41090217,0.0004883342],"about_ca_topic_score_codex":0.0013185361,"about_ca_topic_score_gemma":0.0036066961,"teacher_disagreement_score":0.017819447,"about_ca_system_score_codex":0.0016207634,"about_ca_system_score_gemma":0.0018890566,"threshold_uncertainty_score":0.094239354},"labels":[],"label_agreement":null},{"id":"W4392000216","doi":"10.4236/jcc.2024.122006","title":"Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit","year":2024,"lang":"en","type":"article","venue":"Journal of Computer and Communications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Social media; Coronavirus disease 2019 (COVID-19); Sentiment analysis; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Microblogging; Advertising; Internet privacy; Media studies; Computer science; Political science; Virology; World Wide Web; Sociology; Business; Medicine; Artificial intelligence","score_opus":0.06871244268204685,"score_gpt":0.3187487962503896,"score_spread":0.25003635356834275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392000216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98776513,0.00014809424,0.0020714712,0.000543926,0.00011886539,0.00009258049,0.0028028449,0.0001090767,0.0063480083],"genre_scores_gemma":[0.99008244,0.00016058808,0.0033696247,0.00021198194,0.00018233007,0.000098844066,0.0028226573,0.000042177122,0.0030294433],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988287,0.00042133377,0.00008086199,0.000112227,0.00044431872,0.00011256185],"domain_scores_gemma":[0.9914091,0.0039556567,0.0015259528,0.00024650752,0.0025902682,0.00027248607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016948236,0.00033173626,0.00023166962,0.001825043,0.00049047946,0.00076628296,0.0001674888,0.0002904645,0.0010570664],"category_scores_gemma":[0.007537311,0.00007334578,0.00016619418,0.0009809536,0.00020985847,0.0009829679,0.0005719643,0.00047748417,0.00064790656],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001720813,0.0004137654,0.65582067,0.0010630506,0.00016945874,0.0012251668,0.015841553,0.0025521962,0.06367201,0.0017505425,0.027933978,0.22783682],"study_design_scores_gemma":[0.000023385886,0.0005472627,0.87617975,0.00018594966,0.0001136112,0.00051386433,0.020182159,0.047308534,0.01816627,0.0008685523,0.03580793,0.0001027033],"about_ca_topic_score_codex":0.0017669449,"about_ca_topic_score_gemma":0.0034660394,"teacher_disagreement_score":0.001825043,"about_ca_system_score_codex":0.00043378404,"about_ca_system_score_gemma":0.00026463292,"threshold_uncertainty_score":0.008963227},"labels":[],"label_agreement":null},{"id":"W4392121144","doi":"10.1145/3641510","title":"Combating Islamophobia: Compromise, Community, and Harmony in Mitigating Harmful Online Content","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Social Computing","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Waterloo; University of Toronto","funders":"","keywords":"Islamophobia; Moderation; Faith; Harmony (color); Compromise; Public relations; Sociology; Scholarship; Political science; Social psychology; Social science; Law; Psychology; Epistemology","score_opus":0.084079500959639,"score_gpt":0.3032594720689645,"score_spread":0.21917997110932547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392121144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8811182,0.00081389473,0.06295401,0.0065956675,0.00022515054,0.0011622858,0.00008775665,0.0010760176,0.04596697],"genre_scores_gemma":[0.95961887,0.00038922793,0.036252804,0.000631389,0.00006819228,0.00045202326,0.000045606692,0.00008140653,0.0024605629],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99426395,0.004196067,0.00019625819,0.00025191205,0.0007624782,0.00032927925],"domain_scores_gemma":[0.97900724,0.015375468,0.002187268,0.0014846319,0.0010726475,0.0008726244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006027257,0.00066274195,0.00023033952,0.0011633914,0.0024756193,0.003546591,0.0006748945,0.0008117778,0.0029250376],"category_scores_gemma":[0.028059518,0.00025360932,0.00028563093,0.00056311575,0.0019749666,0.00414483,0.0043374924,0.0012287052,0.0005120936],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004867194,0.0012368388,0.073526435,0.0025056915,0.00012374729,0.00088342483,0.2838887,0.00088904944,0.039287835,0.015659364,0.0053043524,0.57620776],"study_design_scores_gemma":[0.00023086544,0.0038361996,0.18718866,0.004053809,0.00066808326,0.0022396678,0.4379332,0.015438307,0.046783466,0.043313373,0.25795317,0.00036122094],"about_ca_topic_score_codex":0.00061298505,"about_ca_topic_score_gemma":0.0018744968,"teacher_disagreement_score":0.006027257,"about_ca_system_score_codex":0.00057044014,"about_ca_system_score_gemma":0.0012950136,"threshold_uncertainty_score":0.03187555},"labels":[],"label_agreement":null},{"id":"W4392663948","doi":"10.31234/osf.io/gswm6","title":"Accuracy prompts protect professional content moderators from the illusory truth effect","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; John Templeton Foundation","keywords":"Content (measure theory); Psychology; Cognitive psychology; Social psychology; Mathematics","score_opus":0.030132730207182032,"score_gpt":0.26618568469577203,"score_spread":0.23605295448858998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392663948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97627103,0.0001814315,0.0075487,0.0009918554,0.00013820377,0.0003855326,0.00012965918,0.00018028762,0.014173318],"genre_scores_gemma":[0.99031025,0.00008530365,0.005956484,0.00054924097,0.0000754766,0.00043279908,0.00007330291,0.000056796744,0.002460478],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9924712,0.004284107,0.00044910095,0.0010342323,0.0013254242,0.00043585701],"domain_scores_gemma":[0.8575272,0.1040644,0.015711341,0.014042133,0.005609934,0.003045062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011106354,0.0005515529,0.00043298546,0.00057094335,0.0013391434,0.0015676514,0.0006514253,0.0013799125,0.012255466],"category_scores_gemma":[0.08319608,0.00053544994,0.00037099211,0.0002830372,0.0017227655,0.001702528,0.0019553998,0.0017677913,0.0010770613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01821549,0.016640598,0.31368235,0.0041329884,0.00064952986,0.00078606,0.080680184,0.001675332,0.17121837,0.02279599,0.008285445,0.36123767],"study_design_scores_gemma":[0.0022612198,0.01499738,0.7760331,0.0010978393,0.0013458123,0.0006327611,0.011652379,0.0058347583,0.11242227,0.023685975,0.04982391,0.00021253602],"about_ca_topic_score_codex":0.0009859528,"about_ca_topic_score_gemma":0.001253335,"teacher_disagreement_score":0.012255466,"about_ca_system_score_codex":0.00067486166,"about_ca_system_score_gemma":0.0010838221,"threshold_uncertainty_score":0.05873674},"labels":[],"label_agreement":null},{"id":"W4392669787","doi":"10.18653/v1/2023.eval4nlp-1.9","title":"Can a Prediction’s Rank Offer a More Accurate Quantification of Bias? A Case Study Measuring Sexism in Debiased Language Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Debiasing; Computer science; Sentence; Rank (graph theory); Statistics; Benchmark (surveying); Artificial intelligence; Natural language processing; Econometrics; Psychology; Mathematics; Social psychology","score_opus":0.10039459710483373,"score_gpt":0.2963982998557292,"score_spread":0.19600370275089546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392669787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88726395,0.0008330991,0.10440208,0.0011583082,0.00012715843,0.00007516155,0.00071234384,0.001387218,0.004040624],"genre_scores_gemma":[0.9738202,0.000090127556,0.02481107,0.00012508855,0.00003624084,0.000021878199,0.00039146002,0.00011412166,0.0005897995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951521,0.0021961266,0.00024293223,0.00081180927,0.0012939599,0.0003031248],"domain_scores_gemma":[0.9694336,0.021309061,0.0019788505,0.004124464,0.002595624,0.00055838405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00906174,0.0010389354,0.00077872205,0.0011002067,0.00053020724,0.0015660812,0.0007424916,0.001149945,0.0016658668],"category_scores_gemma":[0.043288726,0.00028379,0.00041422804,0.0005963871,0.0013844148,0.00231881,0.0012638416,0.0016155874,0.0008300305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036454939,0.0007000208,0.26681095,0.00066826725,0.0006550968,0.0010875994,0.0025291292,0.19105572,0.06122814,0.012236716,0.009626399,0.44975638],"study_design_scores_gemma":[0.000091560825,0.001572423,0.06536925,0.00013992669,0.00019595759,0.0008909776,0.001120696,0.84202564,0.05946199,0.021657055,0.0073114596,0.00016308198],"about_ca_topic_score_codex":0.00277772,"about_ca_topic_score_gemma":0.0036479204,"teacher_disagreement_score":0.00906174,"about_ca_system_score_codex":0.0006312946,"about_ca_system_score_gemma":0.0008896624,"threshold_uncertainty_score":0.047923625},"labels":[],"label_agreement":null},{"id":"W4392693097","doi":"10.1007/978-981-97-1274-8_21","title":"Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Social media; Computer science; Artificial intelligence; Information retrieval; World Wide Web","score_opus":0.06479327528948592,"score_gpt":0.3013920897076237,"score_spread":0.2365988144181378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392693097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4098076,0.00227789,0.533329,0.00272536,0.0002143355,0.000431238,0.0036591874,0.0043556946,0.043199666],"genre_scores_gemma":[0.7122129,0.0013095145,0.26835403,0.00021874899,0.0001197278,0.00022259526,0.0040715835,0.00024477168,0.013246129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996555,0.0001049854,0.000019288136,0.000076812044,0.00011633755,0.00002710415],"domain_scores_gemma":[0.9981614,0.0015330439,0.000118379125,0.00008947407,0.000078279205,0.000019419394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005035983,0.00065287563,0.00025307626,0.001727878,0.00034266926,0.0013913143,0.0005645187,0.0007621886,0.004747182],"category_scores_gemma":[0.003249974,0.00027235007,0.0006339261,0.00095622696,0.0005316493,0.0014856332,0.0005991194,0.0008487748,0.0011419267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019485483,0.00048460322,0.06091242,0.0008196508,0.00023263584,0.0009890153,0.002860422,0.043993637,0.05447116,0.044292387,0.015074062,0.7756752],"study_design_scores_gemma":[0.00002425934,0.00021182724,0.07992083,0.00023130426,0.00015141843,0.00074242253,0.0014571097,0.7863657,0.03810614,0.061100207,0.03160589,0.00008293296],"about_ca_topic_score_codex":0.0022392992,"about_ca_topic_score_gemma":0.0037778732,"teacher_disagreement_score":0.004747182,"about_ca_system_score_codex":0.00054741197,"about_ca_system_score_gemma":0.0003892308,"threshold_uncertainty_score":0.015880883},"labels":[],"label_agreement":null},{"id":"W4392729266","doi":"10.1109/tcss.2024.3359010","title":"User-Centric Modeling of Online Hate Through the Lens of Psycholinguistic Patterns and Behaviors in Social Media","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Social media; Computer science; Lens (geology); Human–computer interaction; Internet privacy; Computer security; World Wide Web; Physics; Optics","score_opus":0.034758364329108705,"score_gpt":0.29505509735135327,"score_spread":0.26029673302224454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392729266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.928711,0.000275348,0.06492637,0.0005647808,0.000023666242,0.0000887741,0.0009034898,0.00018468915,0.004321838],"genre_scores_gemma":[0.9943316,0.000061003047,0.0047366237,0.000020313077,0.00001065527,0.00002453854,0.0002432937,0.000010177574,0.00056175044],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961567,0.00018131865,0.000017379163,0.00009576378,0.000039976552,0.000049893693],"domain_scores_gemma":[0.998109,0.0011159105,0.00029076263,0.00018498731,0.00016625352,0.00013313656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081647583,0.0003780709,0.00023851692,0.0014391657,0.0003947777,0.001248626,0.00035304812,0.00048785712,0.00093357393],"category_scores_gemma":[0.0029213615,0.00022481642,0.00051766186,0.0005678599,0.000549415,0.0013235663,0.00064337003,0.00068188104,0.0002777067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005399782,0.00095467054,0.7487057,0.0002451783,0.00033040487,0.0006661237,0.009644122,0.110619,0.01932062,0.024801904,0.0031909507,0.08098131],"study_design_scores_gemma":[0.000005416235,0.00009287408,0.15222982,0.00001860719,0.00003086274,0.00013891021,0.0009814025,0.8375198,0.00089101866,0.006645373,0.0014235852,0.000022194645],"about_ca_topic_score_codex":0.018637158,"about_ca_topic_score_gemma":0.016855871,"teacher_disagreement_score":0.018637158,"about_ca_system_score_codex":0.00083461695,"about_ca_system_score_gemma":0.0005361312,"threshold_uncertainty_score":0.03705734},"labels":[],"label_agreement":null},{"id":"W4392947462","doi":"10.1063/5.0198822","title":"Transformer-based models for hate speech classification","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Transformer; Speech recognition; Artificial intelligence; Natural language processing; Engineering; Electrical engineering","score_opus":0.04710967688376341,"score_gpt":0.2685192096191227,"score_spread":0.22140953273535927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392947462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08936094,0.0014074037,0.8992104,0.00095294794,0.00020096869,0.00013447464,0.0007305168,0.0017952502,0.006207069],"genre_scores_gemma":[0.92096776,0.0007858668,0.06759823,0.00031092114,0.00015471551,0.000097493015,0.0012729713,0.00011958658,0.008692587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943835,0.00019166322,0.000032343818,0.000138686,0.00012738419,0.00007164179],"domain_scores_gemma":[0.99808735,0.0011025835,0.00016157197,0.00018545864,0.0003979151,0.000065166896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001810872,0.00094158866,0.0007098534,0.0014402104,0.00036638777,0.0010360314,0.0011783638,0.000740736,0.0026271085],"category_scores_gemma":[0.005320695,0.00030577893,0.00084751286,0.00085943314,0.0005667798,0.002074385,0.000954182,0.0018968829,0.0018892284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007670466,0.0003789886,0.010078653,0.00021298489,0.00019202416,0.00028581367,0.0004697753,0.39929888,0.013972072,0.044413388,0.010405095,0.5195253],"study_design_scores_gemma":[0.000004401463,0.00003234258,0.0004756904,0.000007452562,0.000013643011,0.000044932225,0.000021909518,0.99021274,0.0011407825,0.007456746,0.000580159,0.000009167287],"about_ca_topic_score_codex":0.0047008945,"about_ca_topic_score_gemma":0.006535004,"teacher_disagreement_score":0.0047008945,"about_ca_system_score_codex":0.001099131,"about_ca_system_score_gemma":0.00065866107,"threshold_uncertainty_score":0.009576976},"labels":[],"label_agreement":null},{"id":"W4393110041","doi":"10.1016/j.telpol.2024.102735","title":"Agents of platform governance: Analyzing U.S. civil society's role in contesting online content moderation","year":2024,"lang":"en","type":"article","venue":"Telecommunications Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canadian Political Science Association","keywords":"Moderation; Civil society; Corporate governance; Political science; Content (measure theory); Business; Public administration; Law; Social psychology; Psychology; Politics","score_opus":0.05214897827768067,"score_gpt":0.306239917304204,"score_spread":0.25409093902652335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393110041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9713405,0.00028051177,0.0015811168,0.004223253,0.000028511635,0.000040103456,0.000046280227,0.000015314847,0.022444496],"genre_scores_gemma":[0.9982299,0.00012943431,0.00031585142,0.00025075252,0.000007895649,0.000015195434,0.000028453398,0.0000091784195,0.0010133565],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99590164,0.002871126,0.00007885136,0.00028285274,0.0003238284,0.00054167444],"domain_scores_gemma":[0.98810613,0.007906627,0.0018124193,0.00054924045,0.0007329599,0.0008924771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006736552,0.00021099306,0.00020832835,0.0014678381,0.0064497893,0.006234576,0.0006090716,0.0013442251,0.0027100553],"category_scores_gemma":[0.011664664,0.00026208334,0.00012904008,0.0014447173,0.00574227,0.0051255524,0.0049753245,0.0021871242,0.00031402515],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009418218,0.00015290572,0.08791759,0.00006182912,0.000012177524,0.00044944513,0.84735894,0.0002384879,0.0014112697,0.032058105,0.0030087482,0.027236333],"study_design_scores_gemma":[0.000013603495,0.000076006676,0.03953092,0.0001378038,0.000010942804,0.00010274731,0.8801052,0.001469776,0.00078618305,0.007773272,0.06996176,0.000031865533],"about_ca_topic_score_codex":0.020677043,"about_ca_topic_score_gemma":0.03840289,"teacher_disagreement_score":0.020677043,"about_ca_system_score_codex":0.004351211,"about_ca_system_score_gemma":0.0041846656,"threshold_uncertainty_score":0.041113377},"labels":[],"label_agreement":null},{"id":"W4393146386","doi":"10.1609/aaai.v38i20.30213","title":"Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Transformer; Modal; Computer science; Natural language processing; Speech recognition; Artificial intelligence; Engineering; Electrical engineering; Voltage; Materials science","score_opus":0.04765902806975645,"score_gpt":0.2932541412982369,"score_spread":0.24559511322848046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393146386","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5928792,0.00272271,0.34042472,0.0014049262,0.00050059636,0.0010828938,0.017459188,0.017441722,0.026084175],"genre_scores_gemma":[0.87422293,0.00049489114,0.102245435,0.00023702114,0.00023799695,0.00034861558,0.012252214,0.00045284038,0.009508061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914765,0.00025663644,0.000038217004,0.00026846962,0.00019270039,0.00009624307],"domain_scores_gemma":[0.9967399,0.001377108,0.00045885457,0.0004565638,0.00073396286,0.0002335249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011525655,0.001043468,0.000324361,0.00410879,0.0007780367,0.0011870876,0.0008928057,0.0011241758,0.0029191915],"category_scores_gemma":[0.005382252,0.00028100878,0.00063738803,0.0013143746,0.0006333904,0.0037222886,0.0019663966,0.0012127102,0.0022075325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020597323,0.00090167986,0.06285453,0.001882833,0.00035380034,0.0015319613,0.008922371,0.011340981,0.1700194,0.010133182,0.054566532,0.6754331],"study_design_scores_gemma":[0.00012218999,0.0009774555,0.10923816,0.0002538552,0.00032713506,0.0028666176,0.010230902,0.6004119,0.15137857,0.026263643,0.09761502,0.0003145991],"about_ca_topic_score_codex":0.0032027496,"about_ca_topic_score_gemma":0.0073544923,"teacher_disagreement_score":0.00410879,"about_ca_system_score_codex":0.00069285656,"about_ca_system_score_gemma":0.00048900454,"threshold_uncertainty_score":0.009765685},"labels":[],"label_agreement":null},{"id":"W4393312564","doi":"10.1007/978-981-99-8937-9_55","title":"Identifying Hidden Factors for Verbal Harassment Comments on Social Media","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Harassment; Psychology; Social psychology; Sociology","score_opus":0.042130375079093674,"score_gpt":0.2675489458060492,"score_spread":0.22541857072695554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393312564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9703249,0.0004947639,0.02426458,0.0003498785,0.00011695367,0.00007542223,0.0018235818,0.0002404565,0.0023094472],"genre_scores_gemma":[0.99316543,0.00016455147,0.0030677551,0.000017375382,0.000115864445,0.00003591378,0.001676889,0.000026330768,0.0017299079],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985707,0.00046185672,0.00008085375,0.00028368033,0.00033003665,0.0002728286],"domain_scores_gemma":[0.97905,0.016711826,0.0019327962,0.00083288783,0.0009809213,0.0004915871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014685183,0.0008590623,0.00048989424,0.0027901093,0.0007831391,0.0014671687,0.00060699054,0.00091547286,0.0034594135],"category_scores_gemma":[0.01053924,0.00043994503,0.000990785,0.0016085345,0.0005601432,0.0014688498,0.0008887935,0.0015561456,0.0015238379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018473966,0.0010619248,0.7524831,0.0002450745,0.0006031296,0.0011888277,0.0019267909,0.008421324,0.02484488,0.0038556587,0.006065412,0.19745637],"study_design_scores_gemma":[0.000027953174,0.0003926004,0.66805726,0.00012618935,0.00034683992,0.0008965586,0.0022948931,0.31507605,0.007024202,0.0036488476,0.0020093136,0.00009936836],"about_ca_topic_score_codex":0.008698185,"about_ca_topic_score_gemma":0.012120233,"teacher_disagreement_score":0.008698185,"about_ca_system_score_codex":0.0006129923,"about_ca_system_score_gemma":0.0005665902,"threshold_uncertainty_score":0.017295122},"labels":[],"label_agreement":null},{"id":"W4393439813","doi":"10.5281/zenodo.6020475","title":"Worldwide Gender Differences in Public Code Contributions - Replication Package","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Replication (statistics); R package; Code (set theory); Computer science; Programming language; Political science; Biology; Virology","score_opus":0.05505426056193453,"score_gpt":0.2650785159953777,"score_spread":0.21002425543344316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393439813","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0483181,0.00042953494,0.06652352,0.007544593,0.00327788,0.003499684,0.59287393,0.064027935,0.21350475],"genre_scores_gemma":[0.21468657,0.00053619914,0.091014184,0.0043509696,0.00075825973,0.023193147,0.39392346,0.07809992,0.1934373],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99136835,0.002373569,0.0006038547,0.001099701,0.0036899103,0.0008645865],"domain_scores_gemma":[0.94399524,0.016645946,0.0038266126,0.015801504,0.017343657,0.0023869788],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009800368,0.00094658585,0.0008074917,0.0032655755,0.0013999263,0.0025261333,0.0017973661,0.0009528143,0.2804744],"category_scores_gemma":[0.07305797,0.00091056863,0.0013715266,0.0041654115,0.00071626686,0.0037235352,0.0053859213,0.0029920195,0.13977748],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035553979,0.00014685273,0.011204698,0.00028464265,0.00006537304,0.00006743572,0.000997886,0.0004820142,0.00052949967,0.0066338177,0.9069573,0.07227483],"study_design_scores_gemma":[0.00025419943,0.00021560173,0.11323065,0.000526107,0.00007133303,0.00022587327,0.0016146661,0.0020985338,0.002991245,0.010962952,0.8676145,0.00019417753],"about_ca_topic_score_codex":0.007871566,"about_ca_topic_score_gemma":0.008562159,"teacher_disagreement_score":0.9901996,"about_ca_system_score_codex":0.0013140329,"about_ca_system_score_gemma":0.0032398738,"threshold_uncertainty_score":0.93828034},"labels":[],"label_agreement":null},{"id":"W4393617534","doi":"10.5281/zenodo.5732299","title":"Bash in the Wild: Language Usage, Code Smells, and Bugs - Dataset","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code (set theory); Computer science; Programming language; Code smell; Software; Software quality; Set (abstract data type)","score_opus":0.024043598752681593,"score_gpt":0.2531799358686012,"score_spread":0.22913633711591963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393617534","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005914772,0.00021636729,0.00027312757,0.00017752915,0.000107731816,0.000055385874,0.9911225,0.00091153034,0.0012209419],"genre_scores_gemma":[0.0027351186,0.000037326623,0.00041188218,0.00005857022,0.000015761752,0.0000851312,0.9958962,0.000047147892,0.0007128519],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987208,0.00018987774,0.00012295139,0.0003294476,0.000407036,0.00022977815],"domain_scores_gemma":[0.9984072,0.00027835838,0.00016400087,0.0003890898,0.000467171,0.00029427683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008077156,0.002559613,0.0013973602,0.0025095816,0.00091291673,0.001277141,0.0022781813,0.0024702637,0.010331935],"category_scores_gemma":[0.0026870065,0.00048767787,0.0012532849,0.002651706,0.0005415285,0.0009084174,0.0017103862,0.0018596345,0.022399332],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033420822,0.0002188059,0.00628071,0.0005449341,0.00008343103,0.00013225914,0.00007896289,0.0006014494,0.0008270869,0.00031917845,0.9823888,0.008190179],"study_design_scores_gemma":[0.0009826659,0.00034950464,0.08798432,0.00044411398,0.0001976755,0.0009711739,0.0007000256,0.007546954,0.0046479497,0.0020276285,0.89391875,0.00022928814],"about_ca_topic_score_codex":0.025403887,"about_ca_topic_score_gemma":0.056130968,"teacher_disagreement_score":0.025403887,"about_ca_system_score_codex":0.0009690107,"about_ca_system_score_gemma":0.0014763514,"threshold_uncertainty_score":0.050512016},"labels":[],"label_agreement":null},{"id":"W4393638055","doi":"10.5281/zenodo.5732298","title":"Bash in the Wild: Language Usage, Code Smells, and Bugs - Dataset","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code (set theory); Computer science; Programming language; Code smell; Software; Software quality","score_opus":0.024043598752681593,"score_gpt":0.2531799358686012,"score_spread":0.22913633711591963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393638055","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005914772,0.00021636729,0.00027312757,0.00017752915,0.000107731816,0.000055385874,0.9911225,0.00091153034,0.0012209419],"genre_scores_gemma":[0.0027351186,0.000037326623,0.00041188218,0.00005857022,0.000015761752,0.0000851312,0.9958962,0.000047147892,0.0007128519],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987208,0.00018987774,0.00012295139,0.0003294476,0.000407036,0.00022977815],"domain_scores_gemma":[0.9984072,0.00027835838,0.00016400087,0.0003890898,0.000467171,0.00029427683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008077156,0.002559613,0.0013973602,0.0025095816,0.00091291673,0.001277141,0.0022781813,0.0024702637,0.010331935],"category_scores_gemma":[0.0026870065,0.00048767787,0.0012532849,0.002651706,0.0005415285,0.0009084174,0.0017103862,0.0018596345,0.022399332],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033420822,0.0002188059,0.00628071,0.0005449341,0.00008343103,0.00013225914,0.00007896289,0.0006014494,0.0008270869,0.00031917845,0.9823888,0.008190179],"study_design_scores_gemma":[0.0009826659,0.00034950464,0.08798432,0.00044411398,0.0001976755,0.0009711739,0.0007000256,0.007546954,0.0046479497,0.0020276285,0.89391875,0.00022928814],"about_ca_topic_score_codex":0.025403887,"about_ca_topic_score_gemma":0.056130968,"teacher_disagreement_score":0.025403887,"about_ca_system_score_codex":0.0009690107,"about_ca_system_score_gemma":0.0014763514,"threshold_uncertainty_score":0.050512016},"labels":[],"label_agreement":null},{"id":"W4393909283","doi":"10.5281/zenodo.6020474","title":"Worldwide Gender Differences in Public Code Contributions - Replication Package","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Replication (statistics); R package; Code (set theory); Computer science; Psychology; Political science; Programming language; Biology; Virology","score_opus":0.05505426056193453,"score_gpt":0.2650785159953777,"score_spread":0.21002425543344316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393909283","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0483181,0.00042953494,0.06652352,0.007544593,0.00327788,0.003499684,0.59287393,0.064027935,0.21350475],"genre_scores_gemma":[0.21468657,0.00053619914,0.091014184,0.0043509696,0.00075825973,0.023193147,0.39392346,0.07809992,0.1934373],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99136835,0.002373569,0.0006038547,0.001099701,0.0036899103,0.0008645865],"domain_scores_gemma":[0.94399524,0.016645946,0.0038266126,0.015801504,0.017343657,0.0023869788],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009800368,0.00094658585,0.0008074917,0.0032655755,0.0013999263,0.0025261333,0.0017973661,0.0009528143,0.2804744],"category_scores_gemma":[0.07305797,0.00091056863,0.0013715266,0.0041654115,0.00071626686,0.0037235352,0.0053859213,0.0029920195,0.13977748],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035553979,0.00014685273,0.011204698,0.00028464265,0.00006537304,0.00006743572,0.000997886,0.0004820142,0.00052949967,0.0066338177,0.9069573,0.07227483],"study_design_scores_gemma":[0.00025419943,0.00021560173,0.11323065,0.000526107,0.00007133303,0.00022587327,0.0016146661,0.0020985338,0.002991245,0.010962952,0.8676145,0.00019417753],"about_ca_topic_score_codex":0.007871566,"about_ca_topic_score_gemma":0.008562159,"teacher_disagreement_score":0.9901996,"about_ca_system_score_codex":0.0013140329,"about_ca_system_score_gemma":0.0032398738,"threshold_uncertainty_score":0.93828034},"labels":[],"label_agreement":null},{"id":"W4394486122","doi":"10.6084/m9.figshare.20632605","title":"Social Media Protest Conspiracy","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Media studies; Political science; Sociology; Law","score_opus":0.046871492977748945,"score_gpt":0.26894714530248565,"score_spread":0.2220756523247367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394486122","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013561071,0.000025826983,0.000054898148,0.000074750744,0.000032385833,0.00005741481,0.99755496,0.00017314359,0.00067040685],"genre_scores_gemma":[0.0018963333,0.000018161281,0.0002034812,0.000029647576,0.000011459661,0.00022859097,0.9966023,0.000027515582,0.0009825168],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986375,0.00022998126,0.00018750665,0.00033107764,0.00039606143,0.00021789312],"domain_scores_gemma":[0.99545836,0.0010892386,0.00056775374,0.0012410193,0.0012428326,0.0004009086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014114516,0.0015172411,0.0009439065,0.0032740638,0.0011866773,0.0016399161,0.0021775702,0.0021923785,0.032754354],"category_scores_gemma":[0.007266258,0.0005323056,0.0010454531,0.0044789133,0.00057404407,0.00080657844,0.0015319576,0.0016348002,0.03384131],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019012898,0.00013573904,0.006525275,0.0006136754,0.000037915244,0.00007550953,0.000118706426,0.00043009248,0.00022165522,0.00044157857,0.9874258,0.0037840146],"study_design_scores_gemma":[0.0009278185,0.00018498999,0.08927316,0.0005659608,0.000097711556,0.0002636057,0.000890643,0.002245187,0.0014505131,0.001448066,0.90253025,0.00012211813],"about_ca_topic_score_codex":0.045001287,"about_ca_topic_score_gemma":0.090503685,"teacher_disagreement_score":0.045001287,"about_ca_system_score_codex":0.0012737608,"about_ca_system_score_gemma":0.0020544874,"threshold_uncertainty_score":0.10957426},"labels":[],"label_agreement":null},{"id":"W4394931697","doi":"10.55041/ijsrem31109","title":"AUTOMATED HATE SPEECH DETECTION USING RECURRENT NEURAL NETWORK","year":2024,"lang":"en","type":"article","venue":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Computer science; Contempt; Artificial intelligence; Variety (cybernetics); Machine learning; Artificial neural network; Convolutional neural network; Psychology","score_opus":0.040851249037068264,"score_gpt":0.32601613914999766,"score_spread":0.2851648901129294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394931697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7897291,0.001703788,0.18867168,0.0008217785,0.0004579639,0.00013768258,0.002053701,0.0050773844,0.01134692],"genre_scores_gemma":[0.9762007,0.00023147854,0.018151414,0.00007611885,0.00004645218,0.000033925975,0.0012274567,0.000026387279,0.004006095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996542,0.00006443765,0.000017202285,0.000100255726,0.000085142674,0.00007892995],"domain_scores_gemma":[0.99947006,0.00014441987,0.00008620143,0.00005302265,0.00020668712,0.000039598755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000489386,0.0006675921,0.00037666698,0.00086442946,0.0002457757,0.00045341408,0.00049872266,0.0004817597,0.001267195],"category_scores_gemma":[0.0013069877,0.00015438575,0.00033575337,0.00033562983,0.00018472307,0.00047707534,0.00054542295,0.000628506,0.00076825416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006824359,0.00053439307,0.042389154,0.00022212518,0.00025271293,0.00073598354,0.00042406374,0.042702023,0.05486481,0.001190216,0.01713322,0.83886886],"study_design_scores_gemma":[0.00001167003,0.00012611953,0.020407327,0.000025543128,0.000052397,0.00015606348,0.00018979625,0.9572123,0.019016169,0.00077958294,0.0019942215,0.00002875185],"about_ca_topic_score_codex":0.006092383,"about_ca_topic_score_gemma":0.00762756,"teacher_disagreement_score":0.006092383,"about_ca_system_score_codex":0.0005085449,"about_ca_system_score_gemma":0.0003930394,"threshold_uncertainty_score":0.01211381},"labels":[],"label_agreement":null},{"id":"W4394969999","doi":"10.22214/ijraset.2024.60284","title":"An Enhanced System to Detect Cyberbullying and Automate Reporting on Twitter Using Text Based Pattern Recognition Technique","year":2024,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition","score_opus":0.0630225182618272,"score_gpt":0.38126984011369475,"score_spread":0.31824732185186755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394969999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2800065,0.00039268882,0.6200887,0.0010645868,0.0006586669,0.0016900914,0.00373788,0.08286908,0.009491871],"genre_scores_gemma":[0.6094623,0.00017170675,0.3711793,0.00067885814,0.00027660138,0.0007440619,0.0027721473,0.00030346404,0.014411587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994185,0.000077601224,0.000071597,0.00018522808,0.00019407683,0.00005305593],"domain_scores_gemma":[0.9984706,0.0003591841,0.00016142482,0.00022486674,0.000703073,0.00008085135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006400241,0.00056028174,0.00067589985,0.0014565255,0.00038429207,0.0006846129,0.0009065605,0.0008241138,0.005665608],"category_scores_gemma":[0.0016752715,0.000257972,0.0002988757,0.00051685475,0.00016802146,0.00095795543,0.0005543565,0.0004894457,0.003106175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014867589,0.001334871,0.026879089,0.0004588064,0.00020015593,0.0009084051,0.00028343336,0.0030138122,0.34550968,0.0011054645,0.024114972,0.5947045],"study_design_scores_gemma":[0.00032134168,0.0015478238,0.038218312,0.000090661146,0.00028710786,0.0015534202,0.00015909903,0.58574396,0.34185123,0.0011778136,0.028892098,0.00015714324],"about_ca_topic_score_codex":0.001294405,"about_ca_topic_score_gemma":0.0014058555,"teacher_disagreement_score":0.005665608,"about_ca_system_score_codex":0.00027657763,"about_ca_system_score_gemma":0.00039680282,"threshold_uncertainty_score":0.018953323},"labels":[],"label_agreement":null},{"id":"W4395031009","doi":"10.1002/poi3.387","title":"How harassment and hate speech policies have changed over time: Comparing Facebook, Twitter and Reddit (2005–2020)","year":2024,"lang":"en","type":"article","venue":"Policy & Internet","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Ottawa","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Harassment; Notice; Social media; Internet privacy; Public relations; Moderation; Sociology; Political science; Computer science; Media studies; Advertising; World Wide Web; Business; Psychology; Law; Social psychology","score_opus":0.021936620218937043,"score_gpt":0.27207352914784516,"score_spread":0.2501369089289081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395031009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99188375,0.00029817084,0.00009289509,0.0012332853,0.000044546054,0.000017255396,0.0011784089,0.000007669665,0.0052438495],"genre_scores_gemma":[0.9971112,0.00019705779,0.000043285872,0.00015785113,0.00002571036,0.000024570883,0.00094567856,0.000006056265,0.0014887092],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99831605,0.00048461594,0.00016372277,0.00020104433,0.00036029384,0.0004743099],"domain_scores_gemma":[0.9869826,0.0028388829,0.0064724647,0.0003467627,0.0021577156,0.0012015676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024240757,0.000158803,0.00028115913,0.0026593758,0.0014698432,0.0052523375,0.00076770125,0.0010367591,0.0034828137],"category_scores_gemma":[0.011495193,0.00025386747,0.0003941679,0.003419152,0.0013043551,0.0034376516,0.0025018146,0.0017957867,0.0007565857],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033334555,0.0003019118,0.9403315,0.00013711142,0.00011188414,0.00025655344,0.026520502,0.00058760674,0.00023612572,0.0032903901,0.006090172,0.021802863],"study_design_scores_gemma":[0.0000043649993,0.000076436954,0.94151336,0.00008167785,0.00001994365,0.000035484165,0.051945962,0.00040535204,0.00018389245,0.00016052056,0.005552741,0.000020359068],"about_ca_topic_score_codex":0.091034055,"about_ca_topic_score_gemma":0.096631795,"teacher_disagreement_score":0.091034055,"about_ca_system_score_codex":0.0036047588,"about_ca_system_score_gemma":0.0014415255,"threshold_uncertainty_score":0.18100834},"labels":[],"label_agreement":null},{"id":"W4395956088","doi":"10.29173/cjfy30069","title":"Break the Hate: A Survey of Youth Experiences with Hate and Violent Extremism Online","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Family and Youth / Le Journal Canadien de Famille et de la Jeunesse","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Violent extremism; Hate crime; Love and hate; Criminology; Psychology; Political science; Psychoanalysis; Terrorism; Law","score_opus":0.018290522675517135,"score_gpt":0.2385728048539996,"score_spread":0.22028228217848245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395956088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998979,0.00008232373,0.00002435321,0.000055066346,0.000002642592,0.00002123815,0.00039995863,0.0000032062455,0.00043234418],"genre_scores_gemma":[0.99824727,0.0003407873,0.00010541549,0.00008928981,0.000004229467,0.000025498704,0.0004907524,0.0000031837305,0.0006935737],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995542,0.0000709772,0.0000446989,0.000034241646,0.00013016554,0.00016575567],"domain_scores_gemma":[0.99854505,0.000117231946,0.0004317185,0.000034311957,0.00037979241,0.0004918208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084825896,0.00020812069,0.00026333873,0.001253332,0.0013144516,0.0011131575,0.00037526738,0.0003881821,0.0011422602],"category_scores_gemma":[0.0015392973,0.00028244793,0.00027226363,0.0013015994,0.00045253453,0.00060701946,0.00078045414,0.0006607097,0.00029278605],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022668397,0.000065392196,0.9778504,0.000031102918,0.000009872348,0.00014470803,0.016294295,0.000012707333,0.00021956376,0.000032260534,0.0005538055,0.0047631664],"study_design_scores_gemma":[0.0000013999069,0.00006925668,0.9582083,0.000028408276,0.000008310091,0.00014117439,0.04057655,0.000042862303,0.000060003345,0.000009013279,0.00084860687,0.0000061298033],"about_ca_topic_score_codex":0.37700558,"about_ca_topic_score_gemma":0.5632856,"teacher_disagreement_score":0.37700558,"about_ca_system_score_codex":0.002154974,"about_ca_system_score_gemma":0.0016769336,"threshold_uncertainty_score":0.74962234},"labels":[],"label_agreement":null},{"id":"W4396223314","doi":"10.1002/cl2.1397","title":"Mapping the scientific knowledge and approaches to defining and measuring hate crime, hate speech, and hate incidents: A systematic review","year":2024,"lang":"en","type":"review","venue":"Campbell Systematic Reviews","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Hate crime; Legislation; Scrutiny; Prejudice (legal term); Criminology; Psychology; Rebuttal; Sociology; Political science; Law; Social psychology","score_opus":0.2267327007947307,"score_gpt":0.31179889646385867,"score_spread":0.08506619566912796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396223314","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019430335,0.9915276,0.0012795763,0.0012738787,0.00034487087,0.0022302817,0.00067959767,0.000020445681,0.0007007643],"genre_scores_gemma":[0.01812364,0.96965665,0.0047779856,0.0010357689,0.00014746939,0.0055068065,0.00054004235,0.00001916595,0.000192545],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.92802864,0.025376042,0.028496062,0.0041567776,0.012383375,0.0015590785],"domain_scores_gemma":[0.74250305,0.19401114,0.029924937,0.005465775,0.026402507,0.0016925432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06635792,0.002620375,0.01228273,0.06852613,0.0028936795,0.007946353,0.0048572375,0.0052673416,0.005490311],"category_scores_gemma":[0.25297633,0.0033549056,0.008113545,0.04934337,0.004822183,0.012526327,0.00675617,0.0034134635,0.0008581202],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008929804,0.000026216218,0.0009226485,0.94381064,0.0021423385,0.00018891723,0.0015870588,0.00014454221,0.0001809818,0.001125717,0.001735593,0.048045956],"study_design_scores_gemma":[0.000050005317,0.000057551104,0.0011153664,0.97900546,0.007523982,0.00014279998,0.0013252802,0.00006284604,0.00011306355,0.00054853177,0.0100280875,0.00002711748],"about_ca_topic_score_codex":0.019481162,"about_ca_topic_score_gemma":0.043514922,"teacher_disagreement_score":0.06852613,"about_ca_system_score_codex":0.015600752,"about_ca_system_score_gemma":0.07795575,"threshold_uncertainty_score":0.35093838},"labels":[],"label_agreement":null},{"id":"W4396542874","doi":"10.1109/ihtc58960.2023.10508866","title":"An Examination of Online Hate and Harassment Targeting Immigrants","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Harassment; Immigration; Computer science; Computer security; Internet privacy; Political science; Law","score_opus":0.013258825868064228,"score_gpt":0.2566774003150294,"score_spread":0.24341857444696516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396542874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985085,0.000063528394,0.000033709053,0.000096248485,0.0000034667912,0.000016472019,0.000030333074,7.895469e-7,0.0012469593],"genre_scores_gemma":[0.9985077,0.0004212518,0.00008873434,0.00018777321,0.000010790052,0.000024218974,0.000058773672,0.0000015200017,0.0006991801],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991211,0.00026045676,0.00007379424,0.000072550465,0.0002065535,0.0002654436],"domain_scores_gemma":[0.99702615,0.00045772022,0.001036732,0.00012989168,0.0008533909,0.0004961188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001483216,0.00026103025,0.00024845978,0.0019970452,0.004287035,0.001964567,0.00039968945,0.00067918433,0.0016005359],"category_scores_gemma":[0.004652941,0.0002784581,0.0002580768,0.0017659519,0.0016281859,0.0011688031,0.0023645104,0.0008776439,0.00023525511],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043721517,0.00016472579,0.71123964,0.00013010281,0.000023115728,0.0008510421,0.26590022,0.00002599928,0.0015226996,0.00034727153,0.00052102405,0.019230356],"study_design_scores_gemma":[0.0000018057943,0.00015666857,0.5969817,0.00013410352,0.000014868496,0.00049603166,0.39924115,0.000056680747,0.00022881369,0.00005073523,0.0026195506,0.000017934008],"about_ca_topic_score_codex":0.10256122,"about_ca_topic_score_gemma":0.24937002,"teacher_disagreement_score":0.10256122,"about_ca_system_score_codex":0.0015553982,"about_ca_system_score_gemma":0.0028170366,"threshold_uncertainty_score":0.20392853},"labels":[],"label_agreement":null},{"id":"W4396542919","doi":"10.1109/ihtc58960.2023.10508881","title":"Automated Identification of Microaggressions","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Identification (biology); Computer science; Biology","score_opus":0.013223208906149803,"score_gpt":0.2664356526854641,"score_spread":0.2532124437793143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396542919","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6740052,0.0015967367,0.27258378,0.0024355666,0.0007543544,0.00064354297,0.01020715,0.023881711,0.013891913],"genre_scores_gemma":[0.85104287,0.00031224487,0.1310873,0.00040837663,0.00017665053,0.00020555587,0.01004813,0.00028467388,0.00643425],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974101,0.0005894677,0.00022183359,0.00077809265,0.0007126478,0.00028776444],"domain_scores_gemma":[0.99258655,0.003055947,0.0011137902,0.00088487705,0.002098861,0.00025996054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015907804,0.0008964698,0.00068112666,0.003503743,0.0008529092,0.0014495178,0.0012948219,0.0012332099,0.0028422733],"category_scores_gemma":[0.0060866685,0.00022687651,0.00075994356,0.001365781,0.00043969473,0.00204318,0.0014077291,0.0015656785,0.0035387806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005136754,0.00066294515,0.11885152,0.0006861037,0.00021652534,0.0012083051,0.0020250739,0.012659996,0.040348157,0.0026325486,0.030902272,0.789293],"study_design_scores_gemma":[0.000046748617,0.00033449294,0.10060995,0.00020489455,0.00019071171,0.0013559808,0.0039723143,0.77209723,0.072538115,0.009062954,0.039441288,0.00014530211],"about_ca_topic_score_codex":0.004774764,"about_ca_topic_score_gemma":0.006133047,"teacher_disagreement_score":0.004774764,"about_ca_system_score_codex":0.00073023885,"about_ca_system_score_gemma":0.0009862314,"threshold_uncertainty_score":0.009508371},"labels":[],"label_agreement":null},{"id":"W4396671072","doi":"10.1145/3657635","title":"A Hybrid Deep BiLSTM-CNN for Hate Speech Detection in Multi-social media","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Social media; Voice activity detection; Artificial intelligence; Speech recognition; Natural language processing; Speech processing; World Wide Web","score_opus":0.010580423705731345,"score_gpt":0.24609146739213666,"score_spread":0.23551104368640532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396671072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39737535,0.005791165,0.5646153,0.0016026505,0.0011245748,0.00021266757,0.0026737894,0.012366098,0.014238305],"genre_scores_gemma":[0.92050105,0.00080909074,0.061191462,0.0005738267,0.000107066604,0.00009543939,0.002928373,0.00011461042,0.013679037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972063,0.000029463094,0.000012355376,0.00009239891,0.000062849256,0.000082233346],"domain_scores_gemma":[0.99971753,0.000055071017,0.0000315852,0.000034162334,0.00012945103,0.00003218673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005428717,0.0012840426,0.0006057702,0.0007749434,0.00036052646,0.00060258585,0.0012456059,0.0009085321,0.0020614453],"category_scores_gemma":[0.0010019802,0.00036613725,0.0005078377,0.0005648836,0.00025261627,0.0014146926,0.00096093956,0.0011126259,0.0012804015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069695176,0.000687507,0.012057646,0.00029749895,0.00030924525,0.00059914065,0.00022613606,0.10784007,0.042789284,0.0022421787,0.021511463,0.8107429],"study_design_scores_gemma":[0.00001036567,0.000096738026,0.0017491713,0.000021747786,0.000039111314,0.00007130872,0.00003519312,0.9877331,0.007903759,0.00096799963,0.0013555165,0.000016090531],"about_ca_topic_score_codex":0.013378076,"about_ca_topic_score_gemma":0.023328036,"teacher_disagreement_score":0.013378076,"about_ca_system_score_codex":0.00083648,"about_ca_system_score_gemma":0.00092203345,"threshold_uncertainty_score":0.02660042},"labels":[],"label_agreement":null},{"id":"W4396715353","doi":"10.29173/mlj1299","title":"Talking to Strangers: A Critical Analysis of the Supreme Court of Canada’s Decision in R v Mills","year":2022,"lang":"en","type":"article","venue":"Manitoba Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Warrant; Supreme court; Officer; Law; Enforcement; Jurisprudence; Law enforcement; Economic Justice; Power (physics); Position (finance); Political science; Business","score_opus":0.011194873975422119,"score_gpt":0.23348687022270465,"score_spread":0.22229199624728252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396715353","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15657355,0.013287281,0.0038246077,0.58995736,0.00442399,0.0006171933,0.0002622955,0.000095350435,0.23095837],"genre_scores_gemma":[0.6841801,0.004865064,0.0016803849,0.26881775,0.0014076589,0.00030038448,0.00007591781,0.00011708875,0.038555633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9708237,0.0069545954,0.0009961014,0.0037729081,0.008018905,0.009433818],"domain_scores_gemma":[0.94854754,0.035011888,0.001282223,0.0009243515,0.010394453,0.0038394507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032219443,0.0008099079,0.001241435,0.003703213,0.09519554,0.022797897,0.0075582927,0.037963104,0.0042704563],"category_scores_gemma":[0.06670216,0.0016510212,0.0016271485,0.0037151654,0.039294645,0.0075572086,0.008014834,0.049481206,0.00034880172],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079181336,0.00008249409,0.0042156815,0.0002608307,0.000069950554,0.0104160495,0.25851622,0.0003362709,0.0006326372,0.5874922,0.121544614,0.01635386],"study_design_scores_gemma":[0.00007938388,0.000095328076,0.009838541,0.0022418557,0.00025241522,0.0013640975,0.310155,0.0010351669,0.0015422442,0.07164684,0.6011958,0.00055327674],"about_ca_topic_score_codex":0.90679646,"about_ca_topic_score_gemma":0.94339174,"teacher_disagreement_score":0.12003877,"about_ca_system_score_codex":0.12003877,"about_ca_system_score_gemma":0.16790786,"threshold_uncertainty_score":0.8709463},"labels":[],"label_agreement":null},{"id":"W4396832273","doi":"10.1145/3613904.3642669","title":"The ``Colonial Impulse\" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Defense Advanced Research Projects Agency","keywords":"Bengali; Sentiment analysis; Computer science; Sociotechnical system; Artificial intelligence; Colonialism; Identity (music); Natural language processing; Political science; Aesthetics","score_opus":0.017674424163220005,"score_gpt":0.2905672516684043,"score_spread":0.2728928275051843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396832273","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8872232,0.0027587146,0.050804455,0.0041053933,0.00034645633,0.0015118723,0.011688894,0.007259029,0.034302093],"genre_scores_gemma":[0.8794414,0.0023118255,0.08764547,0.0010420199,0.00019476807,0.0011387444,0.01138594,0.005769695,0.011070226],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9825593,0.0063021565,0.002294208,0.0014883667,0.006584221,0.00077169144],"domain_scores_gemma":[0.9096071,0.040856365,0.0064541097,0.01427713,0.027924424,0.0008807956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01739845,0.00075726403,0.00049349305,0.006752968,0.0026060059,0.0046795076,0.0011134789,0.00053402386,0.0021585452],"category_scores_gemma":[0.06588906,0.0005966167,0.0005575178,0.008285143,0.0033364615,0.003851922,0.0043550734,0.001465034,0.0022179997],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011839768,0.00018488104,0.12888291,0.005435705,0.00026430225,0.0027534547,0.17775674,0.0012847258,0.1015116,0.010698905,0.039741114,0.5303017],"study_design_scores_gemma":[0.00005657012,0.00032616316,0.3459696,0.0015618252,0.00026396476,0.0023513532,0.057155978,0.012988698,0.10318598,0.006753165,0.46895558,0.00043109746],"about_ca_topic_score_codex":0.009653536,"about_ca_topic_score_gemma":0.0180572,"teacher_disagreement_score":0.01739845,"about_ca_system_score_codex":0.00222689,"about_ca_system_score_gemma":0.0030671223,"threshold_uncertainty_score":0.09201288},"labels":[],"label_agreement":null},{"id":"W4398174247","doi":"10.1002/poi3.393","title":"The (complex) effect of internet voting on turnout: Theoretical and methodological considerations","year":2024,"lang":"en","type":"article","venue":"Policy & Internet","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"Konrad-Adenauer-Stiftung","keywords":"Turnout; Voting; The Internet; Computer science; Political science; Epistemology; World Wide Web; Philosophy; Law; Politics","score_opus":0.04154728053599447,"score_gpt":0.34772429169931784,"score_spread":0.30617701116332335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398174247","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90030086,0.0050516045,0.04312168,0.016919756,0.0003352138,0.0010348847,0.006653153,0.00016907271,0.026413845],"genre_scores_gemma":[0.9898788,0.00034603957,0.0031620413,0.00058721035,0.00014554546,0.00038184953,0.0008819482,0.00002994917,0.0045867674],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9846882,0.008987298,0.0011999822,0.0023699861,0.0013214203,0.0014330536],"domain_scores_gemma":[0.8798059,0.09535638,0.0150268255,0.0052313698,0.0027610688,0.0018184219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0227855,0.0007674467,0.0017125665,0.0025624863,0.0028606222,0.004433548,0.0038962897,0.0019135271,0.018344255],"category_scores_gemma":[0.058737766,0.0005348213,0.002418729,0.004317719,0.0048573553,0.0035834287,0.0032740142,0.0026529345,0.0009010642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003430285,0.0003540884,0.93208784,0.000497943,0.0015647591,0.00041584406,0.0033786888,0.0061557377,0.00033120214,0.038694866,0.003161467,0.013014445],"study_design_scores_gemma":[0.0002753156,0.00060532504,0.8729689,0.0005446024,0.0018546261,0.00021885317,0.009745653,0.043482352,0.0012451797,0.054317024,0.014584023,0.00015806849],"about_ca_topic_score_codex":0.30125055,"about_ca_topic_score_gemma":0.26887637,"teacher_disagreement_score":0.30125055,"about_ca_system_score_codex":0.004994015,"about_ca_system_score_gemma":0.0041882517,"threshold_uncertainty_score":0.59899414},"labels":[],"label_agreement":null},{"id":"W4398361493","doi":"10.7910/dvn/oe6umr/7edtvs","title":"Becker Porter Centola - Wisdom of Partisan Crowds - Preprint.pdf","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Preprint; Crowds; Computer science; World Wide Web; Computer security","score_opus":0.014827743270474978,"score_gpt":0.23667473871305628,"score_spread":0.2218469954425813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398361493","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001382352,0.00045271724,0.00085126393,0.0011787039,0.0003557952,0.00004691311,0.98891276,0.0018272034,0.004992336],"genre_scores_gemma":[0.004247763,0.0002347028,0.0012858096,0.00018184313,0.00010764401,0.00010789849,0.9894924,0.00027911118,0.0040628184],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99761933,0.00050647336,0.0001943177,0.0005700899,0.00076609146,0.00034361565],"domain_scores_gemma":[0.99371135,0.0016208513,0.00037849406,0.0024418428,0.0013814904,0.00046597671],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0033986145,0.0029510735,0.0016396444,0.0063060047,0.0019002054,0.005085578,0.0030743612,0.0028077129,0.06394691],"category_scores_gemma":[0.016949726,0.0009225937,0.0017158402,0.0057947277,0.0010012181,0.0028832944,0.00387691,0.0024423595,0.11457149],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046380308,0.000020625663,0.0014786065,0.00015560065,0.000029476621,0.000010530077,0.000018596806,0.00039855498,0.000025550646,0.000606615,0.99338853,0.0038209008],"study_design_scores_gemma":[0.0005309107,0.000054163705,0.010480326,0.00045539113,0.00008642419,0.00014334738,0.0004454087,0.007426807,0.00096111075,0.015906699,0.9634405,0.0000689895],"about_ca_topic_score_codex":0.023583058,"about_ca_topic_score_gemma":0.05565761,"teacher_disagreement_score":0.9360531,"about_ca_system_score_codex":0.0017939636,"about_ca_system_score_gemma":0.0024227004,"threshold_uncertainty_score":0.2139237},"labels":[],"label_agreement":null},{"id":"W4398804797","doi":"10.7910/dvn/rv8akc","title":"Replication Data for: Does Protest Influence Political Speech? Evidence from UK Climate Protest, 2017-2019","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Replication (statistics); Politics; Political science; Media studies; Sociology; Law; Statistics; Mathematics","score_opus":0.04853380364154299,"score_gpt":0.3104079342969735,"score_spread":0.2618741306554305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398804797","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027268138,0.00019923359,0.0005711065,0.00072008074,0.00052532327,0.00069512107,0.9917807,0.00039104646,0.0023905663],"genre_scores_gemma":[0.015592879,0.00011282212,0.0019412952,0.00054044824,0.0001665958,0.010625689,0.9607324,0.00028186155,0.01000602],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99250644,0.002051064,0.0013542976,0.001679569,0.0016946486,0.0007140132],"domain_scores_gemma":[0.95207953,0.011620595,0.004558501,0.015927019,0.013679303,0.0021351024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010197617,0.0019779284,0.001662675,0.0027157448,0.003126701,0.0033780667,0.004446412,0.0034478467,0.12218361],"category_scores_gemma":[0.10128228,0.0011995564,0.0026683272,0.0036287454,0.001582632,0.0021873554,0.0030769387,0.0032464694,0.067035854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009575885,0.00014199706,0.004866035,0.000879013,0.00022319194,0.00011209883,0.00020904926,0.00016406385,0.00017950521,0.0006966415,0.9872822,0.004288684],"study_design_scores_gemma":[0.0126216365,0.0005663232,0.097528756,0.0022020787,0.00092879776,0.0006095691,0.001542832,0.00091251597,0.0013466686,0.005829688,0.8755386,0.0003725606],"about_ca_topic_score_codex":0.05522218,"about_ca_topic_score_gemma":0.08697469,"teacher_disagreement_score":0.12218361,"about_ca_system_score_codex":0.0015937415,"about_ca_system_score_gemma":0.0053665405,"threshold_uncertainty_score":0.40874493},"labels":[],"label_agreement":null},{"id":"W4399060744","doi":"10.29173/mlj1058","title":"A Proposed Hate Communication Restriction and Freedom of Expression Protection Act: A Possible Compromise to a Continuing Controversy","year":2009,"lang":"en","type":"article","venue":"Manitoba Law Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Compromise; Freedom of expression; Expression (computer science); Political science; Law and economics; Law; Computer science; Sociology; Human rights","score_opus":0.011448350753083269,"score_gpt":0.22071531041698328,"score_spread":0.20926695966390002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399060744","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017707987,0.004615959,0.0058181845,0.8652539,0.005367515,0.00030365025,0.00017060367,0.00015476579,0.100607544],"genre_scores_gemma":[0.1911426,0.003478311,0.021624226,0.5882094,0.003615202,0.00051799335,0.00013429517,0.00010664205,0.19117136],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98750246,0.0036209903,0.0005703064,0.0016965293,0.004362097,0.002247564],"domain_scores_gemma":[0.98116404,0.009592102,0.0006439543,0.0009350414,0.005185258,0.0024795835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023544507,0.0007808927,0.0010131559,0.0018114228,0.014562803,0.016270788,0.0063153277,0.042511072,0.0068545947],"category_scores_gemma":[0.025605457,0.001066294,0.0011575806,0.0017563074,0.017688893,0.0054362942,0.0029168897,0.026699405,0.0014556472],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060074373,0.00018034563,0.0025252018,0.00012767923,0.000028708724,0.0006532963,0.005007762,0.00042103254,0.0012441309,0.8047555,0.16158547,0.023410806],"study_design_scores_gemma":[0.00017676089,0.00021781123,0.005941445,0.00106984,0.00014999756,0.0005391394,0.0063182693,0.0033397689,0.0016343143,0.07963812,0.90066004,0.00031446284],"about_ca_topic_score_codex":0.37390593,"about_ca_topic_score_gemma":0.56099623,"teacher_disagreement_score":0.6260941,"about_ca_system_score_codex":0.025595058,"about_ca_system_score_gemma":0.09210858,"threshold_uncertainty_score":0.7434591},"labels":[],"label_agreement":null},{"id":"W4399139043","doi":"10.55041/ijsrem34695","title":"AUTOMATED HATE SPEECH DETECTION USING RECURRENT NEURAL NETWORK","year":2024,"lang":"en","type":"article","venue":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Recurrent neural network; Entertainment; Filter (signal processing); Artificial neural network; Machine learning; Deep learning; Data science","score_opus":0.040851249037068264,"score_gpt":0.32601613914999766,"score_spread":0.2851648901129294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399139043","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55945337,0.0018388081,0.4240836,0.0006427779,0.0003486181,0.000119360375,0.0010039387,0.0037613756,0.008748105],"genre_scores_gemma":[0.95596194,0.00037857136,0.038964786,0.00009161667,0.000055153865,0.000034947738,0.0008010427,0.000045508863,0.0036664708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995278,0.00010957205,0.000023210598,0.00012573884,0.00013279705,0.00008081856],"domain_scores_gemma":[0.9991026,0.00029642798,0.0001557952,0.00007984522,0.00031954388,0.000045794444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006999736,0.0006552909,0.00048728386,0.0009954277,0.00024866028,0.00052136614,0.0005314423,0.0006260587,0.0011119626],"category_scores_gemma":[0.0019255228,0.00021925737,0.00035863437,0.00040600044,0.00026048554,0.00064332987,0.00062973186,0.0007344791,0.0008522836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078920164,0.000444315,0.026613802,0.00023645813,0.00023887998,0.0008366344,0.00031460728,0.07631714,0.10239165,0.0019463837,0.01271452,0.7771565],"study_design_scores_gemma":[0.000008236206,0.00007089156,0.0073222034,0.000015927748,0.000031366304,0.0001519269,0.00006715656,0.9752484,0.015358942,0.00074427156,0.0009610073,0.000019742149],"about_ca_topic_score_codex":0.003792104,"about_ca_topic_score_gemma":0.0049329223,"teacher_disagreement_score":0.003792104,"about_ca_system_score_codex":0.00046883614,"about_ca_system_score_gemma":0.00036535467,"threshold_uncertainty_score":0.007540047},"labels":[],"label_agreement":null},{"id":"W4399819156","doi":"10.1515/9782760548534-010","title":"La Publicité Politique Négative Sur Internet Au Canada","year":2017,"lang":"fr","type":"book-chapter","venue":"Presses de l'Université du Québec eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Psychology; Political science","score_opus":0.012842254568929934,"score_gpt":0.17997305783827175,"score_spread":0.16713080326934182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399819156","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13600126,0.028543264,0.0007523295,0.08868173,0.0014910147,0.00003782247,0.001713096,0.00012036049,0.7426591],"genre_scores_gemma":[0.6157059,0.011411249,0.00028736534,0.006673914,0.00035595527,0.000026052356,0.0003602357,0.000097660886,0.36508164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99813914,0.00013068851,0.0000284148,0.00015932912,0.00093512237,0.00060722244],"domain_scores_gemma":[0.99799454,0.00049176486,0.00012160303,0.00005055448,0.0009873712,0.00035424854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068563735,0.0003743412,0.00047140708,0.0019667794,0.0102909915,0.008738124,0.00064007624,0.0019309103,0.019042509],"category_scores_gemma":[0.0028876842,0.00039349773,0.00032193767,0.003191148,0.0048364713,0.0017642825,0.0010580019,0.004249011,0.00080807856],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013840772,0.00008175953,0.013846279,0.00021722789,0.00004395651,0.0007321775,0.015993487,0.0010086719,0.0009921469,0.6304183,0.2561465,0.08038113],"study_design_scores_gemma":[0.000030609124,0.000022760916,0.046625618,0.00035197943,0.000033464265,0.000200629,0.010413997,0.00073637883,0.0005094884,0.009300497,0.93171,0.00006456856],"about_ca_topic_score_codex":0.994676,"about_ca_topic_score_gemma":0.9954893,"teacher_disagreement_score":0.09165189,"about_ca_system_score_codex":0.09165189,"about_ca_system_score_gemma":0.097835414,"threshold_uncertainty_score":0.6649841},"labels":[],"label_agreement":null},{"id":"W4399855745","doi":"10.1007/978-3-031-62269-4_24","title":"Multi-modal Deep Learning for Detecting Toxicity in Transcribed-Audio Conversations","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Modal; Computer science; Speech recognition; Artificial intelligence; Chemistry","score_opus":0.017474201884108732,"score_gpt":0.2331538534184319,"score_spread":0.21567965153432317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399855745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28742385,0.003170609,0.68360466,0.00076264684,0.00039113784,0.00021184282,0.004307589,0.007574841,0.012552828],"genre_scores_gemma":[0.7407384,0.0008722508,0.23426045,0.00030326142,0.00016608507,0.0002688571,0.006580897,0.00025733988,0.016552472],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995819,0.00012724487,0.000015129959,0.00011336514,0.00008271805,0.000079642305],"domain_scores_gemma":[0.9992648,0.0004372257,0.000048346814,0.00007423065,0.00013537866,0.00004006166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006887672,0.00094612053,0.00041261656,0.0007685328,0.00038099108,0.000634679,0.00083345984,0.0008966903,0.0030933246],"category_scores_gemma":[0.0017628008,0.0002943975,0.00055717386,0.0006935367,0.00022167643,0.0009796736,0.0014185923,0.0013565188,0.0020561307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051003456,0.00043548064,0.0048897215,0.00017814107,0.0001193469,0.0001796917,0.0002430438,0.030328635,0.053424846,0.0015531733,0.013468958,0.8946689],"study_design_scores_gemma":[0.000014833612,0.00013019495,0.007087139,0.000038103008,0.00006020604,0.00011122971,0.00019080816,0.96384776,0.020513946,0.004902378,0.0030730364,0.000030333205],"about_ca_topic_score_codex":0.003411579,"about_ca_topic_score_gemma":0.009082415,"teacher_disagreement_score":0.003411579,"about_ca_system_score_codex":0.00043089627,"about_ca_system_score_gemma":0.00042845856,"threshold_uncertainty_score":0.010348201},"labels":[],"label_agreement":null},{"id":"W4400084879","doi":"10.1007/978-3-031-61819-2_4","title":"Social Media Hate Against Female Journalists","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Social media; Psychology; Media studies; Sociology; Political science; Social psychology; Law","score_opus":0.0267471711562612,"score_gpt":0.24459226429191328,"score_spread":0.2178450931356521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400084879","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04063362,0.0066233017,0.0010478052,0.0065479395,0.0018302806,0.000033631153,0.0001731815,0.00009365635,0.9430167],"genre_scores_gemma":[0.1624204,0.0052241073,0.00061536505,0.0025889399,0.0014045424,0.000026464377,0.00013978468,0.00010328951,0.82747704],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995486,0.00010722464,0.0000102494405,0.000041037034,0.00020879178,0.00008407759],"domain_scores_gemma":[0.9988319,0.00071725267,0.00012478622,0.000059675738,0.00016211403,0.000104196806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049680984,0.0005236676,0.00015860944,0.0010085222,0.0020335915,0.003082845,0.00034656355,0.0012502243,0.038301602],"category_scores_gemma":[0.0020166403,0.00021898703,0.00021687984,0.0005590629,0.0010614597,0.0018469767,0.0010116898,0.0011009104,0.010175819],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001522205,0.00024995187,0.00849592,0.00057852664,0.000038077742,0.0011720149,0.02062714,0.00013223784,0.009767518,0.08479574,0.39147633,0.48251432],"study_design_scores_gemma":[0.00001101737,0.00021231038,0.022609023,0.00067026436,0.000035290028,0.0020735997,0.012738472,0.00049714907,0.007984237,0.0068494445,0.94628733,0.00003188152],"about_ca_topic_score_codex":0.0020100656,"about_ca_topic_score_gemma":0.0072669555,"teacher_disagreement_score":0.038301602,"about_ca_system_score_codex":0.00060783484,"about_ca_system_score_gemma":0.00039187382,"threshold_uncertainty_score":0.12813169},"labels":[],"label_agreement":null},{"id":"W4400084887","doi":"10.1007/978-3-031-61819-2","title":"Online Hate on Social Media","year":2024,"lang":"en","type":"book","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Government of Canada","keywords":"Social media; Internet privacy; Media studies; Political science; Sociology; Computer science; World Wide Web","score_opus":0.027264767423250576,"score_gpt":0.2521766058601175,"score_spread":0.2249118384368669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400084887","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043398826,0.0071545127,0.010178523,0.0015973401,0.001295269,0.000057289584,0.00095546764,0.0009906993,0.973431],"genre_scores_gemma":[0.012822807,0.004256757,0.0030766563,0.00044815298,0.0005371859,0.00002681026,0.0005258787,0.00027673118,0.9780291],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998128,0.000018949664,0.0000033838899,0.000017533966,0.0001307997,0.000016713133],"domain_scores_gemma":[0.99969757,0.00015055845,0.000017905919,0.000031970412,0.00007391079,0.00002813756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015180849,0.0006915231,0.00022360242,0.0017248048,0.0007335676,0.001997456,0.00042085757,0.00079696346,0.08105729],"category_scores_gemma":[0.0007088681,0.00025943268,0.00023065755,0.0011132732,0.00037221677,0.002445537,0.00079097087,0.00064964587,0.054839943],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018297435,0.000036196907,0.0008631026,0.00012098902,0.0000075195217,0.000111080626,0.00022135086,0.00017943456,0.0015953836,0.019254288,0.50293374,0.47465855],"study_design_scores_gemma":[0.0000029591765,0.00003543655,0.005113025,0.00026748725,0.000011760865,0.000726722,0.00031419983,0.0015939883,0.0028262513,0.012271162,0.97682005,0.000017057346],"about_ca_topic_score_codex":0.0029464078,"about_ca_topic_score_gemma":0.011829001,"teacher_disagreement_score":0.08105729,"about_ca_system_score_codex":0.00039398362,"about_ca_system_score_gemma":0.00027327813,"threshold_uncertainty_score":0.27116364},"labels":[],"label_agreement":null},{"id":"W4400084893","doi":"10.1007/978-3-031-61819-2_5","title":"Conclusion","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Geography","score_opus":0.01044063362792402,"score_gpt":0.2109700833624887,"score_spread":0.20052944973456469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400084893","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002554163,0.001661021,0.004693995,0.034877386,0.006306473,0.00009585115,0.0011574344,0.00038504985,0.94826853],"genre_scores_gemma":[0.047767494,0.002483242,0.0033685206,0.033503715,0.0019204869,0.00013552191,0.0019871865,0.00044518468,0.9083886],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998355,0.00025384128,0.00004235298,0.00033632162,0.00074909936,0.0002633914],"domain_scores_gemma":[0.99708384,0.00051242305,0.00009617937,0.00032565623,0.0015412788,0.00044057675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020366663,0.00051499583,0.000300433,0.0010141191,0.0019862026,0.004873622,0.0014112131,0.0018737618,0.2926117],"category_scores_gemma":[0.0075240824,0.00016542296,0.00057932007,0.0006695253,0.0010326756,0.002657752,0.001959799,0.0020732246,0.1038781],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013729357,0.00006681124,0.0015781391,0.00032478038,0.000017179258,0.00020597609,0.0009189048,0.00020468293,0.00073282025,0.1580614,0.66395277,0.1737992],"study_design_scores_gemma":[0.000011000376,0.00001377324,0.00092530216,0.00020575676,0.00000925205,0.0000953663,0.0010029922,0.00006649709,0.00047368807,0.01859453,0.978596,0.0000057575526],"about_ca_topic_score_codex":0.006816319,"about_ca_topic_score_gemma":0.008053325,"teacher_disagreement_score":0.2926117,"about_ca_system_score_codex":0.0028364393,"about_ca_system_score_gemma":0.0035994279,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4400084938","doi":"10.1007/978-3-031-61819-2_2","title":"The Far-Right Online Communities and the Dissemination of Hate","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Far right; Political science; Geography; Internet privacy; Computer science; Law","score_opus":0.009441692702943783,"score_gpt":0.22865056251016,"score_spread":0.2192088698072162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400084938","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17509317,0.01806965,0.020962428,0.03166426,0.0005125749,0.000070315546,0.0003134802,0.00009767589,0.7532163],"genre_scores_gemma":[0.95949024,0.004955483,0.0026102783,0.0010481004,0.0003478733,0.000036816367,0.000083035986,0.00004723336,0.031381026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850106,0.0007281029,0.00003839126,0.0001670155,0.0003813515,0.00018399075],"domain_scores_gemma":[0.9895856,0.0069589885,0.0013168475,0.00077277125,0.00065160025,0.00071419205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00225855,0.00023728434,0.00027092334,0.0019353844,0.0031914103,0.006031995,0.00068498,0.0023845206,0.014609194],"category_scores_gemma":[0.013472299,0.00027800235,0.00022298584,0.001991735,0.00742119,0.012468228,0.0033122543,0.0023831043,0.0010332685],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006835337,0.00008379224,0.009082664,0.00014724127,0.000020888749,0.00027740464,0.02633711,0.00039628657,0.0006419916,0.8042131,0.016216634,0.14251445],"study_design_scores_gemma":[0.000016972164,0.00006412685,0.026302202,0.00058083335,0.000021845877,0.0009873989,0.032000452,0.0031022956,0.0009303714,0.79467833,0.14126909,0.00004603409],"about_ca_topic_score_codex":0.0049954043,"about_ca_topic_score_gemma":0.008067298,"teacher_disagreement_score":0.014609194,"about_ca_system_score_codex":0.0015518367,"about_ca_system_score_gemma":0.0011461338,"threshold_uncertainty_score":0.04887259},"labels":[],"label_agreement":null},{"id":"W4400084941","doi":"10.1007/978-3-031-61819-2_1","title":"Introduction","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.008436272288253558,"score_gpt":0.19863699932818416,"score_spread":0.1902007270399306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400084941","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002508739,0.002529097,0.0041185757,0.0024909687,0.0040084166,0.00011995201,0.0014263971,0.0007611965,0.9842944],"genre_scores_gemma":[0.000580285,0.0012418546,0.0008266514,0.00074625306,0.00047307878,0.000045579985,0.00079097477,0.00016284866,0.9951324],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942756,0.00005249593,0.000018880322,0.000114590155,0.00032375238,0.0000628888],"domain_scores_gemma":[0.9992156,0.00011034642,0.000025616397,0.00011028203,0.0003736944,0.00016446647],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00060593797,0.00094967434,0.0005294451,0.0019107251,0.0014753866,0.004078924,0.0015305745,0.0017871633,0.60626686],"category_scores_gemma":[0.0021635424,0.0003838333,0.0005088928,0.0015138116,0.0005872717,0.0031708872,0.0025523482,0.001879436,0.5758097],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014056337,0.000032372445,0.00007642309,0.00010119868,0.0000013449123,0.000033898963,0.00009523942,0.00009046286,0.0003110628,0.023496762,0.8100198,0.16572748],"study_design_scores_gemma":[0.0000010577517,0.00000473126,0.000051066534,0.000046383717,5.577975e-7,0.000025965885,0.00002686266,0.000017045302,0.000055898614,0.0024738312,0.9972946,0.0000018613742],"about_ca_topic_score_codex":0.0027255204,"about_ca_topic_score_gemma":0.004985587,"teacher_disagreement_score":0.60626686,"about_ca_system_score_codex":0.0012466623,"about_ca_system_score_gemma":0.0019133012,"threshold_uncertainty_score":0.5616125},"labels":[],"label_agreement":null},{"id":"W4400123646","doi":"10.1145/3675805","title":"Game On, Hate Off: A Study of Toxicity in Online Multiplayer Environments","year":2024,"lang":"en","type":"article","venue":"Games Research and Practice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Ubisoft (Canada)","funders":"Canadian Institute for Advanced Research","keywords":"Leverage (statistics); Computer science; Social media; Video game; Internet privacy; Data science; Multimedia; World Wide Web; Artificial intelligence","score_opus":0.07496877873334366,"score_gpt":0.3900591640580643,"score_spread":0.3150903853247206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400123646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985417,0.000027767952,0.00075227587,0.000062519255,0.000003678707,0.00003438161,0.0000681089,0.000013760429,0.000495795],"genre_scores_gemma":[0.99728227,0.00006157098,0.0011222977,0.00010609221,0.000010739506,0.000051411327,0.00020803406,0.000014236328,0.0011434129],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99855894,0.0007717842,0.0000877217,0.00022349054,0.00026046592,0.000097574535],"domain_scores_gemma":[0.98613364,0.008019411,0.0027213546,0.00078705477,0.001359005,0.0009796026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018541384,0.0005234807,0.00057042117,0.0014286225,0.0011194678,0.0011644097,0.00062791473,0.0009086966,0.0009891381],"category_scores_gemma":[0.017607624,0.00037757907,0.00031743917,0.00063071545,0.0011313186,0.0016368827,0.0012325076,0.0012030945,0.0005438635],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000650486,0.0025897736,0.89903945,0.00020494057,0.00025962837,0.0010952279,0.04194684,0.0020445536,0.008605898,0.00067362614,0.0014794957,0.04141005],"study_design_scores_gemma":[0.00002474867,0.0016476212,0.95563763,0.000062378494,0.00007428486,0.000759184,0.019724663,0.01585963,0.0023968187,0.0012241774,0.0025093972,0.000079292695],"about_ca_topic_score_codex":0.010864666,"about_ca_topic_score_gemma":0.019314524,"teacher_disagreement_score":0.010864666,"about_ca_system_score_codex":0.00065031374,"about_ca_system_score_gemma":0.00043934153,"threshold_uncertainty_score":0.021602869},"labels":[],"label_agreement":null},{"id":"W4400206824","doi":"10.1145/3656156.3663730","title":"Viral Transmissions: Memes as Strategies for Destigmatizing Taboos Around Sexual Health on TikTok","year":2024,"lang":"en","type":"article","venue":"Designing Interactive Systems Conference","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Virology; Biology","score_opus":0.05300509481322056,"score_gpt":0.3233143374913437,"score_spread":0.2703092426781231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400206824","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6188276,0.0031062874,0.09306163,0.019367628,0.0018501354,0.0007646015,0.0003301663,0.0011827629,0.26150915],"genre_scores_gemma":[0.9387505,0.00082269014,0.026315555,0.0018734297,0.00024136582,0.00028237962,0.00010067921,0.00029649344,0.031316802],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9966239,0.0026344147,0.000089354915,0.0001727044,0.00026271664,0.00021700462],"domain_scores_gemma":[0.9894593,0.0076904944,0.0007376069,0.000990764,0.0005249858,0.00059692503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042924224,0.00096083607,0.00026033333,0.0014334305,0.0049789473,0.008689526,0.0012800992,0.0019698227,0.009917639],"category_scores_gemma":[0.014204433,0.00036667642,0.00036843456,0.00059207703,0.0076293508,0.008160856,0.0059495093,0.0019210433,0.0016857618],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039025518,0.0003565689,0.011704603,0.0017893779,0.00008670894,0.0017500919,0.6489162,0.0010115019,0.018917892,0.14763945,0.01693307,0.15050437],"study_design_scores_gemma":[0.00010061458,0.0006767266,0.008513258,0.0016714119,0.00017726238,0.0026050876,0.36736298,0.006857188,0.016973084,0.06307693,0.53178674,0.00019867103],"about_ca_topic_score_codex":0.00055858586,"about_ca_topic_score_gemma":0.0014253828,"teacher_disagreement_score":0.009917639,"about_ca_system_score_codex":0.0010310222,"about_ca_system_score_gemma":0.00074252347,"threshold_uncertainty_score":0.033177853},"labels":[],"label_agreement":null},{"id":"W4400484309","doi":"10.1145/3663529.3664462","title":"Evaluating Social Bias in Code Generation Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Code (set theory); Programming language","score_opus":0.25751621523736423,"score_gpt":0.3771989944879567,"score_spread":0.11968277925059245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400484309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8849606,0.00060726487,0.10688441,0.0009530662,0.00007877008,0.00032726882,0.00043408558,0.00050761434,0.0052469685],"genre_scores_gemma":[0.9766381,0.00005376149,0.02206602,0.00011964887,0.000028884066,0.00016738744,0.00051126356,0.000075769596,0.0003392773],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9593697,0.0280411,0.0016139902,0.003115945,0.0071211457,0.00073803315],"domain_scores_gemma":[0.69204515,0.25456065,0.018238848,0.018415645,0.014407974,0.0023317286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03507867,0.00084406673,0.000550145,0.002148066,0.0009429353,0.0022272374,0.0011030602,0.0015654488,0.0011681742],"category_scores_gemma":[0.22383443,0.0002673515,0.00046048212,0.0010198603,0.0027729217,0.0027179462,0.0025464976,0.0014129975,0.0002656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020460633,0.00087637274,0.434239,0.0006976678,0.00059836596,0.0002953309,0.0046947584,0.24917243,0.009351566,0.039083958,0.0053595635,0.25358492],"study_design_scores_gemma":[0.0001687996,0.0008469377,0.03418125,0.00015863274,0.000090734604,0.00020680377,0.0009872079,0.8992864,0.011321786,0.0490642,0.0036138694,0.000073380106],"about_ca_topic_score_codex":0.0022355448,"about_ca_topic_score_gemma":0.0028371813,"teacher_disagreement_score":0.03507867,"about_ca_system_score_codex":0.0021719134,"about_ca_system_score_gemma":0.0020455,"threshold_uncertainty_score":0.185516},"labels":[],"label_agreement":null},{"id":"W4400603174","doi":"10.2307/jj.17102072.6","title":"The Rise of Online Voting in Ontario","year":2024,"lang":"en","type":"book-chapter","venue":"McGill-Queen's University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Voting; Computer science; Political science; Law","score_opus":0.0141250043705017,"score_gpt":0.1961368530772902,"score_spread":0.1820118487067885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400603174","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29856348,0.0054647042,0.00087148556,0.08463704,0.0007823973,0.00012175897,0.002921392,0.00014648607,0.6064912],"genre_scores_gemma":[0.65770435,0.0025525722,0.00039441974,0.0032919587,0.00014756953,0.000039029608,0.00044083112,0.00010840214,0.33532083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9970816,0.00020944387,0.0000655076,0.0001740829,0.0009102188,0.0015591898],"domain_scores_gemma":[0.9958326,0.0006014318,0.00029848018,0.00024565097,0.0012987107,0.00172321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001143856,0.00017347283,0.00039274408,0.0012811488,0.010961852,0.0048829233,0.0012104968,0.0017679991,0.029408313],"category_scores_gemma":[0.004837851,0.0004598349,0.0004019178,0.0052097756,0.0036873168,0.0018146434,0.0025909918,0.0021569354,0.001363426],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004221423,0.00012306724,0.07208661,0.00035173676,0.000044894252,0.001437725,0.04176005,0.0010773224,0.0009954788,0.4290423,0.27340525,0.17925343],"study_design_scores_gemma":[0.00003459597,0.000034240034,0.12603894,0.00014190747,0.0000148683075,0.00010424875,0.01728834,0.0006948303,0.0003433082,0.0055964775,0.8496617,0.00004656871],"about_ca_topic_score_codex":0.9901889,"about_ca_topic_score_gemma":0.9969379,"teacher_disagreement_score":0.07887425,"about_ca_system_score_codex":0.07887425,"about_ca_system_score_gemma":0.08931078,"threshold_uncertainty_score":0.5722754},"labels":[],"label_agreement":null},{"id":"W4400791873","doi":"10.1515/9780228021780-004","title":"2 The Rise of Online Voting in Ontario","year":2024,"lang":"en","type":"book-chapter","venue":"McGill-Queen's University Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Voting; Computer science; Political science; Law","score_opus":0.013881802825235668,"score_gpt":0.19591229360016738,"score_spread":0.18203049077493172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400791873","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08125355,0.0039431145,0.00076682214,0.05574848,0.0009053944,0.000079800215,0.0017426197,0.00009953067,0.85546064],"genre_scores_gemma":[0.31289995,0.0020777355,0.00034053985,0.0033478073,0.0001597061,0.00003519856,0.0002968296,0.00009395517,0.68074834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800843,0.00015290955,0.000034271543,0.0001311862,0.00058551633,0.0010877958],"domain_scores_gemma":[0.9981371,0.00029474287,0.00012903211,0.00013282328,0.0005632968,0.00074305834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007126324,0.00021670951,0.00028425033,0.0011304871,0.011897515,0.0050526434,0.00092218636,0.0020435601,0.05412943],"category_scores_gemma":[0.003231237,0.00036430775,0.00038858198,0.0040562428,0.0032748436,0.0020217309,0.0022587723,0.0021604213,0.002748989],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016606263,0.000048042857,0.021582538,0.0001814683,0.000015575602,0.00081628194,0.021163294,0.00047089974,0.00045862823,0.50062746,0.3443048,0.11016501],"study_design_scores_gemma":[0.000011987831,0.0000117681475,0.024918867,0.000066159955,0.0000056583426,0.000056553297,0.0059879655,0.000219995,0.00014691742,0.0047228895,0.96383256,0.000018524102],"about_ca_topic_score_codex":0.976137,"about_ca_topic_score_gemma":0.9929508,"teacher_disagreement_score":0.05412943,"about_ca_system_score_codex":0.051661808,"about_ca_system_score_gemma":0.063435175,"threshold_uncertainty_score":0.37483436},"labels":[],"label_agreement":null},{"id":"W4401043462","doi":"10.18653/v1/2024.naacl-long.236","title":"Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Linguistics; Speech recognition","score_opus":0.06829547400335344,"score_gpt":0.30021323872302325,"score_spread":0.2319177647196698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401043462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8051572,0.0028716729,0.040432516,0.003678415,0.0013267904,0.0015830247,0.13167955,0.0017127586,0.011558217],"genre_scores_gemma":[0.637806,0.0006681461,0.052900698,0.0007734652,0.000305255,0.0032373702,0.3004639,0.0005744623,0.0032706992],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.990244,0.004555421,0.0014094928,0.0020751473,0.0011449051,0.0005710738],"domain_scores_gemma":[0.96937716,0.011539258,0.0022771347,0.0085262805,0.0069566313,0.0013235674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010974846,0.00075576204,0.0005791903,0.0047642216,0.0019494736,0.0022370599,0.0015110191,0.00114379,0.0013500636],"category_scores_gemma":[0.035827074,0.00031746912,0.0007901358,0.003426193,0.0010587792,0.0020584594,0.004522048,0.0011899649,0.0020791672],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014918811,0.0010718524,0.4923112,0.0022578824,0.00091741927,0.0010827137,0.018886553,0.001848465,0.024463883,0.004324959,0.16936184,0.28198135],"study_design_scores_gemma":[0.00025473055,0.00053170463,0.7095119,0.0010344775,0.0009587017,0.0014044516,0.046433587,0.016350206,0.017426329,0.006453131,0.19929078,0.0003500649],"about_ca_topic_score_codex":0.011758933,"about_ca_topic_score_gemma":0.034755014,"teacher_disagreement_score":0.011758933,"about_ca_system_score_codex":0.00096591417,"about_ca_system_score_gemma":0.0019242119,"threshold_uncertainty_score":0.058041215},"labels":[],"label_agreement":null},{"id":"W4401049340","doi":"10.1007/978-3-031-56662-2_8","title":"Canada and the Marginalization of Pandemic Dissent","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Dissent; Pandemic; Political science; Coronavirus disease 2019 (COVID-19); Law; Medicine; Infectious disease (medical specialty); Internal medicine; Politics","score_opus":0.006645050457513684,"score_gpt":0.1850315451521062,"score_spread":0.17838649469459253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401049340","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03966497,0.006627244,0.00049598806,0.08037475,0.0008192088,0.000047378715,0.00026656745,0.000034647906,0.87166923],"genre_scores_gemma":[0.7691883,0.004654181,0.00031800458,0.020201577,0.00029564102,0.00005172469,0.0001092726,0.00009110176,0.2050903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9951291,0.0006304012,0.00005662962,0.00026949315,0.0011713542,0.002743056],"domain_scores_gemma":[0.9957557,0.0012157665,0.00020625567,0.00016466372,0.001306629,0.0013509761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019538354,0.0003613988,0.0004957656,0.0019197236,0.02892517,0.011183719,0.0015133275,0.0042608194,0.024872636],"category_scores_gemma":[0.008713254,0.0003189432,0.0003379593,0.003268541,0.01893385,0.003298623,0.004412685,0.0070323497,0.0010007108],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046092242,0.000030169022,0.0027984877,0.00006241867,0.000012270096,0.00037998625,0.029920917,0.00017257605,0.000111216454,0.81965697,0.121551834,0.02525701],"study_design_scores_gemma":[0.000029609708,0.000015617436,0.01177342,0.00035990073,0.000020727917,0.00021371775,0.05870234,0.0003414747,0.00025060805,0.058756314,0.86946654,0.000069677546],"about_ca_topic_score_codex":0.98408026,"about_ca_topic_score_gemma":0.99091,"teacher_disagreement_score":0.084594555,"about_ca_system_score_codex":0.084594555,"about_ca_system_score_gemma":0.14461698,"threshold_uncertainty_score":0.6137793},"labels":[],"label_agreement":null},{"id":"W4401717952","doi":"10.1109/tbiom.2024.3446846","title":"A Data Perspective on Ethical Challenges in Voice Biometrics Research","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Biometrics Behavior and Identity Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Mozilla Foundation","keywords":"Biometrics; Perspective (graphical); Psychology; Engineering ethics; Computer science; Engineering; Computer security; Artificial intelligence","score_opus":0.25637192312052653,"score_gpt":0.45307111263788774,"score_spread":0.1966991895173612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401717952","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05644049,0.012855407,0.3021724,0.5470115,0.0031651151,0.0013209465,0.008187924,0.00029183552,0.06855447],"genre_scores_gemma":[0.5566918,0.010749985,0.28256482,0.11767587,0.0063317404,0.006686675,0.006207497,0.00082419417,0.012267339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.69639105,0.23426114,0.016924573,0.012925592,0.03697265,0.0025250006],"domain_scores_gemma":[0.28572595,0.5657985,0.036167856,0.071425185,0.03784678,0.003035753],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25942364,0.0009880645,0.0012777251,0.007590793,0.008261321,0.02455548,0.0043969452,0.010436214,0.005985162],"category_scores_gemma":[0.44553417,0.0010568816,0.0014202739,0.0147187505,0.03372061,0.029398376,0.0118671805,0.012262984,0.0022026028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031154783,0.00016791566,0.027616093,0.001000533,0.00011685301,0.0004963724,0.038611956,0.0013370791,0.0017509516,0.8104384,0.033747207,0.084405065],"study_design_scores_gemma":[0.000075230884,0.00021538795,0.008442544,0.0037284815,0.00008886863,0.0014701866,0.027422832,0.0032873026,0.0044786474,0.6511009,0.299486,0.00020361778],"about_ca_topic_score_codex":0.0026342885,"about_ca_topic_score_gemma":0.002956667,"teacher_disagreement_score":0.7405764,"about_ca_system_score_codex":0.005984853,"about_ca_system_score_gemma":0.010503394,"threshold_uncertainty_score":0.9132625},"labels":[],"label_agreement":null},{"id":"W4401813233","doi":"10.1016/j.nlp.2024.100098","title":"HarmonyNet: Navigating hate speech detection","year":2024,"lang":"en","type":"article","venue":"Natural Language Processing Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"Vector Institute; Government of Ontario; Canadian Institute for Advanced Research","keywords":"Voice activity detection; Computer science; Speech recognition; Speech processing","score_opus":0.006087059208944044,"score_gpt":0.26614302229227743,"score_spread":0.2600559630833334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401813233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3842869,0.0027573076,0.5529469,0.0015535894,0.0009435217,0.0005376204,0.0039273873,0.030415434,0.022631325],"genre_scores_gemma":[0.8233431,0.00042502416,0.15208387,0.0007284074,0.00021946013,0.00015896116,0.0053090244,0.0005575543,0.017174602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990701,0.00024134081,0.000038540282,0.00029171424,0.00022864249,0.00012958155],"domain_scores_gemma":[0.9988825,0.00043153137,0.00009738699,0.0001890927,0.00031790975,0.0000815883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013213343,0.0012989091,0.00085459556,0.0016551986,0.00069895736,0.0009904621,0.0012454968,0.0013005312,0.0026504202],"category_scores_gemma":[0.0036391683,0.00035529953,0.00055699784,0.00055059086,0.00045731026,0.001943174,0.0024606988,0.00117066,0.0017585628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066176837,0.00055999355,0.024045028,0.00029603252,0.0003188949,0.0009068323,0.00073461264,0.04996382,0.029583013,0.002901873,0.05736713,0.83266115],"study_design_scores_gemma":[0.000020384034,0.00018194673,0.0037982878,0.000023758055,0.00005102776,0.0002669933,0.00028375248,0.96891886,0.015061749,0.0035901999,0.0077678817,0.00003509211],"about_ca_topic_score_codex":0.0060741412,"about_ca_topic_score_gemma":0.010315668,"teacher_disagreement_score":0.0060741412,"about_ca_system_score_codex":0.0005599273,"about_ca_system_score_gemma":0.00065906293,"threshold_uncertainty_score":0.01207757},"labels":[],"label_agreement":null},{"id":"W4401943669","doi":"10.1109/icdh62654.2024.00034","title":"Using Machine Learning to Track Disability Discourse on Social Media","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Track (disk drive); Computer science; Social media; Artificial intelligence; Natural language processing; World Wide Web","score_opus":0.047695610086968376,"score_gpt":0.3219592358438867,"score_spread":0.2742636257569183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401943669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9480503,0.0012009493,0.036315273,0.001041584,0.00028763115,0.00050623395,0.0051346356,0.00152596,0.0059374445],"genre_scores_gemma":[0.9482055,0.0005237743,0.0412252,0.00012158928,0.00024175106,0.00031993873,0.0068046465,0.000047682304,0.0025098189],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9980167,0.00081352214,0.00017774371,0.00041434623,0.00038588658,0.00019189045],"domain_scores_gemma":[0.9895064,0.0075573414,0.0012339251,0.00040187023,0.0010199312,0.00028046052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026791415,0.0009011458,0.00047806295,0.008862111,0.0007856476,0.0020191658,0.0005282588,0.0011179429,0.0011340713],"category_scores_gemma":[0.009229947,0.00019805405,0.000665966,0.00314944,0.00044256073,0.0025740943,0.0012600085,0.0010605853,0.0014590486],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007643692,0.00132807,0.20215708,0.00088664057,0.0002967646,0.00093802094,0.0068908953,0.014555105,0.016850382,0.0016349074,0.012810679,0.7408871],"study_design_scores_gemma":[0.00003912464,0.00047875041,0.19454889,0.00023452898,0.00012130049,0.00052402384,0.009282678,0.7647592,0.009845797,0.0037896682,0.016244737,0.00013129826],"about_ca_topic_score_codex":0.003908193,"about_ca_topic_score_gemma":0.006030903,"teacher_disagreement_score":0.008862111,"about_ca_system_score_codex":0.00084702036,"about_ca_system_score_gemma":0.00049305597,"threshold_uncertainty_score":0.014168799},"labels":[],"label_agreement":null},{"id":"W4402062355","doi":"10.55284/sol.v2022i4.99","title":"Social Media Providers and Human Rights","year":2022,"lang":"en","type":"article","venue":"Science of law.","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Human rights; Social media; Internet privacy; Business; Political science; Computer science; Law","score_opus":0.017106801173377553,"score_gpt":0.2589292910910271,"score_spread":0.24182248991764954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402062355","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09359073,0.0020929598,0.012112816,0.07089321,0.00026056313,0.00015382713,0.000105223524,0.000056799458,0.8207339],"genre_scores_gemma":[0.9671943,0.0009927538,0.0012924424,0.0040723872,0.00024513723,0.000093049275,0.000024973564,0.000023322762,0.026061637],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9855284,0.008559679,0.0003988724,0.0012607021,0.0025014963,0.0017508718],"domain_scores_gemma":[0.9697244,0.019574776,0.004187467,0.0028941922,0.0018280931,0.0017910233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008753953,0.00026355902,0.0002421684,0.002088956,0.0069526886,0.008498661,0.00084501266,0.005764109,0.01793835],"category_scores_gemma":[0.024324011,0.00029703297,0.00025978265,0.0015909903,0.034221787,0.012185004,0.006295961,0.0043482906,0.0013040613],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012334498,0.000039774306,0.0032403476,0.00003357424,0.0000038879684,0.00022212317,0.011138584,0.000101799604,0.00020222981,0.96315795,0.004297833,0.017549442],"study_design_scores_gemma":[0.000027904387,0.000100612226,0.011109433,0.0007829992,0.00001951253,0.0009656402,0.038006723,0.0015683927,0.00094408647,0.63483554,0.31158283,0.000056285477],"about_ca_topic_score_codex":0.0055476013,"about_ca_topic_score_gemma":0.004771831,"teacher_disagreement_score":0.01793835,"about_ca_system_score_codex":0.0035890979,"about_ca_system_score_gemma":0.0043794313,"threshold_uncertainty_score":0.060009718},"labels":[],"label_agreement":null},{"id":"W4402070518","doi":"10.62051/zw6md078","title":"Legal Reflections: Optimizing Global Strategies Against Cyber Sexual Violence Through Comparative Perspectives","year":2024,"lang":"en","type":"article","venue":"Transactions on Social Science Education and Humanities Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sexual violence; Criminology; Political science; Computer security; Sociology; Engineering ethics; Computer science; Engineering","score_opus":0.15583682592291628,"score_gpt":0.4549107482990798,"score_spread":0.2990739223761635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402070518","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08657533,0.026287125,0.0060082977,0.095107384,0.00096471654,0.00008585256,0.000084708525,0.00004137642,0.78484523],"genre_scores_gemma":[0.9646501,0.013714815,0.0028501893,0.00774508,0.00027619227,0.00009473913,0.00008297368,0.000044271877,0.010541624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929081,0.0051590838,0.00013103735,0.00033159042,0.00048020095,0.0009899396],"domain_scores_gemma":[0.99290985,0.0048169773,0.0005128172,0.00046139857,0.00084323867,0.00045560906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0104316445,0.0006112287,0.00050580286,0.00610838,0.0112003265,0.01315647,0.002164909,0.0046892967,0.010430188],"category_scores_gemma":[0.010414421,0.0002130368,0.00037098787,0.004823835,0.034792315,0.016270446,0.007636253,0.0049205166,0.00045019132],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013514476,0.0000365787,0.00087622256,0.00020859919,0.00000880383,0.00033905008,0.063271865,0.00014438307,0.0001316835,0.9111662,0.004123523,0.019679554],"study_design_scores_gemma":[0.000016726468,0.00007975485,0.0040304335,0.0020338988,0.00004889032,0.0004567859,0.48889494,0.00032154692,0.0008818872,0.15558787,0.34761664,0.000030553147],"about_ca_topic_score_codex":0.015381684,"about_ca_topic_score_gemma":0.027758159,"teacher_disagreement_score":0.015381684,"about_ca_system_score_codex":0.012497512,"about_ca_system_score_gemma":0.009330543,"threshold_uncertainty_score":0.09067625},"labels":[],"label_agreement":null},{"id":"W4402144219","doi":"10.5430/wjel.v14n6p583","title":"Speech Act Analysis: Pakistani Private Universities as a Case in Point","year":2024,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Point (geometry); Computer science; Mathematics","score_opus":0.005147448786884441,"score_gpt":0.24573183504869936,"score_spread":0.24058438626181491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402144219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9880613,0.00013914441,0.0039006413,0.0006239934,0.00004161593,0.000111930174,0.00013066211,0.0000189709,0.006971753],"genre_scores_gemma":[0.99448276,0.00016242402,0.0023174612,0.000112018024,0.000024430414,0.000103074664,0.0000674146,0.000011737099,0.002718733],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9962585,0.0027111627,0.000115194955,0.00020342399,0.0005061222,0.0002055859],"domain_scores_gemma":[0.98564005,0.01164705,0.0009229216,0.00047481433,0.0010539732,0.000261223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003509367,0.00036362355,0.0002630622,0.001739756,0.003276561,0.003042042,0.00054995494,0.0012675481,0.0018896933],"category_scores_gemma":[0.010893841,0.00019431503,0.00021221452,0.0014949441,0.0034617353,0.0015434413,0.0017720113,0.0012564695,0.0005365596],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012368681,0.00009893501,0.013602145,0.00017982927,0.000008760572,0.0037291301,0.95198077,0.00025964502,0.005749065,0.0062187705,0.0009953582,0.017054008],"study_design_scores_gemma":[0.000009913116,0.00012559141,0.026089847,0.00017261048,0.000019790463,0.0014591997,0.9415482,0.0023292562,0.0054043555,0.0019231691,0.020863594,0.000054439533],"about_ca_topic_score_codex":0.004957552,"about_ca_topic_score_gemma":0.0070789615,"teacher_disagreement_score":0.004957552,"about_ca_system_score_codex":0.001589639,"about_ca_system_score_gemma":0.00084462104,"threshold_uncertainty_score":0.018559575},"labels":[],"label_agreement":null},{"id":"W4402474726","doi":"10.1109/ccece59415.2024.10667078","title":"A Vision-Language Multimodal Framework for Detecting Hate Speech in Memes","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Speech recognition; Natural language processing; Human–computer interaction","score_opus":0.010630772304018458,"score_gpt":0.29734760023363566,"score_spread":0.2867168279296172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14386645,0.0034061125,0.8273024,0.001442051,0.000471115,0.0005499222,0.0017693309,0.007506362,0.013686211],"genre_scores_gemma":[0.7953784,0.0008491518,0.18922684,0.0006391327,0.00031923476,0.00037033932,0.0026788125,0.0001534997,0.010384586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993611,0.00017681377,0.000026811844,0.00019134361,0.0001283666,0.00011553365],"domain_scores_gemma":[0.9994281,0.00016170324,0.00006300223,0.00006914976,0.00022024738,0.000057723857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011947555,0.0013894631,0.000776158,0.0019461669,0.0005980503,0.0010669845,0.0010457549,0.0011492677,0.0019035673],"category_scores_gemma":[0.0022125526,0.00024045687,0.0010097206,0.0006044639,0.00061429833,0.0013982528,0.0011727582,0.0014144671,0.0014453268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080544554,0.0010424552,0.008652352,0.0004485242,0.0002876268,0.00060083944,0.00066592824,0.043222066,0.066627696,0.0065884097,0.02135968,0.84969896],"study_design_scores_gemma":[0.000030411084,0.00039594542,0.0060185734,0.000077972065,0.00013650818,0.00035183082,0.00044207083,0.9584239,0.021046026,0.0062232707,0.0067770067,0.0000764881],"about_ca_topic_score_codex":0.006969997,"about_ca_topic_score_gemma":0.008960577,"teacher_disagreement_score":0.006969997,"about_ca_system_score_codex":0.00075158954,"about_ca_system_score_gemma":0.00081233337,"threshold_uncertainty_score":0.013858855},"labels":[],"label_agreement":null},{"id":"W4402504791","doi":"10.1002/9781394248438.ch31","title":"Hate Neologism in Election Context in India","year":2024,"lang":"en","type":"other","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Neologism; Context (archaeology); Political science; Geography; Linguistics; Philosophy; Archaeology","score_opus":0.00766970586829317,"score_gpt":0.22858809493108936,"score_spread":0.2209183890627962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402504791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91813004,0.0011938394,0.013091938,0.001708896,0.000108608234,0.000058384878,0.000508635,0.00017491386,0.06502474],"genre_scores_gemma":[0.9962986,0.00018867658,0.0012203859,0.000059403934,0.000011537303,0.0000051883762,0.00009844001,0.000011249295,0.0021065322],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991659,0.00032925792,0.0000702442,0.00014300054,0.00015409579,0.00013745869],"domain_scores_gemma":[0.99888366,0.00047858214,0.0002623048,0.000088605106,0.0002156389,0.0000711681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044331182,0.000198123,0.000105884355,0.0014668998,0.0022241063,0.0020231158,0.00030490296,0.00028879664,0.0019569052],"category_scores_gemma":[0.0018871793,0.00014748097,0.00020780005,0.0016197939,0.0019593523,0.002013953,0.0013821484,0.00072950334,0.00028955564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002140495,0.00017167797,0.3510499,0.0006905478,0.000061813495,0.007391891,0.25423056,0.0020213644,0.019357845,0.16105683,0.011895512,0.191858],"study_design_scores_gemma":[0.0000074444897,0.000061679595,0.54154587,0.00030231194,0.00011389472,0.005741489,0.2538115,0.023850922,0.013725074,0.02784546,0.13282076,0.0001734848],"about_ca_topic_score_codex":0.04244886,"about_ca_topic_score_gemma":0.051725022,"teacher_disagreement_score":0.04244886,"about_ca_system_score_codex":0.0019405918,"about_ca_system_score_gemma":0.0011718213,"threshold_uncertainty_score":0.084403515},"labels":[],"label_agreement":null},{"id":"W4402632559","doi":"10.1007/978-3-031-71602-7_21","title":"Experiments in Modeling Disagreement","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.02277986602355848,"score_gpt":0.2599190047508667,"score_spread":0.23713913872730824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402632559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83594775,0.0018735699,0.09825183,0.005422921,0.0009541929,0.0010978724,0.007137968,0.0022841315,0.04702976],"genre_scores_gemma":[0.96415794,0.00013204529,0.025152788,0.00048751896,0.00014069382,0.0006994504,0.004673692,0.00055415067,0.004001736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9489252,0.041845184,0.0019581504,0.0038328208,0.0024306383,0.0010080116],"domain_scores_gemma":[0.33372405,0.61634046,0.007222894,0.034688875,0.005582418,0.0024412526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050972965,0.0020238592,0.0017984299,0.001304785,0.0028406032,0.0038348944,0.003977021,0.0039126747,0.02618955],"category_scores_gemma":[0.33026165,0.0012318575,0.001828136,0.0021174164,0.004700384,0.013426739,0.004424169,0.008468795,0.0036826618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.038126864,0.014892984,0.08604606,0.003686431,0.0024598837,0.0009056576,0.014907368,0.25041822,0.0058290223,0.29985973,0.09162888,0.19123898],"study_design_scores_gemma":[0.003372736,0.0027444637,0.012889654,0.00041282523,0.0005320137,0.00055234245,0.0027139932,0.71133596,0.006314548,0.24432418,0.014556669,0.0002505187],"about_ca_topic_score_codex":0.0035340034,"about_ca_topic_score_gemma":0.0019389432,"teacher_disagreement_score":0.050972965,"about_ca_system_score_codex":0.002653761,"about_ca_system_score_gemma":0.0014056326,"threshold_uncertainty_score":0.26957405},"labels":[],"label_agreement":null},{"id":"W4402669780","doi":"10.18653/v1/2024.gebnlp-1.24","title":"Analysis of Annotator Demographics in Sexism Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Demographics; Computer science; Natural language processing; Sociology; Demography","score_opus":0.007130768271533387,"score_gpt":0.23790384361041692,"score_spread":0.23077307533888353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402669780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8984495,0.0031257342,0.07390855,0.0015438161,0.0010843467,0.00055304763,0.0076315803,0.002059736,0.011643714],"genre_scores_gemma":[0.94940305,0.00050173834,0.029701646,0.00073476235,0.00028604647,0.00061950187,0.01284569,0.00048980274,0.0054177977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98336756,0.010311918,0.0010135775,0.0025880954,0.0021968756,0.0005220949],"domain_scores_gemma":[0.9317903,0.045818523,0.004713808,0.005637473,0.010936514,0.0011034048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025721204,0.0012045456,0.0008306499,0.001905379,0.00191303,0.0018341648,0.0009298455,0.0010544403,0.0017576489],"category_scores_gemma":[0.06948054,0.0004233747,0.0005865456,0.0013690008,0.0008141653,0.0032016311,0.0022695896,0.0014188712,0.002276062],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029063115,0.0005909656,0.63955605,0.001290069,0.000640379,0.0009341535,0.01071258,0.0075861416,0.042070094,0.0018229358,0.03536271,0.25652772],"study_design_scores_gemma":[0.00028323362,0.0012758044,0.5490295,0.00087078003,0.00090899423,0.002773627,0.014872557,0.25036994,0.066488095,0.008198861,0.10452275,0.00040588755],"about_ca_topic_score_codex":0.004735706,"about_ca_topic_score_gemma":0.016030133,"teacher_disagreement_score":0.025721204,"about_ca_system_score_codex":0.00079452107,"about_ca_system_score_gemma":0.0010908705,"threshold_uncertainty_score":0.13602835},"labels":[],"label_agreement":null},{"id":"W4402671660","doi":"10.18653/v1/2024.acl-long.600","title":"Measuring Political Bias in Large Language Models: What Is Said and How It Is Said","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Politics; Computer science; Natural language processing; Political science; Law","score_opus":0.05210208125202521,"score_gpt":0.263815868988379,"score_spread":0.2117137877363538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402671660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54267097,0.0024295517,0.42675316,0.00497771,0.00026780486,0.0003473307,0.0066468124,0.0035968893,0.012309769],"genre_scores_gemma":[0.9470702,0.0003083803,0.04770927,0.00068529276,0.00020705865,0.0002328016,0.0026559718,0.00025478535,0.0008762337],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.993291,0.004025837,0.00042746522,0.0009301833,0.0010432779,0.00028215058],"domain_scores_gemma":[0.9418938,0.046617415,0.003980986,0.0037997267,0.0030831387,0.0006248553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011021603,0.0010466335,0.00085353194,0.0031924967,0.00081410975,0.0038532505,0.0007657533,0.0012478135,0.0024285077],"category_scores_gemma":[0.056700148,0.00048717746,0.00092759664,0.002317437,0.0012180733,0.005487596,0.0022885916,0.0022168485,0.0012426475],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019409521,0.0007797292,0.3947831,0.0015889433,0.0014459039,0.00049793825,0.010018339,0.08012399,0.027125776,0.052928723,0.013882921,0.41488373],"study_design_scores_gemma":[0.00016332851,0.00038200803,0.08159788,0.00041340306,0.00056618504,0.0003548249,0.002123673,0.7006428,0.013861544,0.17961903,0.020028647,0.0002466394],"about_ca_topic_score_codex":0.00414972,"about_ca_topic_score_gemma":0.0056936275,"teacher_disagreement_score":0.011021603,"about_ca_system_score_codex":0.0011450645,"about_ca_system_score_gemma":0.00130039,"threshold_uncertainty_score":0.058288455},"labels":[],"label_agreement":null},{"id":"W4403417275","doi":"10.4337/9781803925738.00007","title":"The definitional and methodological challenges of studying hate crimes: evaluating official statistics, victimization, and self-report data","year":2024,"lang":"en","type":"book-chapter","venue":"Edward Elgar Publishing eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hate crime; Criminology; Psychology; Crime statistics; Political science; Data science; Computer science","score_opus":0.16098094888266887,"score_gpt":0.3344047743211416,"score_spread":0.1734238254384727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403417275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15086259,0.12832245,0.3349526,0.059513863,0.004162811,0.0020539972,0.0066112154,0.00079002074,0.3127305],"genre_scores_gemma":[0.44264257,0.11354177,0.37971458,0.006938127,0.0024041177,0.0042283107,0.005928557,0.00076364825,0.043838307],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9541465,0.029523665,0.002592249,0.0012511547,0.011994801,0.0004917334],"domain_scores_gemma":[0.81169844,0.1601935,0.0059013437,0.006622564,0.014847561,0.00073659694],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.035231356,0.000575659,0.0012708966,0.007442305,0.0017364657,0.013149415,0.0025016514,0.0013976183,0.0024863582],"category_scores_gemma":[0.14095575,0.00067209016,0.00046423383,0.012775282,0.006550448,0.0057822117,0.003084726,0.0021693355,0.0010920458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029322946,0.00016433481,0.07757715,0.0022379938,0.00009984325,0.00030395214,0.020324174,0.0015153409,0.0007742196,0.2604949,0.054733142,0.5817456],"study_design_scores_gemma":[0.000019659961,0.00033604878,0.19464773,0.014407027,0.00015752489,0.0025101174,0.10035733,0.008614731,0.004693566,0.22260153,0.45139655,0.00025824388],"about_ca_topic_score_codex":0.017033657,"about_ca_topic_score_gemma":0.034147833,"teacher_disagreement_score":0.96476865,"about_ca_system_score_codex":0.0034380925,"about_ca_system_score_gemma":0.006450535,"threshold_uncertainty_score":0.1863234},"labels":[],"label_agreement":null},{"id":"W4403534815","doi":"10.1109/codit62066.2024.10708284","title":"Sexism Discovery using CNN, Word Embeddings, NLP and Data Augmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton; Université Laval","funders":"","keywords":"Natural language processing; Computer science; Artificial intelligence; Word (group theory); Word-sense disambiguation; Speech recognition; Linguistics; WordNet","score_opus":0.046735851483566695,"score_gpt":0.3241419398995877,"score_spread":0.27740608841602105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403534815","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4614216,0.003549573,0.49073118,0.0027567032,0.0008652796,0.00062717026,0.009380574,0.008233689,0.022434192],"genre_scores_gemma":[0.80029994,0.00086878217,0.17894843,0.0005521515,0.0001970286,0.0002597835,0.011626689,0.0001244069,0.007122781],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929595,0.00019012792,0.00005990239,0.00020542364,0.0001787431,0.00006983291],"domain_scores_gemma":[0.9982888,0.0006825247,0.00024316447,0.00048791894,0.00023572173,0.0000618799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009165948,0.0011281117,0.00048670312,0.0016824483,0.00044219336,0.0008961342,0.0007202431,0.00072914816,0.0022958806],"category_scores_gemma":[0.0041713873,0.00023688492,0.00061620405,0.0010017658,0.00058563874,0.0021642775,0.0018950079,0.0013749682,0.0013663794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000416096,0.00045805253,0.027641222,0.0004662175,0.00015524487,0.00089138333,0.0004998588,0.013588836,0.02203764,0.0052433414,0.013091158,0.91551095],"study_design_scores_gemma":[0.000071254464,0.00053320325,0.025027024,0.0002688523,0.0001950687,0.0018928882,0.0017607558,0.8126241,0.05958106,0.050316803,0.047610275,0.000118767915],"about_ca_topic_score_codex":0.0020031063,"about_ca_topic_score_gemma":0.0048379037,"teacher_disagreement_score":0.0022958806,"about_ca_system_score_codex":0.0003774351,"about_ca_system_score_gemma":0.0006985142,"threshold_uncertainty_score":0.0076804757},"labels":[],"label_agreement":null},{"id":"W4403565706","doi":"10.21275/sr24724150350","title":"Real-Time Content Moderation Using Artificial Intelligence and Machine Learning","year":2021,"lang":"en","type":"article","venue":"International Journal of Science and Research (IJSR)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Moderation; Content (measure theory); Computer science; Artificial intelligence; Machine learning; Psychology; Mathematics","score_opus":0.17427628389857697,"score_gpt":0.38140352744548217,"score_spread":0.2071272435469052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403565706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17731014,0.0015766014,0.7946473,0.0015851422,0.0003053838,0.0007190252,0.00053502095,0.009266546,0.014054877],"genre_scores_gemma":[0.7196554,0.00042906994,0.27513516,0.00031204388,0.00027968964,0.0003100302,0.00038555087,0.00026033047,0.0032326877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545753,0.0021722964,0.00020092228,0.0008094898,0.0011363856,0.0002234863],"domain_scores_gemma":[0.97501457,0.016088981,0.00325846,0.0026018007,0.00234716,0.00068912166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048762206,0.0006805336,0.00085552677,0.003107739,0.00078317604,0.002323179,0.0008350776,0.00094830454,0.002146703],"category_scores_gemma":[0.024943048,0.00036003857,0.00042860603,0.0015602346,0.0009860087,0.0034984464,0.0024280006,0.0016379248,0.0017002643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010074407,0.0005223733,0.037073277,0.00061341765,0.00018308075,0.00029644475,0.0030948133,0.01115832,0.049227793,0.0094825905,0.0050599556,0.8822806],"study_design_scores_gemma":[0.000097197175,0.0010050386,0.058490336,0.00029413184,0.00015591427,0.0006369944,0.002598181,0.72451866,0.09929042,0.06705358,0.045592975,0.00026669973],"about_ca_topic_score_codex":0.00075476733,"about_ca_topic_score_gemma":0.0010426209,"teacher_disagreement_score":0.0048762206,"about_ca_system_score_codex":0.00064790784,"about_ca_system_score_gemma":0.000679116,"threshold_uncertainty_score":0.025788248},"labels":[],"label_agreement":null},{"id":"W4403577338","doi":"10.1145/3627673.3679974","title":"Osprey 🪶: A Reference Framework for Online Grooming Detection via Neural Models and Conversation Features","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Conversation; Computer science; Communication; Psychology","score_opus":0.028759875902245678,"score_gpt":0.2747591403637519,"score_spread":0.2459992644615062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403577338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030499157,0.00039510545,0.9722516,0.00011919648,0.000112190006,0.000058560858,0.00154347,0.020487275,0.001982779],"genre_scores_gemma":[0.21205002,0.0010070057,0.7567671,0.00032675112,0.00026194163,0.00038084236,0.011116745,0.0032060249,0.014883568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916935,0.00017217235,0.000036216716,0.00024530533,0.00028847193,0.00008852308],"domain_scores_gemma":[0.9989548,0.00020823536,0.000056566434,0.00034359883,0.0003766152,0.0000601521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015837981,0.0016068997,0.0014435381,0.0016529307,0.00076280796,0.0018837613,0.004112555,0.0019719978,0.007229837],"category_scores_gemma":[0.0047601964,0.000736611,0.0013872822,0.0012127581,0.00048159715,0.0026643532,0.0021138417,0.0019477106,0.005777424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005614955,0.00027206217,0.0017752952,0.00029765454,0.00031880903,0.00018841495,0.00011452983,0.17150441,0.0070499056,0.028650455,0.06200226,0.7272647],"study_design_scores_gemma":[0.000015164596,0.00004996327,0.00033725117,0.000021177562,0.000036391717,0.00006022871,0.000013045811,0.9701656,0.0033196667,0.015956108,0.009998069,0.000027396367],"about_ca_topic_score_codex":0.028257355,"about_ca_topic_score_gemma":0.032266125,"teacher_disagreement_score":0.028257355,"about_ca_system_score_codex":0.0008938229,"about_ca_system_score_gemma":0.0019091361,"threshold_uncertainty_score":0.056185782},"labels":[],"label_agreement":null},{"id":"W4403579775","doi":"10.48550/arxiv.2410.13480","title":"Broken Windows: Exploring the Applicability of a Controversial Theory on Code Quality","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; Natural Sciences and Engineering Research Council of Canada; European Commission; Athens University of Economics and Business","keywords":"Code (set theory); Quality (philosophy); Computer science; Programming language; Epistemology; Philosophy","score_opus":0.10642681773307179,"score_gpt":0.21387977553922752,"score_spread":0.10745295780615573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403579775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85770977,0.0011964756,0.12404581,0.0061587966,0.00008467328,0.00012358511,0.0002890224,0.000119230725,0.010272663],"genre_scores_gemma":[0.9902986,0.00014449033,0.008768306,0.00018419749,0.000061535844,0.00008924725,0.0000996023,0.000056621502,0.0002973635],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9807389,0.010385393,0.00076786196,0.0044592,0.0030182314,0.0006304392],"domain_scores_gemma":[0.50981414,0.41313183,0.03846484,0.024397323,0.011077225,0.0031147166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03807206,0.0007213428,0.0010377053,0.0057423105,0.0021373578,0.006058573,0.0024494054,0.0019221817,0.0041561946],"category_scores_gemma":[0.30243608,0.00071021536,0.0011398434,0.006343179,0.012629563,0.013200921,0.006212522,0.0047227647,0.00036748443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006998032,0.0003171148,0.6437125,0.0003530014,0.0005228189,0.00047730852,0.038523532,0.005144324,0.0014416054,0.21344997,0.0017459454,0.093612015],"study_design_scores_gemma":[0.00013947774,0.0007306056,0.32635477,0.00037953595,0.00031047745,0.0005680341,0.0171546,0.12194106,0.0017416881,0.525663,0.004846321,0.00017036645],"about_ca_topic_score_codex":0.007848118,"about_ca_topic_score_gemma":0.003399855,"teacher_disagreement_score":0.03807206,"about_ca_system_score_codex":0.0029652668,"about_ca_system_score_gemma":0.0018565542,"threshold_uncertainty_score":0.20134676},"labels":[],"label_agreement":null},{"id":"W4403784918","doi":"10.1080/10511431.2024.2419747","title":"Understanding fallacious reasoning <i>via</i> detecting moves of motivated criticism in Argument Continuity—a new strategy of circular reasoning in coping responses to disagreement within a distorted reasoning context","year":2024,"lang":"en","type":"article","venue":"Argumentation and Advocacy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Epistemology; Analytic reasoning; Argumentation theory; Argument (complex analysis); Criticism; Psychology; Context (archaeology); Coping (psychology); Verbal reasoning; Deductive reasoning; Social psychology; Philosophy; Cognition; Law","score_opus":0.035246680906485986,"score_gpt":0.2840686886128992,"score_spread":0.24882200770641322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403784918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21926439,0.000636206,0.7333419,0.006042882,0.00018212524,0.00048433224,0.00067769294,0.0016590958,0.037711434],"genre_scores_gemma":[0.7334667,0.00017172452,0.2620956,0.00034770984,0.000059853177,0.00032015305,0.00045397447,0.00029547923,0.0027887349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9859337,0.007690932,0.00094362063,0.0028943606,0.002025938,0.0005114034],"domain_scores_gemma":[0.92258024,0.04793829,0.012881749,0.011236964,0.004354269,0.0010084826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016324263,0.0008014815,0.00054707297,0.008288697,0.0031226748,0.011915252,0.0026915965,0.0028074353,0.003537098],"category_scores_gemma":[0.0763956,0.00071138726,0.00080972194,0.0044318186,0.012785998,0.014643048,0.008458511,0.003084796,0.0009008376],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032453839,0.0001499277,0.06984552,0.00097245205,0.00010867627,0.0017458356,0.32605466,0.0035291428,0.018823126,0.35023957,0.005822364,0.22238407],"study_design_scores_gemma":[0.000047810787,0.00012144336,0.048987765,0.0012279922,0.00014050027,0.0023100744,0.117236555,0.0863949,0.02144174,0.59928614,0.1225054,0.00029976762],"about_ca_topic_score_codex":0.004421591,"about_ca_topic_score_gemma":0.00559054,"teacher_disagreement_score":0.016324263,"about_ca_system_score_codex":0.002988832,"about_ca_system_score_gemma":0.0032560518,"threshold_uncertainty_score":0.08633202},"labels":[],"label_agreement":null},{"id":"W4404078544","doi":"10.1080/17467586.2024.2407922","title":"Linguistic models of abusive language","year":2024,"lang":"en","type":"article","venue":"Dynamics of Asymmetric Conflict","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Linguistics; Sociology; Psychology; Philosophy","score_opus":0.010959513072492181,"score_gpt":0.24978319288396456,"score_spread":0.23882367981147237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404078544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75108,0.001498489,0.19461744,0.0048443256,0.00021037052,0.00015841512,0.0018021035,0.0009571059,0.044831805],"genre_scores_gemma":[0.9847564,0.00026107195,0.008657024,0.00010794945,0.00009082006,0.000058523034,0.00048592887,0.00006324537,0.0055189845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999213,0.00035283927,0.00003979365,0.00014330982,0.00013681345,0.00011415798],"domain_scores_gemma":[0.9927169,0.004863104,0.00089854037,0.00038742973,0.0008263047,0.0003077671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019638042,0.00068913883,0.00037413143,0.002451177,0.0009550556,0.0025723088,0.00096041284,0.0007999296,0.0075936094],"category_scores_gemma":[0.011045731,0.000460719,0.000702154,0.0012228854,0.0012017098,0.002589921,0.0010876998,0.0018108039,0.001738866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007183805,0.00069885777,0.22161517,0.00039411706,0.0004909375,0.001549387,0.009927692,0.29485923,0.009343885,0.22952071,0.01514469,0.21573687],"study_design_scores_gemma":[0.000015342517,0.00005408261,0.021392692,0.00006162319,0.000058095262,0.00022345416,0.0005701734,0.9108345,0.0005100946,0.063517086,0.0027261819,0.000036646303],"about_ca_topic_score_codex":0.009139112,"about_ca_topic_score_gemma":0.008521308,"teacher_disagreement_score":0.009139112,"about_ca_system_score_codex":0.0010809669,"about_ca_system_score_gemma":0.0009898442,"threshold_uncertainty_score":0.025403142},"labels":[],"label_agreement":null},{"id":"W4404088216","doi":"10.1145/3700794.3700802","title":"A Civics-oriented Approach to Understanding Intersectionally Marginalized Users' Experience with Hate Speech Online","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Civics; Moderation; Ethnic group; Indigenous; Metaphor; Citizenship; Sociology; Internet privacy; Qualitative research; Computer science; Public relations; Political science; Social psychology; Psychology; Pedagogy; Linguistics; Social science","score_opus":0.04557319063069297,"score_gpt":0.26996761551086873,"score_spread":0.22439442488017575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404088216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92814195,0.00032731245,0.013814462,0.0024011168,0.00003203567,0.00017046281,0.000088655506,0.00002140936,0.055002622],"genre_scores_gemma":[0.99748874,0.00010887516,0.00080868806,0.00010508255,0.0000049087007,0.00009173061,0.000015915266,0.0000065526756,0.0013694825],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9973501,0.002047656,0.000060904244,0.00016604016,0.00013521298,0.00024020195],"domain_scores_gemma":[0.99613935,0.0026746865,0.00044660742,0.0002654388,0.00019801526,0.00027585297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032950304,0.00041463677,0.0003090538,0.0021946377,0.006447278,0.005808295,0.0008238927,0.0011802837,0.0050326837],"category_scores_gemma":[0.00483262,0.000252932,0.00017677025,0.0017324397,0.016942855,0.007022484,0.006050667,0.0019379135,0.00025242657],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014678003,0.000032602606,0.006042168,0.00006258987,0.000003535803,0.0002451259,0.95415694,0.000038042333,0.0008890479,0.034946103,0.00013293119,0.0034363028],"study_design_scores_gemma":[0.0000026499595,0.000026093798,0.0052551213,0.00007467644,0.000003937212,0.00012109998,0.9774754,0.00013733686,0.00034896604,0.007501191,0.009046266,0.000007318825],"about_ca_topic_score_codex":0.0044810213,"about_ca_topic_score_gemma":0.0074299793,"teacher_disagreement_score":0.006447278,"about_ca_system_score_codex":0.0030074338,"about_ca_system_score_gemma":0.001948841,"threshold_uncertainty_score":0.021820486},"labels":[],"label_agreement":null},{"id":"W4404088409","doi":"10.1145/3686926","title":"The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Universitas Brawijaya","keywords":"Sociology; Deliberation; Politics; Indigenous; Social psychology; Gender studies; Psychology; Political science; Law","score_opus":0.046501669837578814,"score_gpt":0.30444959556034834,"score_spread":0.25794792572276953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404088409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98520654,0.00043114816,0.00022663429,0.003047538,0.00004155747,0.000017532668,0.000027902997,0.000009858393,0.010991321],"genre_scores_gemma":[0.9982906,0.0002629332,0.00005984943,0.00037192364,0.0000151436825,0.000016443157,0.000009633863,0.0000049168743,0.0009685747],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99694186,0.00201062,0.000101627105,0.00015077808,0.00035225294,0.00044281356],"domain_scores_gemma":[0.9942257,0.003124894,0.0010651342,0.00022565055,0.00032287766,0.0010358318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003104222,0.00047265753,0.00039061054,0.001337757,0.011931453,0.0069533437,0.0007672785,0.001843212,0.0042658052],"category_scores_gemma":[0.008358434,0.00037798638,0.0003015599,0.00089742534,0.009080128,0.004365691,0.0076322607,0.002845858,0.00044477746],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004399764,0.000036265374,0.012999547,0.0000555818,0.0000101187425,0.00046306485,0.9773293,0.00001708819,0.000656905,0.0019645097,0.00057271903,0.005850973],"study_design_scores_gemma":[0.000003157036,0.00003373451,0.0051550944,0.00006623518,0.0000070421665,0.00016356823,0.98778754,0.000037857077,0.00009771101,0.0004235182,0.0062086987,0.000015931182],"about_ca_topic_score_codex":0.016305415,"about_ca_topic_score_gemma":0.020879716,"teacher_disagreement_score":0.016305415,"about_ca_system_score_codex":0.0025589545,"about_ca_system_score_gemma":0.0018149759,"threshold_uncertainty_score":0.032420993},"labels":[],"label_agreement":null},{"id":"W4404331838","doi":"10.29173/irie528","title":"Exploring Gender Bias in Search Engines","year":2024,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Search engine; Information retrieval; Computer science; Psychology","score_opus":0.274491186202332,"score_gpt":0.3683936572425915,"score_spread":0.0939024710402595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404331838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9436803,0.0008886169,0.0057436624,0.003551699,0.00005832537,0.000041214927,0.00006562102,0.000026868782,0.045943845],"genre_scores_gemma":[0.9969746,0.00018570489,0.0006377439,0.00028849317,0.000013653216,0.0000123953,0.000017686714,0.000013085119,0.0018565964],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9892944,0.007902057,0.00021859985,0.00042703142,0.0015422023,0.0006156165],"domain_scores_gemma":[0.9688542,0.02602929,0.0023698744,0.0009501533,0.001327823,0.00046867345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009115833,0.00027237475,0.00024000999,0.0013466444,0.0033641737,0.004681438,0.00048897165,0.0013508513,0.0031404556],"category_scores_gemma":[0.032207005,0.0001721881,0.00019845724,0.00091504777,0.004091278,0.0051133535,0.0030006317,0.0008980775,0.00048800386],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003599058,0.00012071861,0.11296733,0.00035269864,0.000048226626,0.0023668513,0.7257414,0.00055313116,0.005267129,0.06870283,0.0050830473,0.078436814],"study_design_scores_gemma":[0.000042987747,0.0003399429,0.048757542,0.0006564736,0.000069708294,0.00420291,0.7456021,0.0056007598,0.0069994237,0.045117054,0.14248997,0.00012114346],"about_ca_topic_score_codex":0.0027277325,"about_ca_topic_score_gemma":0.004163102,"teacher_disagreement_score":0.009115833,"about_ca_system_score_codex":0.0018014696,"about_ca_system_score_gemma":0.0011974924,"threshold_uncertainty_score":0.048209667},"labels":[],"label_agreement":null},{"id":"W4404444646","doi":"10.1007/s10982-024-09508-1","title":"Hate-Speech Bans are at Odds with Central Principles of Liberalism","year":2024,"lang":"en","type":"article","venue":"Law and Philosophy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political philosophy; Liberalism; Philosophy of law; Odds; Political science; Law; Free speech; Law and economics; Sociology; Criminology; Politics; Medicine; Comparative law; Logistic regression","score_opus":0.016169335032331415,"score_gpt":0.20712094403960252,"score_spread":0.1909516090072711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404444646","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14914432,0.0012651955,0.28733134,0.063933246,0.00053695886,0.00015788805,0.00012513806,0.00035682973,0.49714914],"genre_scores_gemma":[0.98195136,0.00022454117,0.0076010725,0.0023620354,0.0001686213,0.00006859021,0.000024417395,0.0000560264,0.007543485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9797335,0.008923093,0.00071123254,0.0026082518,0.0066988454,0.0013250009],"domain_scores_gemma":[0.92414916,0.05053302,0.00655098,0.011674487,0.0053584916,0.0017338877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014893325,0.00037036964,0.0007576239,0.0013686456,0.0041546635,0.011335349,0.0013228523,0.0039706365,0.006784721],"category_scores_gemma":[0.057788733,0.00045430835,0.00051894167,0.0010250884,0.05174335,0.012719305,0.0051938603,0.009971606,0.001237111],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019254261,0.000024165827,0.0016030152,0.00003385008,0.000006723843,0.00005669303,0.003997852,0.0002490861,0.00033605992,0.9820869,0.0016592419,0.009927264],"study_design_scores_gemma":[0.000008745384,0.000018164581,0.0013046825,0.000093565366,0.00000710447,0.00015551121,0.0024089464,0.0017858687,0.00093898387,0.9779849,0.01527316,0.000020260533],"about_ca_topic_score_codex":0.0015817786,"about_ca_topic_score_gemma":0.0018216348,"teacher_disagreement_score":0.014893325,"about_ca_system_score_codex":0.0028286993,"about_ca_system_score_gemma":0.002685103,"threshold_uncertainty_score":0.07876438},"labels":[],"label_agreement":null},{"id":"W4404558508","doi":"10.1515/ijdlg-2024-0013","title":"Tackling Hate Speech in the Digital Space: Germany’s Plans on an Act Against Digital Violence and its Impact on Ethno-Cultural Minorities","year":2024,"lang":"en","type":"article","venue":"International Journal of Digital Law and Governance","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Safeguarding; Political science; Human rights; Legislature; Government (linguistics); Obligation; Public relations; Law; Sociology","score_opus":0.009204791398131252,"score_gpt":0.26830097227681654,"score_spread":0.2590961808786853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404558508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.845632,0.0021612665,0.003840775,0.06644601,0.00056484295,0.00011624932,0.00008477967,0.000041570995,0.08111252],"genre_scores_gemma":[0.98779386,0.000657987,0.000564655,0.0036991378,0.00003839401,0.000025405585,0.000022697965,0.0000102783615,0.0071876026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9955108,0.0018485265,0.00014763545,0.00031252517,0.00092161703,0.0012589555],"domain_scores_gemma":[0.9948173,0.0024914357,0.00056625536,0.00035794755,0.00063264766,0.0011343473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0080186855,0.00030912543,0.00021769381,0.0012576154,0.006762807,0.0067496267,0.00082874857,0.0034668848,0.0022064764],"category_scores_gemma":[0.007150909,0.00020986567,0.0003607673,0.0007421931,0.009728558,0.0035849803,0.0065557593,0.003393429,0.00039197752],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014081215,0.0003687947,0.06344566,0.00033510107,0.000056397395,0.0050317366,0.23326689,0.0016425292,0.0031256024,0.54015154,0.026811529,0.12562346],"study_design_scores_gemma":[0.000041214233,0.00040677187,0.093280435,0.0018355788,0.00013277792,0.0018090735,0.45122114,0.0032784825,0.006849399,0.040021118,0.40091124,0.00021274654],"about_ca_topic_score_codex":0.06560102,"about_ca_topic_score_gemma":0.080534846,"teacher_disagreement_score":0.06560102,"about_ca_system_score_codex":0.006791746,"about_ca_system_score_gemma":0.011199022,"threshold_uncertainty_score":0.13043839},"labels":[],"label_agreement":null},{"id":"W4404644149","doi":"10.1007/978-3-031-75813-3_11","title":"Conclusion and a Way Forward","year":2024,"lang":"en","type":"book-chapter","venue":"Law, governance and technology series","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science","score_opus":0.004052232342682685,"score_gpt":0.18513676617672228,"score_spread":0.1810845338340396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404644149","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008520444,0.00862407,0.0030131899,0.8483622,0.042257447,0.00004377326,0.00014321142,0.00007368511,0.096630275],"genre_scores_gemma":[0.048859917,0.01132895,0.0056074047,0.55256224,0.019654378,0.0002549112,0.0003134989,0.0002012529,0.36121747],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949504,0.0019017374,0.000163682,0.0006766506,0.0013866668,0.0009208672],"domain_scores_gemma":[0.9902092,0.002969225,0.00022337318,0.0007219085,0.0040718806,0.0018044022],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0068497118,0.00092478155,0.00095732673,0.001724248,0.0058809128,0.012231413,0.0030598072,0.015968215,0.069986686],"category_scores_gemma":[0.018525971,0.00027183926,0.0011790601,0.0011422677,0.008242308,0.017033225,0.0074754492,0.015177163,0.020810056],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038824615,0.00004591494,0.000092941365,0.000103681705,0.000008832406,0.00007973636,0.0004854258,0.0000898616,0.00010523915,0.5072318,0.47663328,0.0150844995],"study_design_scores_gemma":[0.000025809073,0.000018149116,0.00015057896,0.00042166142,0.00001113769,0.000041458807,0.0035516599,0.0001682825,0.00014765312,0.28872517,0.7067194,0.000019143456],"about_ca_topic_score_codex":0.009092282,"about_ca_topic_score_gemma":0.011412796,"teacher_disagreement_score":0.9300133,"about_ca_system_score_codex":0.0059709465,"about_ca_system_score_gemma":0.012272962,"threshold_uncertainty_score":0.23412877},"labels":[],"label_agreement":null},{"id":"W4404644151","doi":"10.1007/978-3-031-75813-3","title":"Free Speech in the Puzzle of Content Regulation","year":2024,"lang":"en","type":"book","venue":"Law, governance and technology series","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Content (measure theory); Speech recognition; Free speech; Linguistics; Computer science; Psychology; Political science; Mathematics; Philosophy; Law","score_opus":0.010255190977480047,"score_gpt":0.19608571315555678,"score_spread":0.18583052217807675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404644151","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02045661,0.027495893,0.03165155,0.070019305,0.0013474015,0.000033756478,0.00018884822,0.00025609613,0.84855056],"genre_scores_gemma":[0.5966841,0.01172983,0.006439607,0.012460596,0.002909251,0.00011692335,0.00016128962,0.00031051046,0.36918798],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984971,0.0006422857,0.000037808128,0.00015067798,0.0005570828,0.000115030234],"domain_scores_gemma":[0.9912111,0.007416351,0.00027315278,0.0004329039,0.00048339355,0.00018295812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015415039,0.0003115661,0.00037574852,0.0009915758,0.0020877179,0.007216775,0.0006735974,0.0032609024,0.009757049],"category_scores_gemma":[0.010009053,0.00027550463,0.00016553183,0.001101769,0.011776051,0.00731102,0.0012347925,0.0031085843,0.0018743254],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010528874,0.000015842292,0.00021371801,0.000036072557,0.0000031527093,0.00003297644,0.001375548,0.00025337902,0.00022290974,0.9245317,0.039592195,0.03371197],"study_design_scores_gemma":[0.0000064239216,0.000009824069,0.0009538318,0.00014064393,0.0000056722165,0.00007697536,0.0012210009,0.0011345684,0.000660897,0.822288,0.17348929,0.000012956943],"about_ca_topic_score_codex":0.0034953973,"about_ca_topic_score_gemma":0.004603461,"teacher_disagreement_score":0.009757049,"about_ca_system_score_codex":0.0019208695,"about_ca_system_score_gemma":0.0020508773,"threshold_uncertainty_score":0.032640576},"labels":[],"label_agreement":null},{"id":"W4404644180","doi":"10.1007/978-3-031-75813-3_2","title":"The Evolution of Free Speech in the Era of Social Media","year":2024,"lang":"en","type":"book-chapter","venue":"Law, governance and technology series","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Social media; Free speech; History; Sociology; Political science; Computer science; World Wide Web; Law","score_opus":0.004923495314971253,"score_gpt":0.18833266987234168,"score_spread":0.18340917455737044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404644180","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07877335,0.016246108,0.011405019,0.038587198,0.00079702475,0.00003322565,0.0002465343,0.00010755813,0.853804],"genre_scores_gemma":[0.9124346,0.007135376,0.0024723778,0.0023738665,0.001007893,0.00004397332,0.00009105603,0.00013462522,0.07430626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982583,0.0008313706,0.000045304558,0.00023679581,0.00045262394,0.00017555381],"domain_scores_gemma":[0.9927725,0.005566147,0.00036313545,0.0004128996,0.00052628457,0.00035909502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025253068,0.0002545085,0.00021322396,0.0019752993,0.0023034697,0.009911611,0.00061998697,0.0026983544,0.009642677],"category_scores_gemma":[0.008388156,0.00028032696,0.00018890495,0.0017196744,0.016504498,0.009694861,0.0021813046,0.003130141,0.0011138163],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022905853,0.000020027064,0.0010077163,0.000030487838,0.000004238874,0.00008998331,0.013354573,0.00022624843,0.00026612706,0.9328141,0.008206876,0.043956753],"study_design_scores_gemma":[0.000010334708,0.000041096304,0.010488581,0.00037183386,0.000008781925,0.00039913127,0.013894122,0.0017333777,0.0011034698,0.5468157,0.42509508,0.000038560996],"about_ca_topic_score_codex":0.0049041444,"about_ca_topic_score_gemma":0.005851175,"teacher_disagreement_score":0.009911611,"about_ca_system_score_codex":0.003937731,"about_ca_system_score_gemma":0.0016475071,"threshold_uncertainty_score":0.032258034},"labels":[],"label_agreement":null},{"id":"W4404725608","doi":"10.1177/13634615241296308","title":"Distort, post, repeat: Laundering antisemitism on “cliquey networks” during COVID-19","year":2024,"lang":"en","type":"article","venue":"Transcultural Psychiatry","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Agence Nationale de la Recherche","keywords":"Blame; Social media; Antisemitism; Coronavirus disease 2019 (COVID-19); Criminology; Scripting language; Law; Sociology; Social network (sociolinguistics); Racism; The Internet; Media studies; Political science; Internet privacy; History; Social psychology; Psychology; Computer science","score_opus":0.010355212507361676,"score_gpt":0.2529711616383758,"score_spread":0.2426159491310141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404725608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9689724,0.00013580137,0.0018360176,0.0017581413,0.000062803294,0.000027609438,0.000031341344,0.000047274578,0.02712854],"genre_scores_gemma":[0.9964869,0.00005443648,0.00031184178,0.00019045372,0.000020128824,0.000007760345,0.000015653444,0.000019549478,0.0028933685],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9972994,0.0019318515,0.00003608769,0.00017412077,0.0002961055,0.00026242627],"domain_scores_gemma":[0.99467826,0.0026960303,0.0011096521,0.0005108636,0.00040361178,0.00060162245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020348993,0.00030619316,0.00017759774,0.0010442466,0.0073962477,0.0037031374,0.00045362936,0.0009162578,0.0033268393],"category_scores_gemma":[0.008930094,0.00023697078,0.00013448438,0.00043366433,0.0074030003,0.0033863813,0.003566728,0.0015468583,0.00060829445],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008995322,0.00007870515,0.023130154,0.000052232153,0.000013946191,0.0016557628,0.9224824,0.00009216186,0.0037232342,0.019728769,0.0032592106,0.025693605],"study_design_scores_gemma":[0.000012445533,0.00019361828,0.04488148,0.00017285332,0.000027173512,0.0020401778,0.8467291,0.0014989604,0.003676685,0.007278823,0.09342224,0.00006637931],"about_ca_topic_score_codex":0.006329108,"about_ca_topic_score_gemma":0.0109919235,"teacher_disagreement_score":0.0073962477,"about_ca_system_score_codex":0.0015199541,"about_ca_system_score_gemma":0.00071293506,"threshold_uncertainty_score":0.012584567},"labels":[],"label_agreement":null},{"id":"W4404781178","doi":"10.18653/v1/2024.findings-emnlp.924","title":"Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Misinformation; Harm; Task (project management); Internet privacy; Social media; Computer science; Political science; Computer security; Engineering; Law; World Wide Web","score_opus":0.012408066400300833,"score_gpt":0.24480782944584872,"score_spread":0.23239976304554788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404781178","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25843012,0.022366075,0.56310713,0.04830559,0.0026885774,0.0032461963,0.016476836,0.011905389,0.07347415],"genre_scores_gemma":[0.6547125,0.0041116774,0.3096457,0.0061916322,0.001523156,0.0006348934,0.013487023,0.0008525828,0.008840776],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9823268,0.008776549,0.0011282458,0.0027346548,0.004593249,0.0004405393],"domain_scores_gemma":[0.9255624,0.04408739,0.006811377,0.01473273,0.006815679,0.001990447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011173889,0.0020095464,0.0012764053,0.009007152,0.0034657896,0.008018648,0.0027308876,0.005195235,0.0035527318],"category_scores_gemma":[0.057073195,0.00059482706,0.0012171813,0.0031616616,0.0054585575,0.013384258,0.008294013,0.004545857,0.0015029623],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006877407,0.00075235113,0.07468282,0.0047534904,0.0005358577,0.0015793659,0.0101745585,0.017107224,0.011198459,0.086542785,0.11179825,0.6801871],"study_design_scores_gemma":[0.00016305916,0.0008515341,0.05106231,0.0032266553,0.0005584667,0.006381346,0.016703859,0.20252518,0.044584125,0.30556315,0.36765778,0.00072244985],"about_ca_topic_score_codex":0.007499475,"about_ca_topic_score_gemma":0.009435153,"teacher_disagreement_score":0.011173889,"about_ca_system_score_codex":0.0026428164,"about_ca_system_score_gemma":0.004498164,"threshold_uncertainty_score":0.059093893},"labels":[],"label_agreement":null},{"id":"W4404984766","doi":"10.1007/978-3-031-78119-3_8","title":"MuLAD: Multimodal Aggression Detection from Social Media Memes Exploiting Visual and Textual Features","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Social media; Artificial intelligence; Aggression; Natural language processing; Information retrieval; World Wide Web; Psychology","score_opus":0.012080476133708684,"score_gpt":0.24719598628816164,"score_spread":0.23511551015445295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404984766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2237388,0.0058665364,0.4914299,0.0008218931,0.002363478,0.00210428,0.060208254,0.18480845,0.028658397],"genre_scores_gemma":[0.4136287,0.0012600229,0.47942284,0.0006842079,0.0006381778,0.0021250611,0.048830096,0.0019224912,0.05148837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995591,0.00006987922,0.000019341367,0.00012071225,0.00016500228,0.00006597224],"domain_scores_gemma":[0.9995652,0.0001605437,0.000037672904,0.000071335475,0.00010759006,0.000057700294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005064865,0.0016698836,0.0013038714,0.0026847343,0.0005509145,0.0011392497,0.0011227609,0.0009725356,0.009191201],"category_scores_gemma":[0.0010571233,0.00036111363,0.00074406655,0.0009355369,0.0002366964,0.0010469162,0.0021270378,0.00052380917,0.007869746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011963551,0.00058258395,0.007402346,0.00064785744,0.00030308744,0.00047376924,0.0002681091,0.0026899336,0.0711859,0.0009793893,0.10500647,0.8092641],"study_design_scores_gemma":[0.0003056044,0.0016010548,0.05532478,0.00025054422,0.00048969197,0.0029856625,0.0013904814,0.69446915,0.13847242,0.008345809,0.095994584,0.00037030454],"about_ca_topic_score_codex":0.0026080627,"about_ca_topic_score_gemma":0.0059134536,"teacher_disagreement_score":0.009191201,"about_ca_system_score_codex":0.0002991026,"about_ca_system_score_gemma":0.00031149603,"threshold_uncertainty_score":0.030747652},"labels":[],"label_agreement":null},{"id":"W4405000972","doi":"10.1515/9780776636429-015","title":"CHAPTER B-4 Does Debunking Work? Correcting COVID-19 Misinformation on Social Media","year":2020,"lang":"en","type":"book-chapter","venue":"University of Ottawa Press eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Alberta Innovates; Government of Canada; Ministero dello Sviluppo Economico; Government of Alberta","keywords":"Misinformation; Social media; Coronavirus disease 2019 (COVID-19); Psychology; Internet privacy; Computer science; Sociology; Medicine; World Wide Web; Computer security; Internal medicine","score_opus":0.03988212445818131,"score_gpt":0.2149391235598386,"score_spread":0.17505699910165728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405000972","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003987323,0.060862266,0.0060463115,0.04318624,0.006273295,0.00018002419,0.00044249618,0.0003368797,0.8786852],"genre_scores_gemma":[0.03409365,0.048338026,0.006426878,0.011131564,0.0021530404,0.00016237816,0.00044791313,0.00030366873,0.89694285],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996799,0.00010971462,0.000011429593,0.00005105032,0.00011009111,0.000037752114],"domain_scores_gemma":[0.9990201,0.00051406375,0.000054206736,0.000057127843,0.00024405889,0.000110498135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006773133,0.00056319416,0.00030216802,0.0008357076,0.0016575784,0.003634593,0.0008038143,0.0020340628,0.06525554],"category_scores_gemma":[0.0023148123,0.00021358879,0.00033826727,0.0008587611,0.0012892933,0.003120885,0.0010211816,0.0021213787,0.026976697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033415174,0.00011195334,0.0006403451,0.00059852144,0.000009154842,0.000081977305,0.002927009,0.00021898947,0.00050077855,0.10991163,0.58083636,0.3041299],"study_design_scores_gemma":[0.0000046620357,0.0000413739,0.0009667585,0.00094571366,0.0000075653775,0.00017251626,0.0014350979,0.0001515902,0.00045621907,0.020774743,0.97503406,0.000009779967],"about_ca_topic_score_codex":0.003910345,"about_ca_topic_score_gemma":0.007304569,"teacher_disagreement_score":0.06525554,"about_ca_system_score_codex":0.0015928434,"about_ca_system_score_gemma":0.0019865741,"threshold_uncertainty_score":0.21830153},"labels":[],"label_agreement":null},{"id":"W4405093804","doi":"10.3138/cjc-2023-0033","title":"Perspectives of Canadian Youth on Islamophobic Hate Speech on Social Media","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Sociology; Political science; Media studies","score_opus":0.03234807654974338,"score_gpt":0.24195314368639112,"score_spread":0.20960506713664773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405093804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9838395,0.0007827116,0.00011716207,0.0021479991,0.000069880385,0.000046807763,0.0004111607,0.000008949672,0.012575814],"genre_scores_gemma":[0.99424136,0.0012835748,0.00013005645,0.00072147156,0.000020962936,0.000021016582,0.00012963812,0.000007973375,0.0034439564],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9973621,0.0005031341,0.000067584966,0.00015130542,0.00067522255,0.001240764],"domain_scores_gemma":[0.9955122,0.0005590111,0.00051726954,0.000074107134,0.001777325,0.0015600864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021411516,0.0006140633,0.00052556104,0.002517805,0.01964681,0.004693349,0.0008155505,0.00092882366,0.0033118594],"category_scores_gemma":[0.0032867235,0.000406334,0.0003911076,0.0034370453,0.0043646214,0.0010908559,0.0026977155,0.0022871133,0.0003038319],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007093905,0.00006005907,0.12623224,0.00013009815,0.0000137545485,0.0011025993,0.85224026,0.000036543777,0.0009157775,0.0012231823,0.0040361057,0.013938461],"study_design_scores_gemma":[0.0000025366421,0.000026357027,0.045853302,0.000092233044,0.000012653889,0.00021984025,0.9426223,0.000034959307,0.00016806395,0.00005265178,0.010886915,0.00002816272],"about_ca_topic_score_codex":0.9768014,"about_ca_topic_score_gemma":0.9881295,"teacher_disagreement_score":0.028244385,"about_ca_system_score_codex":0.028244385,"about_ca_system_score_gemma":0.029250195,"threshold_uncertainty_score":0.20492828},"labels":[],"label_agreement":null},{"id":"W4405113473","doi":"10.25306/skad.1446002","title":"Şiddetin Topolojisi","year":2024,"lang":"tr","type":"article","venue":"Sosyal ve Kültürel Araştırmalar Dergisi (SKAD)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Theology; Humanities; Art; Philosophy","score_opus":0.011120037237899014,"score_gpt":0.24264978390697722,"score_spread":0.23152974666907822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113473","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09222711,0.010134383,0.009471375,0.020069888,0.0024566008,0.00017643151,0.0024847218,0.00056351145,0.862416],"genre_scores_gemma":[0.49912563,0.010508809,0.00711906,0.003561301,0.0005553942,0.0001594885,0.0031129268,0.00037103484,0.47548634],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99786645,0.0004051343,0.00008605345,0.00031696886,0.00081390864,0.0005115508],"domain_scores_gemma":[0.9983931,0.00021157356,0.00019364912,0.00016449152,0.0006657524,0.0003713352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013727334,0.0009536022,0.0005277573,0.0014534828,0.0053617167,0.007771019,0.0009324747,0.0017380054,0.084322944],"category_scores_gemma":[0.0024422444,0.00039928482,0.00072769285,0.001800944,0.0026205597,0.0029121437,0.005449776,0.0031661387,0.017540822],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004572731,0.00039348158,0.047172047,0.0014187786,0.00014192895,0.002422308,0.023471419,0.001029849,0.004778504,0.29836315,0.22755067,0.39280057],"study_design_scores_gemma":[0.000014447288,0.00009186772,0.022039173,0.0005267692,0.000038104357,0.0008357535,0.013380846,0.00025278967,0.0016217512,0.014633169,0.94650704,0.000058280573],"about_ca_topic_score_codex":0.021123087,"about_ca_topic_score_gemma":0.03394876,"teacher_disagreement_score":0.084322944,"about_ca_system_score_codex":0.004819472,"about_ca_system_score_gemma":0.006656939,"threshold_uncertainty_score":0.28208834},"labels":[],"label_agreement":null},{"id":"W4405178022","doi":"10.52922/ti77734","title":"Exposure to and sharing of fringe or radical content online","year":2024,"lang":"en","type":"book","venue":"Australian Institute of Criminology eBooks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mainstream; Content (measure theory); Quarter (Canadian coin); The Internet; Internet privacy; Advertising; Psychology; Computer science; World Wide Web; Business; Political science; Geography; Mathematics","score_opus":0.15028428163746188,"score_gpt":0.2976741620312826,"score_spread":0.14738988039382073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405178022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99643,0.00007917667,0.00014123453,0.000102099955,0.0000038013973,0.000050732448,0.00033177604,0.0000043020705,0.002857017],"genre_scores_gemma":[0.99596196,0.00032713808,0.0005340202,0.00015289622,0.000010959305,0.000093649076,0.00035555268,0.000004426958,0.0025594435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990295,0.00035742138,0.00008727644,0.0000899192,0.00030065567,0.00013509017],"domain_scores_gemma":[0.9939713,0.001134571,0.0026270484,0.00048563335,0.0008449439,0.0009364395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012154261,0.00017063004,0.00019263003,0.0016382445,0.0010241781,0.0009190952,0.000509956,0.00045859106,0.0030032948],"category_scores_gemma":[0.00579556,0.00030565943,0.00018068338,0.0011130425,0.00054905325,0.0015083726,0.0014568404,0.00087753474,0.00093516795],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023188464,0.00028124714,0.9510418,0.0000912335,0.000019372377,0.00018287908,0.029283518,0.000026581902,0.0004582556,0.0002068031,0.0009088452,0.01747637],"study_design_scores_gemma":[0.0000010439269,0.000075567135,0.9890182,0.000046373665,0.000007720391,0.00018928999,0.008515134,0.00009170294,0.00010841043,0.00007298656,0.0018649881,0.000008594798],"about_ca_topic_score_codex":0.026384762,"about_ca_topic_score_gemma":0.04236551,"teacher_disagreement_score":0.026384762,"about_ca_system_score_codex":0.0005492001,"about_ca_system_score_gemma":0.0006176426,"threshold_uncertainty_score":0.0524624},"labels":[],"label_agreement":null},{"id":"W4405178788","doi":"10.2139/ssrn.4990476","title":"Politically biased moderation drives echo chamber formation: An analysis of user-driven content removals on Reddit","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Moderation; Echo (communications protocol); Computer science; Computer security","score_opus":0.028433912262476452,"score_gpt":0.27464244058832143,"score_spread":0.24620852832584497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405178788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99347115,0.000095734526,0.0008162415,0.00037280377,0.00001706157,0.000020664907,0.00034052075,0.000022966558,0.0048428834],"genre_scores_gemma":[0.9978483,0.000022457309,0.000108887085,0.00005026854,0.000019415398,0.000012357711,0.00018653905,0.000018866383,0.0017328468],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99838614,0.0006648594,0.000058428373,0.00026810414,0.00027179453,0.00035062776],"domain_scores_gemma":[0.9430088,0.042582575,0.0067535527,0.002658487,0.0019349778,0.0030616308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032898353,0.00016464008,0.00062378147,0.0007120177,0.0011368251,0.0024942672,0.0009722071,0.0012124821,0.015864914],"category_scores_gemma":[0.033211846,0.00027538123,0.00030067196,0.00088911416,0.0007080468,0.0015689177,0.0013036384,0.0019291901,0.0019519113],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027953484,0.0011972557,0.91646296,0.00014980239,0.0003751546,0.00046102796,0.006439886,0.0038903707,0.008768687,0.014014261,0.006538649,0.03890664],"study_design_scores_gemma":[0.00006448364,0.00028875374,0.9722276,0.000021486196,0.0001738674,0.000105415085,0.0035411003,0.012886488,0.002556341,0.0035002807,0.0045963307,0.000037997994],"about_ca_topic_score_codex":0.0074623316,"about_ca_topic_score_gemma":0.008390128,"teacher_disagreement_score":0.015864914,"about_ca_system_score_codex":0.0006229337,"about_ca_system_score_gemma":0.0006572652,"threshold_uncertainty_score":0.053073406},"labels":[],"label_agreement":null},{"id":"W4405194255","doi":"10.5753/stil.2024.245416","title":"Toxic Text Classification in Portuguese: Is LLaMA 3.1 8B All You Need?","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blutip (Canada)","funders":"Universidade Federal de Ouro Preto; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Portuguese; Computer science; Natural language processing; Artificial intelligence; Linguistics","score_opus":0.02507409172674729,"score_gpt":0.26685139629566434,"score_spread":0.24177730456891705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405194255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8232833,0.002487147,0.090304695,0.0077198227,0.0016964208,0.0004331731,0.011954078,0.02944702,0.032674316],"genre_scores_gemma":[0.9281538,0.0005052686,0.042184763,0.0008322665,0.00019337852,0.00016141734,0.014825738,0.0010529221,0.012090297],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991887,0.0003366869,0.00005285321,0.00021824284,0.00012568562,0.000077751305],"domain_scores_gemma":[0.99756765,0.0014452745,0.00013849193,0.0003117617,0.00036251717,0.00017432662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017780843,0.0012184591,0.0006680427,0.0010059946,0.00066760357,0.0019096097,0.0008047846,0.0015145967,0.0038508596],"category_scores_gemma":[0.0083269775,0.00034829482,0.0008051566,0.0005122513,0.0005013334,0.002020871,0.00095664704,0.0015117315,0.0049132463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002557403,0.0013450715,0.059543204,0.0010879701,0.00027466693,0.001609805,0.0021371143,0.10578705,0.03853269,0.005393334,0.10154468,0.68018705],"study_design_scores_gemma":[0.000106817184,0.0005085086,0.022112856,0.00020712405,0.00012389167,0.00068154617,0.0011253892,0.91864663,0.021487078,0.0048634233,0.030011253,0.00012548987],"about_ca_topic_score_codex":0.014122613,"about_ca_topic_score_gemma":0.021105105,"teacher_disagreement_score":0.014122613,"about_ca_system_score_codex":0.0009524095,"about_ca_system_score_gemma":0.0012537851,"threshold_uncertainty_score":0.028080821},"labels":[],"label_agreement":null},{"id":"W4405288398","doi":"10.20355/jcie29601","title":"Classroom Incivility Going Viral on Social Media: One Professor’s Encounters","year":2024,"lang":"en","type":"article","venue":"Journal of Contemporary Issues in Education","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Incivility; Social media; Sociology; Social psychology; Psychology; Media studies; Political science; Law","score_opus":0.030200268093686745,"score_gpt":0.31647519369129007,"score_spread":0.2862749255976033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405288398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74122125,0.0036066782,0.00767154,0.19600776,0.00328044,0.00017927469,0.00010404934,0.0003009486,0.047627937],"genre_scores_gemma":[0.9577044,0.0022358058,0.0019250171,0.01442937,0.00085376797,0.00008199322,0.000025701,0.0001371746,0.02260679],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9822843,0.0124527,0.00036653943,0.0012463623,0.0015545106,0.0020956523],"domain_scores_gemma":[0.98431337,0.0061946083,0.0012622984,0.0005467708,0.0016531475,0.006029753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006445543,0.0007641511,0.00096257747,0.0015056933,0.033485126,0.010247485,0.0024977066,0.0074377726,0.004536799],"category_scores_gemma":[0.022386774,0.0008194755,0.0009733596,0.0011934408,0.011232527,0.008142812,0.011402585,0.0171967,0.0012098593],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055786484,0.00028257698,0.004817986,0.000096135744,0.000023363882,0.0064631924,0.9332097,0.00013898895,0.00082753,0.01327268,0.023286028,0.017525971],"study_design_scores_gemma":[0.00001681297,0.00023095458,0.0013768686,0.00013272412,0.000014897057,0.0051886886,0.8454937,0.0003072042,0.00071019307,0.0016588711,0.14480594,0.0000631364],"about_ca_topic_score_codex":0.0057614786,"about_ca_topic_score_gemma":0.012261171,"teacher_disagreement_score":0.033485126,"about_ca_system_score_codex":0.0051623606,"about_ca_system_score_gemma":0.0038283586,"threshold_uncertainty_score":0.037455678},"labels":[],"label_agreement":null},{"id":"W4405368118","doi":"10.31234/osf.io/t9x8r","title":"Moral Stereotyping in Large Language Models","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Globe; Stereotype (UML); Social psychology; Psychology; Sociology; Positive economics; Economics","score_opus":0.02241511617094059,"score_gpt":0.26292220917361614,"score_spread":0.24050709300267556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405368118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88220376,0.0004188407,0.10841412,0.0011717839,0.000054741544,0.00014045491,0.00076585106,0.0002810284,0.0065493784],"genre_scores_gemma":[0.98849434,0.00006655291,0.010248477,0.00014691989,0.000027558082,0.00009949528,0.0005403237,0.000045350265,0.00033099373],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9866788,0.010612201,0.00041184796,0.0011842108,0.0007964412,0.00031660585],"domain_scores_gemma":[0.88415474,0.098722756,0.0067257355,0.0065014474,0.002990436,0.0009049486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020406552,0.00088467257,0.0007938751,0.0023677319,0.0011065371,0.00425526,0.0011271117,0.00090946443,0.0026863767],"category_scores_gemma":[0.101538934,0.00056809105,0.0010158733,0.0014390909,0.0019307064,0.005189639,0.0029287294,0.0020585419,0.0005935378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017394882,0.00070447254,0.5461504,0.0005918482,0.0010342809,0.00073068385,0.038529027,0.18331204,0.0036328512,0.06577153,0.0069264853,0.150877],"study_design_scores_gemma":[0.000064041,0.00015752562,0.05380725,0.00021471303,0.000114511415,0.00029615706,0.007111957,0.8344259,0.0011408202,0.09944304,0.0030945565,0.00012946708],"about_ca_topic_score_codex":0.009029098,"about_ca_topic_score_gemma":0.008066039,"teacher_disagreement_score":0.020406552,"about_ca_system_score_codex":0.0014554941,"about_ca_system_score_gemma":0.00082585425,"threshold_uncertainty_score":0.10792148},"labels":[],"label_agreement":null},{"id":"W4405635425","doi":"10.1016/j.dss.2024.114389","title":"The implications of account suspensions on online discussion platforms","year":2024,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Chulalongkorn University","keywords":"Computer science","score_opus":0.025527056483968833,"score_gpt":0.2984681075807626,"score_spread":0.27294105109679373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405635425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925283,0.000092144146,0.00094403676,0.0007220482,0.000020688714,0.000030725332,0.0000663091,0.000032315853,0.0055634994],"genre_scores_gemma":[0.99917525,0.000025158946,0.00019331591,0.00009133261,0.000020298989,0.000021207507,0.000045842346,0.0000074822515,0.00042013917],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9911582,0.0048197345,0.0006495497,0.00072311395,0.0015794261,0.0010700739],"domain_scores_gemma":[0.89850324,0.05207822,0.034298558,0.005816209,0.0036091835,0.005694547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065533784,0.00027778934,0.00031149364,0.0015435155,0.0033367197,0.0037537853,0.0008191445,0.0010984428,0.0065396614],"category_scores_gemma":[0.062209427,0.00034633366,0.00054865517,0.001174481,0.0025257056,0.0037742604,0.0039076083,0.0019579513,0.00083461375],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004738787,0.00047536287,0.9115582,0.00011800757,0.00008488944,0.00047022366,0.039996352,0.0004410088,0.001990541,0.0033283012,0.0010426738,0.0400206],"study_design_scores_gemma":[0.000021241416,0.0003568622,0.95307547,0.00009802917,0.00004587761,0.00023926285,0.034787502,0.0017838144,0.0008853394,0.0037331153,0.004921978,0.000051649957],"about_ca_topic_score_codex":0.0033825482,"about_ca_topic_score_gemma":0.004672568,"teacher_disagreement_score":0.0065533784,"about_ca_system_score_codex":0.0014123761,"about_ca_system_score_gemma":0.0012894652,"threshold_uncertainty_score":0.034658015},"labels":[],"label_agreement":null},{"id":"W4405652488","doi":"10.1007/978-3-031-75813-3_12","title":"Correction to: Free Speech in the Puzzle of Content Regulation: Insights from the West and the Global South","year":2024,"lang":"en","type":"book-chapter","venue":"Law, governance and technology series","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Content (measure theory); Computer science; Linguistics; Philosophy; Mathematics","score_opus":0.008511927878754584,"score_gpt":0.18538795244274733,"score_spread":0.17687602456399276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405652488","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017003926,0.0010688261,0.00060145644,0.18050992,0.801102,0.000037183276,0.0019379715,0.0005651459,0.014007507],"genre_scores_gemma":[0.014336942,0.00554843,0.0029198602,0.1606455,0.27159062,0.0003197895,0.0018891053,0.0018249553,0.54092485],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952028,0.00063615135,0.0007219485,0.00076172757,0.0020783455,0.0005991617],"domain_scores_gemma":[0.95525455,0.01229843,0.0018701947,0.003846948,0.024792625,0.0019373008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037202768,0.0021317413,0.0021319422,0.005447686,0.007216604,0.008615886,0.0048407027,0.014854533,0.09914521],"category_scores_gemma":[0.06966518,0.0012492654,0.0011874937,0.005755336,0.0059882407,0.0056300918,0.0032751984,0.017739156,0.0666164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007176389,0.000002361044,0.000023740324,0.00003573476,0.0000018667904,0.00003789285,0.00006327795,0.000015197806,0.0000105080135,0.001643988,0.9960681,0.0020900972],"study_design_scores_gemma":[0.000016252998,0.0000061820397,0.000533366,0.00017728504,0.0000079501915,0.00008748528,0.0002351579,0.00010421712,0.00008829268,0.0017789823,0.9969386,0.00002616743],"about_ca_topic_score_codex":0.08715726,"about_ca_topic_score_gemma":0.09242823,"teacher_disagreement_score":0.09914521,"about_ca_system_score_codex":0.007795596,"about_ca_system_score_gemma":0.010997357,"threshold_uncertainty_score":0.33167374},"labels":[],"label_agreement":null},{"id":"W4405944926","doi":"10.7592/ejhr.2024.12.4.886","title":"Is humour effective in combating hate speech? Maybe not so clearly","year":2024,"lang":"en","type":"article","venue":"European Journal of Humour Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université de Sherbrooke","funders":"","keywords":"Perception; Psychology; Order (exchange); The Internet; Social psychology; Field (mathematics); Computer science; World Wide Web; Business","score_opus":0.05483572568829413,"score_gpt":0.34835088006479564,"score_spread":0.2935151543765015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405944926","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8953579,0.011030085,0.014192772,0.014758554,0.00049942354,0.00021028079,0.000049826842,0.00010818796,0.06379305],"genre_scores_gemma":[0.9937844,0.0018980782,0.0022340503,0.00073886674,0.00015720163,0.00003407225,0.000009190654,0.000013024353,0.0011311139],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9918276,0.0058658062,0.0002522605,0.00032759176,0.0013254649,0.00040124753],"domain_scores_gemma":[0.97393453,0.019778075,0.0027784246,0.000840795,0.0019350764,0.0007331146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007991302,0.00041942293,0.0006381579,0.00075952144,0.0012038379,0.0031558538,0.0005455872,0.0014701609,0.0021554662],"category_scores_gemma":[0.023689361,0.0001491829,0.00023834778,0.0004079836,0.0045209057,0.0034915046,0.0013274072,0.0008611149,0.0004167564],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011759445,0.0009864164,0.06479118,0.00896929,0.0005492824,0.0017406229,0.18677238,0.000614615,0.043669395,0.03340143,0.003562491,0.65376693],"study_design_scores_gemma":[0.00018020291,0.0045895665,0.28338206,0.0071067307,0.0011185635,0.0035972286,0.46462855,0.003967774,0.062959835,0.031768396,0.13636856,0.00033250576],"about_ca_topic_score_codex":0.000695362,"about_ca_topic_score_gemma":0.00093528826,"teacher_disagreement_score":0.007991302,"about_ca_system_score_codex":0.00066269614,"about_ca_system_score_gemma":0.001003198,"threshold_uncertainty_score":0.042262554},"labels":[],"label_agreement":null},{"id":"W4406115220","doi":"10.14445/23488549/ijece-v11i12p116","title":"A Novel Feature Extraction Classifier for Hateful, Offense, and Neutral Content on X","year":2024,"lang":"en","type":"article","venue":"International Journal of Electronics and Communication Engineering","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Computer science","score_opus":0.02146946363223282,"score_gpt":0.26101924788891173,"score_spread":0.23954978425667892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406115220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47532874,0.0025099954,0.50375414,0.0010291724,0.00078941346,0.00065709586,0.0048649404,0.0063354466,0.004731041],"genre_scores_gemma":[0.80885315,0.00066716253,0.17084119,0.00033311537,0.00033126207,0.0006002078,0.00945595,0.000078060475,0.008839888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945825,0.00006752939,0.00005028362,0.00016852615,0.00016055623,0.000094860625],"domain_scores_gemma":[0.99929106,0.00026343128,0.0000569046,0.000047514248,0.00030097534,0.000039940787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065575604,0.00077661633,0.0010305519,0.0014620508,0.00054676743,0.00072392385,0.0008401225,0.0010848633,0.001482863],"category_scores_gemma":[0.0017199313,0.00013608823,0.0006766952,0.0008196106,0.00021632986,0.0009933633,0.000511516,0.00079436053,0.001567141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009249971,0.0007237934,0.027753327,0.00017585322,0.000109214736,0.00048414615,0.00013268186,0.008660799,0.058549345,0.0010147075,0.019013157,0.88245803],"study_design_scores_gemma":[0.0001180585,0.000821344,0.030876692,0.000060742746,0.00013722757,0.0008970911,0.00031201576,0.9093896,0.0454395,0.0016079103,0.010275318,0.000064462554],"about_ca_topic_score_codex":0.005062721,"about_ca_topic_score_gemma":0.003674881,"teacher_disagreement_score":0.005062721,"about_ca_system_score_codex":0.00047754674,"about_ca_system_score_gemma":0.00067623035,"threshold_uncertainty_score":0.010066509},"labels":[],"label_agreement":null},{"id":"W4406124573","doi":"10.1007/s11042-025-20603-w","title":"Unmasking offensive content: a multimodal approach with emotional understanding","year":2025,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Offensive; Computer science; Task (project management); Context (archaeology); Harassment; Natural language processing; Artificial intelligence; Psychology; Social psychology","score_opus":0.050406508871172206,"score_gpt":0.24205386100187623,"score_spread":0.19164735213070402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406124573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18678433,0.0018891668,0.7722671,0.0008041398,0.00028453022,0.00056274806,0.0009428545,0.002556602,0.03390849],"genre_scores_gemma":[0.6419737,0.001612489,0.3419627,0.00034241934,0.0002604542,0.00040796498,0.00070520217,0.00030368747,0.01243144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957806,0.0001231815,0.000017462373,0.00010004056,0.000115140494,0.00006607336],"domain_scores_gemma":[0.99952114,0.00020141779,0.000061421284,0.000057748064,0.00011896338,0.000039388495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057084294,0.0008330815,0.00045684076,0.0014277406,0.00043367737,0.0017898073,0.0004951016,0.0008201661,0.005557707],"category_scores_gemma":[0.0022205971,0.00019893942,0.0005678983,0.00071317825,0.00044619796,0.0018470716,0.0019667896,0.00068506686,0.0013960627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043438416,0.0003344531,0.002905538,0.00040960958,0.00008923837,0.0003060041,0.0012623068,0.0042773737,0.21359082,0.005432651,0.003625104,0.7673325],"study_design_scores_gemma":[0.000062600484,0.001489029,0.0652421,0.0006925909,0.00068132544,0.0015075096,0.0064782486,0.6068999,0.23409535,0.037075214,0.04550754,0.00026857186],"about_ca_topic_score_codex":0.0006878676,"about_ca_topic_score_gemma":0.0013590117,"teacher_disagreement_score":0.005557707,"about_ca_system_score_codex":0.00024091538,"about_ca_system_score_gemma":0.00028708664,"threshold_uncertainty_score":0.018592417},"labels":[],"label_agreement":null},{"id":"W4406433177","doi":"10.2139/ssrn.5097707","title":"&lt;div&gt; But My Ghosts Are So Hard to Hear:&amp;nbsp;&lt;span&gt;Pseudolaw and Conspiracy Culture&lt;/span&gt;&lt;/div&gt;","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Span (engineering); Life span; Medicine; Engineering; Gerontology; Structural engineering","score_opus":0.009094097455147835,"score_gpt":0.24004514301931384,"score_spread":0.230951045564166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406433177","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050992123,0.0006024601,0.0005435032,0.06202234,0.003907011,0.00010756659,0.0021908192,0.0005943108,0.92493284],"genre_scores_gemma":[0.010583142,0.00019536771,0.00018286881,0.0032755304,0.00021273525,0.000028186565,0.00019178931,0.00020530318,0.9851252],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.99961483,0.000052258285,0.00001709335,0.000053367414,0.00015101893,0.00011149083],"domain_scores_gemma":[0.9986896,0.0002307246,0.00006329739,0.000090618145,0.0005803913,0.00034534276],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005386108,0.00048732985,0.0004441064,0.0006509499,0.005842561,0.0036566295,0.00061179115,0.0035665773,0.49650294],"category_scores_gemma":[0.0035678856,0.00035411856,0.00018622736,0.00064612104,0.0012177519,0.0021883212,0.0017983101,0.002704944,0.23219143],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001609031,0.000014994556,0.00070683216,0.000023897723,0.0000012627808,0.00009235258,0.0004084987,0.000022287515,0.00030643976,0.0032994195,0.95974195,0.03536604],"study_design_scores_gemma":[0.0000039944043,0.000013890641,0.0035889626,0.000050452294,0.0000018585451,0.00007610031,0.0015944435,0.00009138588,0.00036090327,0.0013715232,0.99283737,0.00000901949],"about_ca_topic_score_codex":0.06092027,"about_ca_topic_score_gemma":0.19889107,"teacher_disagreement_score":0.49650294,"about_ca_system_score_codex":0.0018049489,"about_ca_system_score_gemma":0.0020321994,"threshold_uncertainty_score":0.71817744},"labels":[],"label_agreement":null},{"id":"W4406433374","doi":"10.1002/cl2.70018","title":"Exposure to hate in online and traditional media: A systematic review and meta‐analysis of the impact of this exposure on individuals and communities","year":2025,"lang":"en","type":"review","venue":"Campbell Systematic Reviews","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université du Québec à Montréal; Université du Québec à Trois-Rivières","funders":"","keywords":"Meta-analysis; Psychology; Medicine","score_opus":0.11380913317773368,"score_gpt":0.33667744015680484,"score_spread":0.22286830697907117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406433374","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037099884,0.99396765,0.0002724992,0.00021149099,0.00012912942,0.00068124186,0.0006672873,0.0000119150745,0.00034887015],"genre_scores_gemma":[0.060649093,0.9346452,0.0010235911,0.0007370387,0.00012216346,0.0020556455,0.00052332744,0.000015255842,0.00022870683],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.98608834,0.0054719835,0.0048600486,0.0011366935,0.002047616,0.00039534038],"domain_scores_gemma":[0.9481429,0.03932306,0.0072203404,0.0011057961,0.003791256,0.0004166709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014860997,0.0017725022,0.012729807,0.008250482,0.0010302264,0.0036959157,0.0022662124,0.002217014,0.00517872],"category_scores_gemma":[0.060288552,0.0011788151,0.015796293,0.008865952,0.0013089355,0.0029275378,0.0022195599,0.0016529134,0.00031861317],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044779535,0.000030845265,0.0015657089,0.93243223,0.04669135,0.00008274791,0.00028959193,0.00007011688,0.00009929588,0.00014278425,0.00056788995,0.017579677],"study_design_scores_gemma":[0.0003688215,0.00043772662,0.007100151,0.6281065,0.35574505,0.00021941734,0.0005580716,0.00007606098,0.0002590001,0.00027847488,0.0067975176,0.00005323115],"about_ca_topic_score_codex":0.009077138,"about_ca_topic_score_gemma":0.022507034,"teacher_disagreement_score":0.014860997,"about_ca_system_score_codex":0.004218934,"about_ca_system_score_gemma":0.009369734,"threshold_uncertainty_score":0.07859343},"labels":[],"label_agreement":null},{"id":"W4406450848","doi":"10.48550/arxiv.2501.08284","title":"AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; DeepMind; Universität Hamburg; International Development Research Centre; Rockefeller Foundation","keywords":"Computer science; Linguistics; Natural language processing; Political science","score_opus":0.02078586480009493,"score_gpt":0.29669061624019255,"score_spread":0.27590475144009763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406450848","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09605881,0.0015960522,0.005791463,0.001226258,0.00054138456,0.00065569574,0.8715202,0.006070979,0.016539212],"genre_scores_gemma":[0.03939362,0.00031693306,0.009532542,0.00021676555,0.00010040468,0.00080721604,0.94502735,0.0003598563,0.0042453487],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998298,0.0004348886,0.00020542272,0.00039396147,0.00036526838,0.00030258574],"domain_scores_gemma":[0.99647224,0.000863538,0.0004563685,0.0008876903,0.0008856989,0.0004345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015587898,0.001722841,0.0007493261,0.0056497497,0.0023760723,0.0014168122,0.0016578285,0.002038785,0.0075023887],"category_scores_gemma":[0.005865566,0.00039916494,0.0008623333,0.004184292,0.00096319075,0.0020846277,0.003909381,0.0015625696,0.011627321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007988732,0.0006208564,0.049539465,0.0025921557,0.0001691672,0.0015224466,0.0059676957,0.0013182916,0.012201878,0.0029839545,0.8183404,0.10394482],"study_design_scores_gemma":[0.00014508908,0.00017468336,0.1806868,0.00069219916,0.000086504224,0.0015696903,0.007374882,0.0038622387,0.0076608323,0.0021180445,0.7954065,0.00022251035],"about_ca_topic_score_codex":0.020668095,"about_ca_topic_score_gemma":0.048480887,"teacher_disagreement_score":0.020668095,"about_ca_system_score_codex":0.0011017958,"about_ca_system_score_gemma":0.0015135349,"threshold_uncertainty_score":0.041095555},"labels":[],"label_agreement":null},{"id":"W4406458169","doi":"10.1109/bigdata62323.2024.10825802","title":"Are Existing Large Language Models Robust Against Jailbreak Attacks?","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Computer security","score_opus":0.03804687895985621,"score_gpt":0.2714046516492349,"score_spread":0.23335777268937868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406458169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38725433,0.0015431881,0.58360445,0.004705464,0.00031316644,0.00036040318,0.0015424312,0.008681668,0.011994769],"genre_scores_gemma":[0.94406205,0.00028499909,0.051418174,0.00075499347,0.00009648095,0.00013008848,0.0011592205,0.0004987704,0.0015952656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99065524,0.0052704792,0.00047417896,0.0016995809,0.00131989,0.0005807391],"domain_scores_gemma":[0.93841195,0.03862416,0.0044252714,0.0132931275,0.0040417365,0.0012037816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011318123,0.001259627,0.0011463751,0.0017225747,0.001126119,0.0041870326,0.0019579728,0.0018806242,0.0028685331],"category_scores_gemma":[0.06768389,0.0008056417,0.001155559,0.00076294795,0.0020577202,0.00862687,0.0024709336,0.003768601,0.0027329247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019675943,0.0010205312,0.09132659,0.0009879048,0.0013329836,0.00075292785,0.0057496764,0.41802335,0.03192458,0.040310122,0.012516217,0.39408758],"study_design_scores_gemma":[0.000046713078,0.00026874623,0.0040239287,0.00009912065,0.00012224905,0.00023158119,0.0007605612,0.94853497,0.0056328652,0.036209844,0.003977906,0.0000915411],"about_ca_topic_score_codex":0.0045710234,"about_ca_topic_score_gemma":0.005679737,"teacher_disagreement_score":0.011318123,"about_ca_system_score_codex":0.0013162988,"about_ca_system_score_gemma":0.0019903725,"threshold_uncertainty_score":0.059856653},"labels":[],"label_agreement":null},{"id":"W4406530152","doi":"10.33774/apsa-2025-ssz5q","title":"U.S. Public Opinion and Government Regulation of Foreign Social Media Apps: A “Hidden Consensus” About What to Ban?","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Public opinion; Social media; Government (linguistics); Internet privacy; Public relations; Government regulation; Business; Political science; Law; Computer science; Politics","score_opus":0.03539724096650528,"score_gpt":0.25330069708690717,"score_spread":0.21790345612040188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406530152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912243,0.00007557833,0.00016771992,0.0017495687,0.00001526297,0.000010895404,0.0002021345,0.0000033869242,0.0065512513],"genre_scores_gemma":[0.9992225,0.000037239774,0.000050577743,0.00021999051,0.000008660567,0.000008105801,0.00008120676,0.0000018210427,0.00036986693],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99754226,0.0011870527,0.000116444884,0.00021575057,0.00053767115,0.00040091865],"domain_scores_gemma":[0.9787971,0.009696089,0.006415597,0.0009199336,0.0029763652,0.0011949249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005242282,0.00012862377,0.00021271246,0.00095934654,0.0011314577,0.0022147072,0.00023883276,0.0008175778,0.0035067345],"category_scores_gemma":[0.018795393,0.00012122185,0.00025937034,0.0012632248,0.0013708496,0.0015184314,0.001249547,0.0012126855,0.00044846733],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003101932,0.00020720252,0.9387983,0.000058353933,0.000079522935,0.00010401274,0.02292047,0.00032136223,0.0005326445,0.0025539654,0.004269668,0.02984423],"study_design_scores_gemma":[0.000010742951,0.00013197942,0.9170276,0.00006610816,0.000042047843,0.000047964808,0.07527097,0.0010235234,0.00050940015,0.0012721937,0.0045635523,0.000033880187],"about_ca_topic_score_codex":0.014935358,"about_ca_topic_score_gemma":0.019554911,"teacher_disagreement_score":0.014935358,"about_ca_system_score_codex":0.0011247285,"about_ca_system_score_gemma":0.00083597563,"threshold_uncertainty_score":0.029696822},"labels":[],"label_agreement":null},{"id":"W4406572044","doi":"10.1016/j.nlp.2025.100126","title":"RESPECT: A framework for promoting inclusive and respectful conversations in online communications","year":2025,"lang":"en","type":"article","venue":"Natural Language Processing Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College; Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Inclusion (mineral); Psychology; Internet privacy; Sociology; Computer science; Social psychology","score_opus":0.010413841497334457,"score_gpt":0.3321838089788432,"score_spread":0.32176996748150877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406572044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004790983,0.00015903162,0.98626035,0.0011697634,0.00006006689,0.00033107426,0.0001860435,0.0023296692,0.004713014],"genre_scores_gemma":[0.19287084,0.00036790204,0.79934883,0.00056722434,0.00010986675,0.0007707004,0.0006428999,0.00037438772,0.0049473383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930668,0.003463389,0.00048423753,0.0013024758,0.0013539512,0.0003292085],"domain_scores_gemma":[0.99218726,0.0037666124,0.0010310527,0.0012729783,0.001151622,0.00059045956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064953337,0.0015557995,0.0006733933,0.0026950878,0.001995587,0.0044611464,0.0022600514,0.002371007,0.0039731455],"category_scores_gemma":[0.017904973,0.00086159207,0.0016338846,0.00092407735,0.004493254,0.007855867,0.0053008716,0.0028995257,0.0018118713],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002559194,0.00039121514,0.0054697893,0.000578821,0.00008041779,0.000651844,0.008939944,0.057048436,0.016448336,0.6466315,0.014318541,0.2491853],"study_design_scores_gemma":[0.000054861808,0.000341953,0.0015874677,0.00031889504,0.00009904685,0.00077147584,0.0024107576,0.437959,0.011701311,0.44412696,0.100440554,0.00018770173],"about_ca_topic_score_codex":0.007448119,"about_ca_topic_score_gemma":0.007915911,"teacher_disagreement_score":0.007448119,"about_ca_system_score_codex":0.0023520286,"about_ca_system_score_gemma":0.005336461,"threshold_uncertainty_score":0.03435099},"labels":[],"label_agreement":null},{"id":"W4406679108","doi":"10.1007/s13178-024-01079-z","title":"“They’re Unable to See my Decision to Detransition for What it is”: How Detrans Youth Perceive and Receive Discourses on Detransition","year":2025,"lang":"en","type":"article","venue":"Sexuality Research and Social Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council","keywords":"Psychology; Social psychology","score_opus":0.07642908754821064,"score_gpt":0.4118253459191358,"score_spread":0.3353962583709251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406679108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9639528,0.00037643538,0.0002582472,0.010453638,0.0000913035,0.000016109601,0.00004169628,0.0000104786195,0.024799287],"genre_scores_gemma":[0.9941381,0.00017024938,0.000109944216,0.001755444,0.000012264116,0.000017010743,0.000029918032,0.000010970986,0.0037562142],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9958473,0.0026416532,0.000118207674,0.00019143334,0.0005894791,0.000611847],"domain_scores_gemma":[0.9934777,0.002964531,0.0009366022,0.0002071857,0.0010402031,0.0013738503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048148427,0.00024530446,0.00027243447,0.0012579713,0.0081309285,0.007917057,0.0008584612,0.0025028833,0.006139689],"category_scores_gemma":[0.0116991,0.00037386673,0.0002321718,0.000887961,0.005750329,0.0058704535,0.0051323045,0.0047148545,0.00060775],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004382351,0.000089964866,0.05130407,0.000035821915,0.0000106358275,0.00024462357,0.9295285,0.000015288595,0.00041033214,0.007305493,0.0025084366,0.008502944],"study_design_scores_gemma":[0.0000036561462,0.000019030465,0.023408705,0.00009524384,0.000009831561,0.00011573518,0.9633275,0.00007574927,0.00015552482,0.00082801626,0.011946278,0.000014717472],"about_ca_topic_score_codex":0.072747216,"about_ca_topic_score_gemma":0.10831664,"teacher_disagreement_score":0.072747216,"about_ca_system_score_codex":0.0038218505,"about_ca_system_score_gemma":0.003003021,"threshold_uncertainty_score":0.1446476},"labels":[],"label_agreement":null},{"id":"W4406774802","doi":"10.1177/20563051251313844","title":"Toxic Communication on TikTok: Sigma Masculinities and Gendered Disinformation","year":2025,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Disinformation; Sigma; Sociology; Political science; Social media; Physics; Astronomy; Law","score_opus":0.0168391676874907,"score_gpt":0.24433624942039023,"score_spread":0.22749708173289954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406774802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9346959,0.00059086084,0.004806329,0.0017417467,0.00011577052,0.000036425612,0.00010969612,0.000059063008,0.05784418],"genre_scores_gemma":[0.993461,0.00024922352,0.00056381215,0.00021860735,0.000026211412,0.000015734491,0.000034998393,0.000030701285,0.005399854],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9988908,0.00051748793,0.000041784995,0.00009072721,0.00030530422,0.0001538804],"domain_scores_gemma":[0.9957522,0.0025836045,0.0008451664,0.0002638416,0.00027345069,0.00028163145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001333104,0.00035736035,0.00016283171,0.0011349476,0.003055855,0.0030780581,0.0003364405,0.00068058644,0.005507098],"category_scores_gemma":[0.0054022297,0.00016802018,0.00014991386,0.0007042332,0.0052074413,0.0032495244,0.0030386078,0.0009917624,0.0005088807],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022857737,0.00007406687,0.05379489,0.0005130793,0.000025589356,0.002917114,0.81408334,0.00019637196,0.015762094,0.039315093,0.00381797,0.06927181],"study_design_scores_gemma":[0.000009558322,0.00030137942,0.043098122,0.0007849066,0.000051034945,0.0037048352,0.7824304,0.000719006,0.011754175,0.010293546,0.14676847,0.00008449767],"about_ca_topic_score_codex":0.0017325825,"about_ca_topic_score_gemma":0.0038066362,"teacher_disagreement_score":0.005507098,"about_ca_system_score_codex":0.0012235671,"about_ca_system_score_gemma":0.00089427305,"threshold_uncertainty_score":0.01842314},"labels":[],"label_agreement":null},{"id":"W4406790439","doi":"10.1371/journal.pone.0317001","title":"Gender biases and hate speech: Promoters and targets in the Argentinean political context","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Politics; Popularity; Context (archaeology); Social media; Political science; Sociology; Law; Biology","score_opus":0.05960972641092253,"score_gpt":0.2473307400263937,"score_spread":0.18772101361547117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406790439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99786,0.00014355291,0.0001384563,0.000107843036,0.0000047027056,0.000004607272,0.000041760075,9.064784e-7,0.0016981082],"genre_scores_gemma":[0.9995346,0.000085382264,0.000055142915,0.000021860202,0.0000060193524,0.0000042712186,0.000030154824,0.0000016476141,0.0002608532],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99903214,0.00052706146,0.000041519343,0.00010759802,0.00016583143,0.0001259301],"domain_scores_gemma":[0.9941334,0.0029115337,0.0019237979,0.00021722978,0.000417639,0.0003964053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001791975,0.00019806651,0.00021829076,0.0007493125,0.0010991556,0.0011573932,0.00018831491,0.00041352856,0.0022369258],"category_scores_gemma":[0.008062846,0.0001507444,0.000086570195,0.00070993666,0.00097360637,0.00084968517,0.0010028247,0.000441127,0.00026413708],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019694494,0.00008227412,0.8969961,0.00006364895,0.000017469243,0.00017925167,0.08605605,0.000044583034,0.000972902,0.0005775748,0.00034105417,0.014472039],"study_design_scores_gemma":[0.000003009489,0.00006799471,0.9144299,0.000065563334,0.000014484187,0.00021571468,0.08235073,0.0005494168,0.00028822213,0.00025376782,0.0017496325,0.000011503843],"about_ca_topic_score_codex":0.009219391,"about_ca_topic_score_gemma":0.015674239,"teacher_disagreement_score":0.009219391,"about_ca_system_score_codex":0.00049794366,"about_ca_system_score_gemma":0.00026070417,"threshold_uncertainty_score":0.018331409},"labels":[],"label_agreement":null},{"id":"W4406928433","doi":"10.1108/oxan-es292533","title":"Canada faces serious social media manipulation threat","year":2025,"lang":"en","type":"article","venue":"Emerald expert briefings","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Internet privacy; Computer security; Computer science; World Wide Web","score_opus":0.011006499468778406,"score_gpt":0.22929702314838465,"score_spread":0.21829052367960625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406928433","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18223014,0.0021967664,0.0010506932,0.51026994,0.005661828,0.0006367181,0.0060784933,0.00075175206,0.29112366],"genre_scores_gemma":[0.35491708,0.002449655,0.0010860455,0.17652565,0.0010067741,0.00012919668,0.001696896,0.00017643943,0.46201223],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980307,0.000054549422,0.00003698377,0.00010086169,0.0009797284,0.00079710176],"domain_scores_gemma":[0.9945298,0.00031363353,0.00027100017,0.0000982405,0.002551804,0.00223545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005924812,0.00042651227,0.0002809943,0.0011952616,0.015259704,0.0035896196,0.0010987772,0.0076101217,0.019009342],"category_scores_gemma":[0.0030932366,0.0004092324,0.0003275384,0.00089026574,0.0015352052,0.00087007013,0.0017604346,0.006838296,0.0027015707],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007740674,0.00013879487,0.030114118,0.000065450105,0.000026418245,0.005873858,0.0022505065,0.00014168279,0.00088926405,0.0025437903,0.9346373,0.023241408],"study_design_scores_gemma":[0.00004343017,0.00010891169,0.06485495,0.00018193222,0.00003868503,0.0014678603,0.022442045,0.00052657793,0.0009464461,0.000904571,0.9083761,0.0001086054],"about_ca_topic_score_codex":0.9809085,"about_ca_topic_score_gemma":0.99418396,"teacher_disagreement_score":0.025947679,"about_ca_system_score_codex":0.025947679,"about_ca_system_score_gemma":0.11439539,"threshold_uncertainty_score":0.18826443},"labels":[],"label_agreement":null},{"id":"W4407090707","doi":"10.1101/2025.01.30.25321414","title":"Sentiment analysis of employees and COVID-19 vaccine hesitancy at workplace","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Registered Nurses' Association of Ontario","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Sentiment analysis; Business; Virology; Computer science; Medicine; Artificial intelligence; Outbreak; Internal medicine","score_opus":0.016843563884330483,"score_gpt":0.2783707912387036,"score_spread":0.2615272273543731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407090707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974711,0.00008131142,0.0002565344,0.00017351613,0.000025436246,0.00001549117,0.00013611933,0.00000473412,0.001835854],"genre_scores_gemma":[0.99869967,0.00007976067,0.00026568616,0.000074641364,0.000017539265,0.000016930086,0.000126969,0.000002495764,0.00071635796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989561,0.0003905699,0.00008720031,0.00008328063,0.00033521853,0.00014762583],"domain_scores_gemma":[0.99491334,0.0020882287,0.0014320725,0.00012547098,0.001086683,0.00035418253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017373867,0.0002176021,0.00027937815,0.00056197075,0.00064286165,0.0011799915,0.00014939002,0.0003238555,0.0015143949],"category_scores_gemma":[0.005467529,0.00009244458,0.0003460769,0.000391643,0.0003593531,0.0003879128,0.00058557314,0.000493181,0.00031703632],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007366949,0.00024723617,0.8982339,0.0004406196,0.00011946692,0.0005150363,0.035018962,0.0002960661,0.011783753,0.00048111207,0.0044261464,0.047701016],"study_design_scores_gemma":[0.000008494136,0.00031560945,0.92215085,0.00010062549,0.00006765141,0.0001898547,0.06746785,0.002212615,0.0023821937,0.0002932157,0.00477388,0.000037080168],"about_ca_topic_score_codex":0.0020219637,"about_ca_topic_score_gemma":0.0027386905,"teacher_disagreement_score":0.0020219637,"about_ca_system_score_codex":0.0005933718,"about_ca_system_score_gemma":0.00032976642,"threshold_uncertainty_score":0.009188294},"labels":[],"label_agreement":null},{"id":"W4407171721","doi":"10.1016/j.ipm.2025.104079","title":"Exploring hate speech dynamics: The emotional, linguistic, and thematic impact on social media users","year":2025,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dynamics (music); Linguistics; Social media; Thematic map; Psychology; Sociology; Computer science; Geography; World Wide Web; Philosophy","score_opus":0.03384694922045842,"score_gpt":0.26944526665705476,"score_spread":0.23559831743659634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407171721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99741614,0.000101467,0.00058103655,0.00009466129,0.0000074504887,0.00001943199,0.0009344873,0.000010491213,0.0008347814],"genre_scores_gemma":[0.9981481,0.0000885347,0.00040257868,0.000034478293,0.000019488041,0.000030605264,0.00079735933,0.0000057622306,0.00047320433],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999328,0.00027334894,0.00005300096,0.00013604928,0.000111644906,0.00009802533],"domain_scores_gemma":[0.9946142,0.0025239466,0.0017201941,0.0003666982,0.0004122757,0.00036269848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010389255,0.00026151174,0.00022409501,0.0009838664,0.0005690728,0.0011480149,0.00019547701,0.0004357878,0.0020910734],"category_scores_gemma":[0.006313156,0.00014314694,0.00034363236,0.001030405,0.00040305557,0.0011977399,0.0008805333,0.00045795392,0.0006345698],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015094035,0.00007986652,0.9806444,0.00007543207,0.00006629058,0.00010002241,0.004909646,0.00016098606,0.0014812864,0.00016871082,0.00060900673,0.011553413],"study_design_scores_gemma":[0.0000030373153,0.00005881331,0.9916863,0.000019288069,0.000029885086,0.000107524305,0.0045257877,0.001811968,0.000411992,0.00018473869,0.0011467804,0.000013832522],"about_ca_topic_score_codex":0.0041367174,"about_ca_topic_score_gemma":0.00740198,"teacher_disagreement_score":0.0041367174,"about_ca_system_score_codex":0.00033897866,"about_ca_system_score_gemma":0.00018091974,"threshold_uncertainty_score":0.008225262},"labels":[],"label_agreement":null},{"id":"W4407573828","doi":"10.1016/j.socnet.2025.02.001","title":"From warnings to bans: The role of social networks in the severity of sanctions","year":2025,"lang":"en","type":"article","venue":"Social Networks","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Sanctions; Psychology; Computer security; Political science; Business; Social psychology; Computer science; Law","score_opus":0.004423958550251469,"score_gpt":0.22788237294916924,"score_spread":0.22345841439891778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407573828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99242777,0.00028966786,0.002339995,0.00045639306,0.00003969235,0.000029283889,0.00047182402,0.00004719372,0.003898067],"genre_scores_gemma":[0.9990792,0.00005421139,0.000337865,0.000021743272,0.000025711528,0.000008176886,0.00021602202,0.0000063090947,0.00025072496],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971125,0.0011381045,0.00022970191,0.00047789305,0.0007756744,0.00026617112],"domain_scores_gemma":[0.94814163,0.027748208,0.017473752,0.0018130888,0.0025483486,0.0022749645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026615304,0.00038926047,0.0003648037,0.002734383,0.00091238465,0.0024182412,0.00043893448,0.0006144535,0.0023075435],"category_scores_gemma":[0.030604918,0.00013784508,0.00029012377,0.0016103213,0.0009128194,0.003471014,0.0015912441,0.0015773644,0.00049386127],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003132716,0.00019645323,0.960375,0.00011051949,0.00015952703,0.00020320067,0.0019469823,0.0027607458,0.0010718678,0.0014748783,0.0012884105,0.03009911],"study_design_scores_gemma":[0.000014391737,0.00022569881,0.96677196,0.00009770219,0.00009532241,0.0005040584,0.004433743,0.018363234,0.0013659651,0.0039923973,0.004069035,0.0000665365],"about_ca_topic_score_codex":0.0027118318,"about_ca_topic_score_gemma":0.0036465775,"teacher_disagreement_score":0.002734383,"about_ca_system_score_codex":0.0004682839,"about_ca_system_score_gemma":0.00035006378,"threshold_uncertainty_score":0.014075637},"labels":[],"label_agreement":null},{"id":"W4407831734","doi":"10.2139/ssrn.5100113","title":"U.S. Public Opinion and Government Regulation of Foreign Social Media Apps: A \"Hidden Consensus\" about What to Ban?","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Public opinion; Social media; Government (linguistics); Political science; Public administration; Public relations; Law and economics; Law; Politics; Sociology","score_opus":0.019644472808512902,"score_gpt":0.24337461755533868,"score_spread":0.2237301447468258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407831734","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29621768,0.0059364648,0.0014408375,0.34700572,0.0014422867,0.000028218463,0.0024634458,0.00006837617,0.345397],"genre_scores_gemma":[0.9698001,0.001522734,0.0002433013,0.019172577,0.0002628287,0.000016936732,0.00032243747,0.000016621014,0.00864248],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9986009,0.00034998482,0.000046862875,0.00018664147,0.00042271457,0.00039280372],"domain_scores_gemma":[0.99013525,0.0052205096,0.0016958757,0.0003550918,0.0020061424,0.00058716844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00265974,0.00017636215,0.0002637737,0.0009925729,0.0019439547,0.0042872247,0.00035137965,0.0029969437,0.009512915],"category_scores_gemma":[0.010147429,0.00015069969,0.00027848568,0.001399086,0.001524392,0.0020514852,0.0010866527,0.0026178204,0.0009943396],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005344653,0.00030571362,0.14529307,0.00032875492,0.00013689575,0.00058316486,0.010490358,0.00077482866,0.0013797253,0.3084973,0.37930062,0.15237504],"study_design_scores_gemma":[0.00012253891,0.00032253744,0.49581414,0.00155422,0.00032061304,0.0003454657,0.037927967,0.004021421,0.004291666,0.10815067,0.34693083,0.00019795675],"about_ca_topic_score_codex":0.04815684,"about_ca_topic_score_gemma":0.062759876,"teacher_disagreement_score":0.04815684,"about_ca_system_score_codex":0.0027781047,"about_ca_system_score_gemma":0.0030294475,"threshold_uncertainty_score":0.09575307},"labels":[],"label_agreement":null},{"id":"W4407874572","doi":"10.1177/0261927x251318887","title":"Automating the Detection of Linguistic Intergroup Bias Through Computerized Language Analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Language and Social Psychology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Lexical analysis; Abstraction; Ingroups and outgroups; Coding (social sciences); Computer science; Outgroup; Natural language processing; Linguistics; Sentence; Psychology; Sentiment analysis; Linguistic analysis; Artificial intelligence; Cognitive psychology; Social psychology; Statistics; Mathematics; Epistemology","score_opus":0.01768414251105102,"score_gpt":0.3330358385620776,"score_spread":0.31535169605102653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407874572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3688288,0.0001940503,0.5963469,0.00078724726,0.00023544778,0.0028106195,0.0046965196,0.018407637,0.007692799],"genre_scores_gemma":[0.40803048,0.00012024,0.5798413,0.00028838517,0.0000854275,0.0029119707,0.0045852317,0.0011336657,0.0030033079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992762,0.0035451462,0.0007385847,0.0015513273,0.0010701101,0.00033277515],"domain_scores_gemma":[0.958949,0.024213852,0.0033419998,0.0036569368,0.009360374,0.00047779072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073374026,0.0011372437,0.0007509467,0.004539061,0.0008852923,0.0022149435,0.0010719376,0.00046962718,0.0070092003],"category_scores_gemma":[0.03366303,0.00047622956,0.0005595213,0.002303062,0.0008967419,0.0025800993,0.0026444849,0.0014271906,0.004507819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006197525,0.00042221043,0.041379433,0.0010410246,0.00012648202,0.0003764013,0.011627632,0.0031747567,0.1653549,0.00431354,0.02194808,0.7496157],"study_design_scores_gemma":[0.0003860696,0.0011127009,0.14914449,0.00048602515,0.00029855603,0.0011863473,0.014213334,0.45135492,0.26533544,0.031888444,0.08410909,0.00048469298],"about_ca_topic_score_codex":0.002978194,"about_ca_topic_score_gemma":0.00442368,"teacher_disagreement_score":0.0073374026,"about_ca_system_score_codex":0.0012609786,"about_ca_system_score_gemma":0.00197339,"threshold_uncertainty_score":0.038804352},"labels":[],"label_agreement":null},{"id":"W4407891888","doi":"10.18280/jesa.580102","title":"Optimizing Hate Speech Detection in Indonesian Social Media: An ADASYN and LSTM-Based Approach","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Indonesian; Computer science; Social media; Voice activity detection; Speech recognition; Natural language processing; Artificial intelligence; Linguistics; World Wide Web; Speech processing; Philosophy","score_opus":0.015940070759209363,"score_gpt":0.24358459675682057,"score_spread":0.2276445259976112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407891888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24931945,0.0011365499,0.741275,0.00057946687,0.00020184887,0.0001454971,0.00023174775,0.0021255764,0.0049848473],"genre_scores_gemma":[0.84191716,0.00041470473,0.150995,0.00025028412,0.00012331,0.00012275513,0.00044297441,0.0001030605,0.005630754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996111,0.00009472342,0.000032451062,0.00011165257,0.000083412604,0.00006671671],"domain_scores_gemma":[0.9993349,0.00026151884,0.00007909784,0.000043465352,0.00024473743,0.00003627997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008335519,0.0010455896,0.0006957998,0.0011288852,0.00041065394,0.00087839563,0.0006878702,0.0007174292,0.0010273795],"category_scores_gemma":[0.0015084314,0.00035241726,0.0004972893,0.0005673873,0.0003207126,0.0010872901,0.00064381474,0.00084035465,0.0007712083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046168626,0.00048650423,0.0061554033,0.00017550528,0.00017272205,0.00017138118,0.00024099344,0.10376387,0.042374812,0.0012829879,0.0029531554,0.84176093],"study_design_scores_gemma":[0.000006113803,0.000097119315,0.0021025832,0.000009894264,0.00003497446,0.00004461343,0.00010302826,0.98910636,0.0073109833,0.0005586987,0.0006153557,0.000010250358],"about_ca_topic_score_codex":0.0047301087,"about_ca_topic_score_gemma":0.0067334,"teacher_disagreement_score":0.0047301087,"about_ca_system_score_codex":0.00057236734,"about_ca_system_score_gemma":0.000811857,"threshold_uncertainty_score":0.009405136},"labels":[],"label_agreement":null},{"id":"W4407958594","doi":"10.2139/ssrn.5137974","title":"It Happens More than Once: Understanding Canadian University Students Experiences of Hate, Online and Offline","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Wilfrid Laurier University; Concordia University; Université du Québec à Montréal; York University","funders":"","keywords":"Online and offline; Psychology; Media studies; Sociology; Mathematics education; Political science; Law","score_opus":0.0180217859861165,"score_gpt":0.2657592097241467,"score_spread":0.24773742373803018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407958594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.965384,0.00076479185,0.00042248747,0.0073711695,0.00010956041,0.000048009868,0.00031661024,0.000022640528,0.025560679],"genre_scores_gemma":[0.99336666,0.00053715636,0.00014918996,0.0010045934,0.00001669191,0.000019341252,0.00011590272,0.000032301534,0.0047580567],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99523157,0.0008356558,0.00009813784,0.0003569748,0.0014211962,0.0020564857],"domain_scores_gemma":[0.9851364,0.003168892,0.001949837,0.00031464617,0.0040111723,0.005419086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044376543,0.0007606246,0.00073935516,0.0045806034,0.032767043,0.016117597,0.0030798318,0.003854369,0.007219009],"category_scores_gemma":[0.014257305,0.0007864337,0.00054189615,0.0062121097,0.014083814,0.0066953176,0.006919244,0.0074857194,0.00081983634],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046854228,0.00004611977,0.03136449,0.000034354784,0.000009245355,0.0001671875,0.9577307,0.000038655013,0.00032931313,0.0029415307,0.0029374475,0.0043540746],"study_design_scores_gemma":[0.0000025086863,0.0000107981605,0.022057578,0.000056319695,0.0000078231205,0.000036759062,0.9690684,0.00005998432,0.000052166848,0.0002492577,0.008362662,0.000035722587],"about_ca_topic_score_codex":0.9850531,"about_ca_topic_score_gemma":0.9912307,"teacher_disagreement_score":0.07896955,"about_ca_system_score_codex":0.07896955,"about_ca_system_score_gemma":0.092447236,"threshold_uncertainty_score":0.57296693},"labels":[],"label_agreement":null},{"id":"W4408386512","doi":"10.1007/s12142-025-00737-y","title":"Correction to: From Commitment to Compliance: The Discursive Challenge to Ending Enforced Disappearances in Democratic Argentina","year":2024,"lang":"en","type":"article","venue":"Human Rights Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Democracy; Compliance (psychology); Political philosophy; Development aid; Political science; Human rights; Law and economics; Law; Public administration; Criminology; Sociology; Politics; Social psychology; Psychology","score_opus":0.04230681903910862,"score_gpt":0.328396889788642,"score_spread":0.2860900707495334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408386512","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048240746,0.001649742,0.0005932154,0.23597257,0.7486838,0.00006153795,0.002382042,0.00040416908,0.009770559],"genre_scores_gemma":[0.04839799,0.012104277,0.004518941,0.26601624,0.25920373,0.00082789036,0.0024226157,0.0014935874,0.40501463],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99432445,0.0010108454,0.0008834443,0.0008875797,0.0020416963,0.0008520579],"domain_scores_gemma":[0.96296614,0.011432282,0.0021531475,0.0027715757,0.018969286,0.001707489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004971474,0.001615628,0.0020307628,0.003132177,0.005332893,0.0058938675,0.003358769,0.015684329,0.04426942],"category_scores_gemma":[0.0676616,0.001162262,0.0009314252,0.003008924,0.0043300483,0.0030168262,0.0030461387,0.019325292,0.018445216],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009118931,0.0000020203518,0.000042495205,0.00007182219,0.000005218574,0.00009514758,0.00013355952,0.000015649044,0.000011721732,0.0015605354,0.9964301,0.0016227227],"study_design_scores_gemma":[0.000022980437,0.000007221185,0.0009558364,0.000433929,0.000018100052,0.00012471754,0.00041141268,0.00013039459,0.00013991726,0.0012908309,0.9964311,0.000033450026],"about_ca_topic_score_codex":0.059966292,"about_ca_topic_score_gemma":0.0698427,"teacher_disagreement_score":0.059966292,"about_ca_system_score_codex":0.0066792243,"about_ca_system_score_gemma":0.013597376,"threshold_uncertainty_score":0.14809597},"labels":[],"label_agreement":null},{"id":"W4408406240","doi":"10.22230/cjc.2017v43n1a3316","title":"Framing the Pipeline Problem: Civic Claimsmakers and Social Media","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Framing (construction); Political science; Sociology; Media studies; Geography","score_opus":0.016207738764560452,"score_gpt":0.22370983451194157,"score_spread":0.20750209574738113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408406240","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42656332,0.0029261245,0.015333062,0.08555432,0.0004052059,0.0002247688,0.0002809685,0.000102659884,0.4686095],"genre_scores_gemma":[0.99249494,0.0004488261,0.00118514,0.0008622459,0.000070277005,0.000028030532,0.000032883992,0.000020035248,0.0048576533],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99036974,0.004429595,0.00021296494,0.00084317353,0.0021772045,0.0019672865],"domain_scores_gemma":[0.9836956,0.01075508,0.0017154014,0.0007511586,0.0018482439,0.0012345915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009158798,0.0006775147,0.00046186883,0.005196317,0.023450708,0.023437623,0.0021152736,0.0039033808,0.009735405],"category_scores_gemma":[0.015657393,0.00045806,0.0003634849,0.0042276494,0.040653534,0.010917733,0.009831327,0.0046839383,0.00037939215],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068919406,0.00010420612,0.023896337,0.0002730397,0.00003299314,0.0015121289,0.5689793,0.00058682304,0.0009607731,0.35047036,0.007734365,0.0453807],"study_design_scores_gemma":[0.000011463865,0.000021307489,0.010948228,0.0005918285,0.00002830785,0.00028414524,0.8066304,0.0009839414,0.0007429825,0.0601688,0.11954395,0.00004475334],"about_ca_topic_score_codex":0.49554527,"about_ca_topic_score_gemma":0.57258487,"teacher_disagreement_score":0.50445473,"about_ca_system_score_codex":0.032242943,"about_ca_system_score_gemma":0.02798884,"threshold_uncertainty_score":0.98532176},"labels":[],"label_agreement":null},{"id":"W4408501309","doi":"10.33422/icrhs.v2i1.924","title":"Brampton, Browntown, or Bramladesh? A Critique of the Hate Speech Used Against Racialized Spaces in Canada","year":2025,"lang":"en","type":"article","venue":"Proceedings of The International Conference on Research in Humanities and Social Sciences","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Sociology; Linguistics; Philosophy","score_opus":0.13290836389276312,"score_gpt":0.3678242426100528,"score_spread":0.2349158787172897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408501309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46177447,0.021029606,0.0016641645,0.25726292,0.0019360055,0.000062945575,0.0004356141,0.00013111843,0.25570315],"genre_scores_gemma":[0.9633044,0.0046272147,0.00041351322,0.01077496,0.00010378895,0.00001826459,0.000042078987,0.00007483098,0.020640958],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9968262,0.00077755627,0.000064077954,0.0002942845,0.0010248943,0.0010130795],"domain_scores_gemma":[0.9936126,0.0022731079,0.00033869644,0.00016805831,0.002126027,0.0014815326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028398389,0.00057781464,0.00039654077,0.0021581058,0.048946742,0.012436951,0.0020742815,0.0028003065,0.0037218092],"category_scores_gemma":[0.0075561465,0.00033122557,0.00026674708,0.003469487,0.0243999,0.0032610712,0.0038817595,0.0050217262,0.00028879664],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006897943,0.000016918137,0.0070712413,0.0002514153,0.00002061846,0.0014896403,0.7986417,0.00024099849,0.0010138426,0.10951529,0.056342993,0.025326397],"study_design_scores_gemma":[0.0000066692146,0.000011984076,0.009703608,0.0004749679,0.000025738913,0.00023555169,0.71757466,0.00026262607,0.00067170936,0.0034528098,0.26750022,0.00007954242],"about_ca_topic_score_codex":0.99474144,"about_ca_topic_score_gemma":0.9963779,"teacher_disagreement_score":0.12549438,"about_ca_system_score_codex":0.12549438,"about_ca_system_score_gemma":0.14980173,"threshold_uncertainty_score":0.9105297},"labels":[],"label_agreement":null},{"id":"W4408659263","doi":"10.2139/ssrn.5182125","title":"&lt;p&gt;Sexual Communications, Digital Intimacy, and the Extremely Limited Admissibility of Evidence of a Complainant's Intention to Consent&lt;b&gt;&lt;i&gt;&lt;/i&gt;&lt;/b&gt;&lt;/p&gt;","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Plaintiff; Psychology; Social psychology; Law; Political science","score_opus":0.03583411345708032,"score_gpt":0.28829533571464167,"score_spread":0.25246122225756135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408659263","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041664943,0.0068408446,0.0068656965,0.45120275,0.0022474835,0.00013314976,0.000733419,0.00012954419,0.49018216],"genre_scores_gemma":[0.849374,0.00427929,0.0063894056,0.08962608,0.0018235829,0.0003700797,0.0002315161,0.00011611092,0.047789905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.98247886,0.009365991,0.0019092002,0.0021249275,0.003190457,0.0009305892],"domain_scores_gemma":[0.79688513,0.1785597,0.008439707,0.007524474,0.005819009,0.0027719392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026433285,0.0003055918,0.00076959946,0.0026743207,0.005085612,0.008413439,0.0020001517,0.01810952,0.020776508],"category_scores_gemma":[0.12775625,0.00079350604,0.00048663773,0.0015185825,0.025455298,0.008127138,0.0036916179,0.00966776,0.0036912146],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030505072,0.000097173,0.017363263,0.00057019,0.00009203261,0.0030612436,0.005934217,0.0007438872,0.0016893384,0.60021824,0.21764491,0.1522804],"study_design_scores_gemma":[0.00010991578,0.00017767098,0.033425637,0.0028150186,0.00012916807,0.0034470332,0.011647815,0.003545722,0.0029829796,0.7271528,0.21434811,0.00021811284],"about_ca_topic_score_codex":0.01741278,"about_ca_topic_score_gemma":0.034983728,"teacher_disagreement_score":0.026433285,"about_ca_system_score_codex":0.0036327597,"about_ca_system_score_gemma":0.0042057573,"threshold_uncertainty_score":0.13979429},"labels":[],"label_agreement":null},{"id":"W4409079155","doi":"10.33524/cjar.v25i1.685","title":"Exploring Anonymous Marking to Mitigate Marking Bias: A Self-Study Through Mixed Methods Action Research","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Action Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Conestoga College","funders":"","keywords":"Action research; Psychology; Multimethodology; Action (physics); Social psychology; Mathematics education; Physics","score_opus":0.6188306581507977,"score_gpt":0.5153416068387506,"score_spread":0.10348905131204711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409079155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94475794,0.00029995776,0.040961612,0.001887333,0.00024407495,0.0050423252,0.00011049803,0.00014698331,0.0065491847],"genre_scores_gemma":[0.88282126,0.0004037887,0.10116263,0.0019158463,0.000108057655,0.0071456465,0.000095625735,0.00012233517,0.0062248358],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.920217,0.06795122,0.002098098,0.0027351677,0.0055129114,0.0014856462],"domain_scores_gemma":[0.7899692,0.15900385,0.00994607,0.0149768675,0.021121092,0.0049829255],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10909064,0.0008595192,0.0007368262,0.001976452,0.0066618747,0.0056801005,0.0030142728,0.002511558,0.002022955],"category_scores_gemma":[0.11840989,0.0007588985,0.00085562095,0.0008561131,0.005954661,0.0036003636,0.0042186975,0.0029361548,0.00074791606],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005784307,0.01120554,0.03253836,0.0015122667,0.00012785314,0.0009855004,0.71899164,0.00057169097,0.008287898,0.011303517,0.0036780902,0.21021919],"study_design_scores_gemma":[0.0008143343,0.016759932,0.02797298,0.0021434259,0.00033926297,0.001601677,0.7852277,0.006788346,0.029386,0.018726002,0.10983067,0.00040969174],"about_ca_topic_score_codex":0.0007085037,"about_ca_topic_score_gemma":0.0020793097,"teacher_disagreement_score":0.8909094,"about_ca_system_score_codex":0.0027158998,"about_ca_system_score_gemma":0.005984646,"threshold_uncertainty_score":0.5769334},"labels":[],"label_agreement":null},{"id":"W4409748992","doi":"10.1145/3706598.3714010","title":"The Role of Expertise in Effectively Moderating Harmful Social Media Content","year":2025,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Social media; Content (measure theory); Computer science; Internet privacy; World Wide Web; Mathematics","score_opus":0.015197890499626028,"score_gpt":0.23424660870972452,"score_spread":0.2190487182100985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409748992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96406335,0.000870601,0.011201774,0.0014458189,0.00009474743,0.000074777054,0.00011274394,0.00010620301,0.022030072],"genre_scores_gemma":[0.99621755,0.00010938604,0.0026002103,0.00018897458,0.00006309562,0.000021307887,0.00003129253,0.000021342012,0.0007469618],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9944676,0.0033022529,0.0001980039,0.0008819854,0.0006660563,0.00048409632],"domain_scores_gemma":[0.8581776,0.12535164,0.004664471,0.0030910687,0.004597424,0.00411783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007053562,0.0005187636,0.0006661593,0.0013171384,0.0010953879,0.0025214625,0.0006385874,0.0017276898,0.004432574],"category_scores_gemma":[0.08743398,0.00034386042,0.000279481,0.00049382803,0.00091844465,0.0036632698,0.0023135836,0.0011616672,0.00081082684],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003711468,0.0025038342,0.6437635,0.00084694923,0.0005285612,0.0009586006,0.012483191,0.006081176,0.047885865,0.0049830433,0.0038393366,0.27241442],"study_design_scores_gemma":[0.00041930776,0.0027189597,0.84108776,0.00056079833,0.0011108796,0.0018689275,0.009682226,0.07157425,0.021892821,0.036875807,0.0119636115,0.00024464456],"about_ca_topic_score_codex":0.0033168762,"about_ca_topic_score_gemma":0.004951626,"teacher_disagreement_score":0.007053562,"about_ca_system_score_codex":0.00047262103,"about_ca_system_score_gemma":0.0010548151,"threshold_uncertainty_score":0.03730327},"labels":[],"label_agreement":null},{"id":"W4410049912","doi":"10.1145/3710970","title":"Exploring Algorithmic Resistance: Responses to Social Media Censorship in Activism","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Universitas Brawijaya; Bundesministerium für Bildung und Forschung","keywords":"Censorship; Social media; Political science; Mores; Public relations; Sociology; Politics; Law","score_opus":0.1239332525760814,"score_gpt":0.32959725125050243,"score_spread":0.20566399867442103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410049912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9398636,0.00028784157,0.005066619,0.0042066798,0.000071863906,0.000100095654,0.00005775594,0.000046209647,0.050299328],"genre_scores_gemma":[0.99731785,0.00009486125,0.00061701826,0.00040811772,0.00002957606,0.00007010337,0.000020857518,0.00002384151,0.0014177021],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98546946,0.010953612,0.00031893203,0.0010299121,0.0012718844,0.00095613435],"domain_scores_gemma":[0.94460773,0.044245873,0.0063466113,0.0021317236,0.0016512636,0.0010168194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009462981,0.00050737633,0.00034953255,0.002164428,0.005378919,0.009218124,0.0013077323,0.0028537416,0.004651722],"category_scores_gemma":[0.040587336,0.00038242113,0.00042584178,0.0013537246,0.011760712,0.006674338,0.0077532586,0.003385559,0.00049719476],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017686687,0.00013544931,0.028791448,0.00021282278,0.000045495184,0.00070038857,0.9174694,0.00028972398,0.003075893,0.022436554,0.001083284,0.025582576],"study_design_scores_gemma":[0.000037099406,0.00018944124,0.042464454,0.00038404882,0.00004751458,0.0005355769,0.89263093,0.001898671,0.001899274,0.020136964,0.03971286,0.00006317458],"about_ca_topic_score_codex":0.0016758444,"about_ca_topic_score_gemma":0.0021126834,"teacher_disagreement_score":0.009462981,"about_ca_system_score_codex":0.0023433242,"about_ca_system_score_gemma":0.0010780401,"threshold_uncertainty_score":0.05004561},"labels":[],"label_agreement":null},{"id":"W4410062048","doi":"10.55927/fjmr.v4i4.166","title":"Analyzing and Predicting Emotional Responses in Cyber Bullying Cases: A Deep Learning Approach","year":2025,"lang":"en","type":"article","venue":"Formosa Journal of Multidisciplinary Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wycliffe College","funders":"","keywords":"Psychology; Cyber bullying; Applied psychology; Artificial intelligence; Computer science; Cognitive psychology; World Wide Web; The Internet","score_opus":0.044174282859434796,"score_gpt":0.3523063310419177,"score_spread":0.3081320481824829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410062048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7879678,0.0010663088,0.20415123,0.0011609064,0.0001204698,0.00014683373,0.0007478054,0.0015447754,0.0030938585],"genre_scores_gemma":[0.9784466,0.00022003773,0.018860623,0.00014201649,0.000029931649,0.000042579402,0.0008587162,0.000015633703,0.0013837754],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997373,0.000068362744,0.000019722747,0.00007016402,0.00003935218,0.000065117514],"domain_scores_gemma":[0.9995511,0.00021673087,0.00005035312,0.000035180852,0.00010921748,0.000037273843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007265765,0.0008937969,0.00031546157,0.0010011919,0.0002450499,0.0005550084,0.0005902101,0.00069665775,0.00089545693],"category_scores_gemma":[0.001571377,0.00024272087,0.00050419726,0.00048049755,0.0002647601,0.00061345496,0.00074663456,0.0011674117,0.00031644324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081005093,0.002192511,0.07375989,0.00018903248,0.00033380365,0.0005608983,0.000536427,0.27149895,0.019546697,0.0012976689,0.007267322,0.6220068],"study_design_scores_gemma":[0.0000066989696,0.00007499718,0.0044514737,0.000010962901,0.000027654618,0.000026691563,0.00008598108,0.9925949,0.0017930716,0.0007101021,0.00021085297,0.0000065827203],"about_ca_topic_score_codex":0.006452332,"about_ca_topic_score_gemma":0.007485971,"teacher_disagreement_score":0.006452332,"about_ca_system_score_codex":0.0005639239,"about_ca_system_score_gemma":0.00043133865,"threshold_uncertainty_score":0.012829542},"labels":[],"label_agreement":null},{"id":"W4410281226","doi":"10.2196/69817","title":"Exploring Mental Health Content Moderation and Well-Being Tools on Social Media Platforms: Walkthrough Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Moderation; Mental health; Software walkthrough; Social media; Psychology; Computer science; World Wide Web; Social psychology; Psychotherapist","score_opus":0.1443474686219426,"score_gpt":0.31136497799023116,"score_spread":0.16701750936828857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410281226","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9674388,0.0006644281,0.02173441,0.0007684972,0.000082261344,0.002448222,0.0013600093,0.00022059356,0.0052828593],"genre_scores_gemma":[0.93755895,0.00064933975,0.05273346,0.00034416583,0.000038726783,0.0042574545,0.0011202504,0.00013059188,0.0031670914],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9933897,0.004946364,0.0002701866,0.0004615199,0.00054201885,0.00039019831],"domain_scores_gemma":[0.9569283,0.035148155,0.0022542654,0.0014512786,0.003344599,0.0008734256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010407965,0.0007856704,0.00047199227,0.0035827665,0.0024520562,0.0025353031,0.00065908895,0.0007314751,0.0022871231],"category_scores_gemma":[0.03074973,0.00037584882,0.0006022743,0.001962628,0.0018839514,0.0026694364,0.002761668,0.0009207578,0.0004459624],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004199677,0.00051627046,0.055199362,0.0031878375,0.00007781649,0.0022830516,0.7849201,0.00048649416,0.010027544,0.003307037,0.0054577203,0.1341169],"study_design_scores_gemma":[0.00011070523,0.0013510371,0.11376467,0.0046816575,0.00016062017,0.00096554856,0.7665989,0.0048652804,0.011161171,0.0062262733,0.089901984,0.00021207446],"about_ca_topic_score_codex":0.0031027794,"about_ca_topic_score_gemma":0.011967541,"teacher_disagreement_score":0.010407965,"about_ca_system_score_codex":0.0019262846,"about_ca_system_score_gemma":0.0020494636,"threshold_uncertainty_score":0.05504328},"labels":[],"label_agreement":null},{"id":"W4410282617","doi":"10.18280/ijsse.150310","title":"A Hybrid Semantic Enrichment Approach for Multi-Label Toxic Speech Detection","year":2025,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Speech recognition; Artificial intelligence","score_opus":0.01027643679605816,"score_gpt":0.24430372462241964,"score_spread":0.23402728782636148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410282617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27097857,0.0019183041,0.7017133,0.001101789,0.00045283916,0.0003729464,0.0018070509,0.0089287,0.012726498],"genre_scores_gemma":[0.760162,0.000624722,0.21722628,0.0006691386,0.00016242849,0.00019184164,0.0043747444,0.00018203851,0.016406944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994267,0.00010437933,0.00003380744,0.00019301636,0.00014235526,0.00009973204],"domain_scores_gemma":[0.99927825,0.00019674597,0.00007559048,0.00009716049,0.00031387698,0.000038325004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081900205,0.0012144864,0.0005667277,0.0017820259,0.0005129852,0.0005385033,0.0007173246,0.0009584571,0.0015758143],"category_scores_gemma":[0.0013993337,0.00022219848,0.00059284864,0.00082602777,0.00047119075,0.0017796516,0.0012586215,0.0009996078,0.0011379828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085648714,0.0007383148,0.01231622,0.0003908083,0.00016711515,0.00062351936,0.0003813979,0.033494633,0.08191053,0.0038727457,0.012176854,0.85307133],"study_design_scores_gemma":[0.00003285109,0.00026384275,0.007890107,0.00005746345,0.0001390396,0.00036481256,0.00022906579,0.90739226,0.06622056,0.0060277376,0.011325253,0.000056960173],"about_ca_topic_score_codex":0.0046092966,"about_ca_topic_score_gemma":0.01112984,"teacher_disagreement_score":0.0046092966,"about_ca_system_score_codex":0.0007245523,"about_ca_system_score_gemma":0.0009913155,"threshold_uncertainty_score":0.009164929},"labels":[],"label_agreement":null},{"id":"W4410298309","doi":"10.1016/j.chbr.2026.101154","title":"Towards a Comprehensive Taxonomy of Online Abusive Language Informed by Machine Learning","year":2025,"lang":"en","type":"preprint","venue":"Computers in Human Behavior Reports","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Taxonomy (biology); Computer science; Natural language processing; Artificial intelligence; Psychology; Ecology; Biology","score_opus":0.027884818029311316,"score_gpt":0.29975003805801503,"score_spread":0.2718652200287037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410298309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14149949,0.0033399025,0.82655543,0.005526615,0.00017294644,0.0026019225,0.006371811,0.001475735,0.012456136],"genre_scores_gemma":[0.29457954,0.0018045943,0.68983954,0.00072024687,0.000078407735,0.0024315196,0.008876232,0.00013276628,0.0015372235],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.988632,0.0040978706,0.0021052137,0.0016806701,0.002878812,0.00060538604],"domain_scores_gemma":[0.9637852,0.016550923,0.005329673,0.0040411907,0.009218788,0.0010742754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009633811,0.0014658546,0.0014751892,0.017912328,0.0032568395,0.007928262,0.0024576797,0.002148482,0.0014833912],"category_scores_gemma":[0.032301012,0.00078357913,0.0017948424,0.01240388,0.0032569184,0.011743482,0.005479594,0.0036444978,0.0012199339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019198524,0.0009243104,0.30547738,0.0025707437,0.00029567568,0.0006963427,0.022567844,0.01552508,0.0074917143,0.09523021,0.017206395,0.5318223],"study_design_scores_gemma":[0.0000654699,0.00046305702,0.14039482,0.0030143955,0.0002704341,0.0017036493,0.027670061,0.33365336,0.0070647816,0.40544298,0.07984197,0.00041505042],"about_ca_topic_score_codex":0.01016721,"about_ca_topic_score_gemma":0.013047548,"teacher_disagreement_score":0.017912328,"about_ca_system_score_codex":0.0028735842,"about_ca_system_score_gemma":0.007628253,"threshold_uncertainty_score":0.050949037},"labels":[],"label_agreement":null},{"id":"W4410368588","doi":"10.3138/cjccj-2024-0045","title":"Wanted Words: The Language of Wanted Person Lists","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Linguistics; Natural language processing; Computer science; Psychology; Philosophy","score_opus":0.04796748761831636,"score_gpt":0.2833226899663681,"score_spread":0.23535520234805174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410368588","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64867514,0.0025112769,0.066107914,0.010345162,0.0005409127,0.0002731593,0.0013508716,0.0007480897,0.26944745],"genre_scores_gemma":[0.98314923,0.00041018295,0.006699512,0.00034094905,0.000035768826,0.000040009283,0.00018688249,0.00017708921,0.008960446],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9978325,0.0008745368,0.00010404004,0.00021271968,0.0006751394,0.0003009801],"domain_scores_gemma":[0.9967481,0.0012530703,0.0004415757,0.00020350648,0.0011349686,0.00021877163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00184039,0.00061573356,0.0002215617,0.001590873,0.0048419246,0.0052456274,0.00094541267,0.0009868894,0.005108061],"category_scores_gemma":[0.006517017,0.00023061156,0.00020505891,0.0011882999,0.007768504,0.003922596,0.0020783225,0.001543151,0.0008090601],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020813463,0.00002399528,0.010267149,0.00040635673,0.000009698327,0.0014242015,0.77838945,0.00027367155,0.011491672,0.14179103,0.00828458,0.04743008],"study_design_scores_gemma":[0.00002196697,0.000094342664,0.03293024,0.00070353283,0.000050163577,0.003079174,0.57830685,0.0019323246,0.009324016,0.016786456,0.3565775,0.00019354683],"about_ca_topic_score_codex":0.34305906,"about_ca_topic_score_gemma":0.40747544,"teacher_disagreement_score":0.65694094,"about_ca_system_score_codex":0.00787281,"about_ca_system_score_gemma":0.011634354,"threshold_uncertainty_score":0.6821245},"labels":[],"label_agreement":null},{"id":"W4410387207","doi":"10.59297/vg1gcm05","title":"Social Media Use by Canadian Law Enforcement Agencies","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International ISCRAM Conference","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Law enforcement; Social media; Law; Enforcement; Political science; Criminology; Sociology","score_opus":0.022524070498416173,"score_gpt":0.23621485649537005,"score_spread":0.21369078599695387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410387207","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.903863,0.0025650216,0.0003528124,0.0056737443,0.00014497437,0.00018549243,0.00398389,0.000091715854,0.08313944],"genre_scores_gemma":[0.9851466,0.002361083,0.00032056018,0.0005878885,0.00003631354,0.00004706349,0.0008656999,0.00003327316,0.010601553],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942597,0.000656705,0.0001899025,0.0005187891,0.003165161,0.0012097409],"domain_scores_gemma":[0.98705983,0.0011812802,0.002034396,0.000444194,0.0073565543,0.0019237709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024852054,0.00046946603,0.00041256385,0.009111174,0.013374515,0.0060744267,0.0013747482,0.00083114597,0.007222154],"category_scores_gemma":[0.012671147,0.0005208766,0.00037545167,0.010579749,0.002482618,0.0016534595,0.0028708812,0.001383456,0.00056028174],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001747336,0.0002095258,0.6326508,0.00038313607,0.00013340948,0.00067774317,0.15292977,0.00025217838,0.0016064432,0.0047883997,0.039137278,0.16705666],"study_design_scores_gemma":[0.000014530743,0.000070399525,0.70503414,0.00058395637,0.000079434685,0.00028815493,0.18077709,0.00051838043,0.0005799294,0.00020147895,0.1117098,0.00014268397],"about_ca_topic_score_codex":0.99056596,"about_ca_topic_score_gemma":0.99481887,"teacher_disagreement_score":0.036115583,"about_ca_system_score_codex":0.036115583,"about_ca_system_score_gemma":0.04759707,"threshold_uncertainty_score":0.26203817},"labels":[],"label_agreement":null},{"id":"W4410449603","doi":"10.1017/cfl.2025.5","title":"Meta’s AI moderation and free speech: Ongoing challenges in the Global South","year":2025,"lang":"en","type":"article","venue":"Cambridge Forum on AI Law and Governance","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Moderation; Free speech; Psychology; Cognitive psychology; Computer science; Political science; Social psychology; Law","score_opus":0.024717944844727453,"score_gpt":0.24377541246208528,"score_spread":0.21905746761735784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410449603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62487686,0.0033822572,0.015983684,0.12891752,0.00037289207,0.000102349426,0.00024737604,0.00017965438,0.22593738],"genre_scores_gemma":[0.990756,0.00060791883,0.0012932994,0.0030880705,0.000061923325,0.000045521356,0.00003640163,0.00009860521,0.0040123756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938232,0.003661025,0.0001564556,0.0008937547,0.0008829459,0.00058259454],"domain_scores_gemma":[0.9714965,0.01946658,0.0022047604,0.003281946,0.0021803174,0.0013698367],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.015565165,0.00028826407,0.0004863115,0.0018323263,0.006573376,0.010069517,0.0009891113,0.001817213,0.011052838],"category_scores_gemma":[0.02414153,0.00034910312,0.00033467592,0.0018333216,0.016020028,0.013563572,0.010940197,0.0037053546,0.0010109007],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016100831,0.00011273693,0.029331664,0.00054516667,0.00003775069,0.0010592184,0.39466053,0.0004887917,0.0036103278,0.4165833,0.012475334,0.14093411],"study_design_scores_gemma":[0.000030061297,0.00010497948,0.034855433,0.0012478654,0.000045649107,0.00066980126,0.52197504,0.0015038822,0.0034156947,0.19323698,0.24284019,0.00007438958],"about_ca_topic_score_codex":0.0069123735,"about_ca_topic_score_gemma":0.009590094,"teacher_disagreement_score":0.9934266,"about_ca_system_score_codex":0.0039551067,"about_ca_system_score_gemma":0.00406088,"threshold_uncertainty_score":0.08231747},"labels":[],"label_agreement":null},{"id":"W4410632591","doi":"10.22215/etd/2025-16425","title":"The Presumption of Non-Credibility","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Presumption; Credibility; Political science; Law and economics; Law; Sociology","score_opus":0.007527994556123062,"score_gpt":0.2576182214467694,"score_spread":0.2500902268906463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410632591","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08922897,0.0064005465,0.15663122,0.14828056,0.0017659351,0.0003552956,0.00033880174,0.00010558112,0.59689313],"genre_scores_gemma":[0.96281505,0.0013357459,0.013722708,0.008961712,0.0005285184,0.00026598322,0.00005903896,0.000041442076,0.012269727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.91986686,0.033064894,0.004077281,0.009344799,0.030210078,0.003436078],"domain_scores_gemma":[0.7378005,0.21008208,0.014023916,0.02203579,0.013559625,0.002498067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05764408,0.00044779837,0.0011501389,0.0020206517,0.007089232,0.0121092005,0.0037626815,0.008819234,0.0045066895],"category_scores_gemma":[0.19808403,0.000898187,0.0009076768,0.0011289433,0.068882,0.011072902,0.008442265,0.016046492,0.0013058523],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003257775,0.000023470177,0.0017026266,0.00013716385,0.000017993854,0.00031910933,0.00754165,0.00036269324,0.00037602583,0.9756644,0.0023501806,0.011472149],"study_design_scores_gemma":[0.000033704302,0.00005295129,0.0020846995,0.0008566309,0.00003502297,0.00063834025,0.0034100348,0.0011977204,0.0018656043,0.9450579,0.04470452,0.000062831095],"about_ca_topic_score_codex":0.014168555,"about_ca_topic_score_gemma":0.011144734,"teacher_disagreement_score":0.05764408,"about_ca_system_score_codex":0.008700562,"about_ca_system_score_gemma":0.015243488,"threshold_uncertainty_score":0.3048547},"labels":[],"label_agreement":null},{"id":"W4410806807","doi":"10.1177/14614448251338493","title":"Online toxic speech as positioning acts: Hate as discursive mechanisms for othering and belonging","year":2025,"lang":"en","type":"article","venue":"New Media & Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Royal Roads University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Sociology; Ableism; Media studies; Gender studies; Social psychology; Psychology","score_opus":0.008907787982984788,"score_gpt":0.2542399672407359,"score_spread":0.2453321792577511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410806807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8968225,0.0009768846,0.013110689,0.001256142,0.00007799035,0.0000616267,0.00008946218,0.00006455997,0.08754024],"genre_scores_gemma":[0.99672455,0.00016797641,0.00096024544,0.000054036296,0.000020599193,0.000012464658,0.00001947146,0.000015082927,0.0020257372],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99813926,0.0011357161,0.00007052881,0.0001494876,0.00033501102,0.00016986627],"domain_scores_gemma":[0.9927014,0.0034562459,0.0020047831,0.0010402125,0.0004027505,0.000394621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001847899,0.0004521497,0.00023442732,0.0016451824,0.003101763,0.005281121,0.0004679048,0.00081514457,0.0034029975],"category_scores_gemma":[0.007007222,0.00022291378,0.00024388469,0.0008834238,0.0074581406,0.0045707608,0.0041387705,0.0010970107,0.00032782985],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032823335,0.000113651404,0.10047854,0.0006529237,0.000094427494,0.001746355,0.658256,0.00053512404,0.016229657,0.12923658,0.001717779,0.09061077],"study_design_scores_gemma":[0.000032934142,0.0003164006,0.20915788,0.0007479025,0.00016868801,0.0029905944,0.62882936,0.003928109,0.008638314,0.042218294,0.10277946,0.00019210637],"about_ca_topic_score_codex":0.0021419546,"about_ca_topic_score_gemma":0.0036686743,"teacher_disagreement_score":0.005281121,"about_ca_system_score_codex":0.001252724,"about_ca_system_score_gemma":0.00076886197,"threshold_uncertainty_score":0.011384189},"labels":[],"label_agreement":null},{"id":"W4410876142","doi":"10.1007/s10207-025-01066-4","title":"A Data-centric approach for safe and secure large language models against threatening and toxic content","year":2025,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Cryptography; Computer security; Internet privacy","score_opus":0.02298082640046019,"score_gpt":0.27295955080452894,"score_spread":0.24997872440406876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410876142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040758606,0.00013200751,0.9889464,0.0005539552,0.000085718486,0.00014940518,0.00038042644,0.0051111802,0.0005650548],"genre_scores_gemma":[0.27258673,0.00024515524,0.7171371,0.0010875207,0.00026492967,0.00063929183,0.0021757563,0.0014991385,0.0043643233],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99028355,0.0026205087,0.0010789958,0.0016970729,0.0036248476,0.00069501495],"domain_scores_gemma":[0.9636385,0.011459155,0.0015995568,0.01816926,0.0044316687,0.00070191844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008597138,0.0013799776,0.0018005513,0.0022272735,0.0016313058,0.005565112,0.005926039,0.003115974,0.005837755],"category_scores_gemma":[0.032712862,0.00185783,0.0027515457,0.0019255053,0.0029177025,0.01272886,0.011088143,0.006412003,0.0046495744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002224969,0.00079736667,0.005416274,0.00048715653,0.00073116034,0.0007140661,0.001302534,0.1935542,0.036364153,0.17813988,0.02885703,0.5514112],"study_design_scores_gemma":[0.00006275577,0.000104853076,0.000164824,0.000028396465,0.00008695419,0.00011722723,0.00014542935,0.86956036,0.015792418,0.10813944,0.005755058,0.000042174],"about_ca_topic_score_codex":0.003622484,"about_ca_topic_score_gemma":0.0063213003,"teacher_disagreement_score":0.008597138,"about_ca_system_score_codex":0.0023359403,"about_ca_system_score_gemma":0.0051460103,"threshold_uncertainty_score":0.045466542},"labels":[],"label_agreement":null},{"id":"W4410940256","doi":"10.2196/70881","title":"Comparing Media and Law Enforcement Reports on Anti-Asian Hate Incidents During the COVID-19 Pandemic: Data Visualization Approach","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Law enforcement; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Political science; Visualization; Criminology; Law; Sociology; Computer science; Virology; World Wide Web; Data mining; Medicine; Infectious disease (medical specialty)","score_opus":0.13348636969423364,"score_gpt":0.4226867584783683,"score_spread":0.2892003887841347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410940256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8427771,0.0019409433,0.05182207,0.0035734985,0.0003465632,0.0011430563,0.07352002,0.0055512176,0.019325543],"genre_scores_gemma":[0.8740168,0.0007090295,0.1041959,0.00013663727,0.000100975376,0.0011115002,0.018056158,0.00018614151,0.0014867532],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963942,0.0015673961,0.00060036633,0.00051766954,0.000721783,0.00019863207],"domain_scores_gemma":[0.9785458,0.011839861,0.004300272,0.0013168505,0.0034527143,0.0005446458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006915621,0.000805335,0.0005655998,0.021655833,0.00075873476,0.004198243,0.0008689935,0.0007419261,0.0026615316],"category_scores_gemma":[0.022306127,0.00042396982,0.00077902444,0.016936105,0.0005143562,0.002661295,0.0025141179,0.00091830996,0.00055863196],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087046926,0.0005282939,0.6990164,0.001994486,0.00073504035,0.0006855533,0.029150913,0.00708024,0.0048997463,0.007492898,0.026655994,0.2208899],"study_design_scores_gemma":[0.00008911091,0.00043678257,0.7652477,0.0016760236,0.00043412187,0.00076915935,0.069173425,0.07419739,0.0073141432,0.009083979,0.0712602,0.00031785853],"about_ca_topic_score_codex":0.010424859,"about_ca_topic_score_gemma":0.014189732,"teacher_disagreement_score":0.021655833,"about_ca_system_score_codex":0.0009881167,"about_ca_system_score_gemma":0.0014966191,"threshold_uncertainty_score":0.036573708},"labels":[],"label_agreement":null},{"id":"W4410992345","doi":"10.25148/lawrev.19.3.6","title":"Should Hate Speech be Criminalized? Lessons from the Canadian experience in R v. Zundel and R v. Keegstra","year":2025,"lang":"en","type":"article","venue":"FIU Law Review","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Political science; Law; Criminology; Sociology","score_opus":0.09134716082655887,"score_gpt":0.347150559292127,"score_spread":0.25580339846556815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410992345","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1771859,0.032838896,0.0011275676,0.5188108,0.0024929566,0.00018626677,0.0005285771,0.000076728946,0.26675227],"genre_scores_gemma":[0.8081473,0.035739582,0.001182205,0.099282965,0.00050132006,0.000050822764,0.00019738456,0.00012839661,0.05476995],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98927444,0.0016087112,0.0003244423,0.0008722596,0.004265651,0.003654546],"domain_scores_gemma":[0.97921133,0.005110307,0.0010452357,0.0005018366,0.009089383,0.0050419318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008386268,0.0006204265,0.000775796,0.0021207125,0.03404046,0.008816078,0.0030441438,0.006460388,0.0060160123],"category_scores_gemma":[0.021783479,0.0005229184,0.0006428295,0.0031313822,0.014623832,0.0030841539,0.003028651,0.011901579,0.0005368382],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017210735,0.0002761121,0.036901318,0.0007427748,0.000090531474,0.007759428,0.2093756,0.00050321105,0.0012062371,0.17754923,0.40423605,0.16118737],"study_design_scores_gemma":[0.000054625136,0.000108810076,0.039700672,0.0013947801,0.0001545586,0.0026931858,0.18620637,0.00030335653,0.00076683244,0.00722373,0.7610425,0.00035061606],"about_ca_topic_score_codex":0.9941855,"about_ca_topic_score_gemma":0.998304,"teacher_disagreement_score":0.11395312,"about_ca_system_score_codex":0.11395312,"about_ca_system_score_gemma":0.21155001,"threshold_uncertainty_score":0.82679164},"labels":[],"label_agreement":null},{"id":"W4411119799","doi":"10.18653/v1/2025.naacl-long.118","title":"Fine-Tuned LLMs are “Time Capsules” for Tracking Societal Bias Through Books","year":2025,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Tracking (education); Computer science; Political science; Sociology","score_opus":0.0365675797256866,"score_gpt":0.2754714269060195,"score_spread":0.23890384718033292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411119799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54519296,0.00726517,0.25600648,0.003923693,0.0013534682,0.00042143403,0.04085188,0.029795319,0.115189664],"genre_scores_gemma":[0.89367604,0.0005091048,0.080433466,0.0003425251,0.0003023892,0.00018984635,0.008264379,0.0012331028,0.015049197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995127,0.00013616879,0.00002559519,0.00015769349,0.0001235398,0.000044257056],"domain_scores_gemma":[0.9969796,0.0013158252,0.0004175732,0.0005363945,0.0005879338,0.00016278634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008581488,0.00039649356,0.00045923545,0.0028425085,0.0005070705,0.0021417437,0.00078138284,0.0005695878,0.01731049],"category_scores_gemma":[0.011036037,0.00027263662,0.00026688926,0.0028242841,0.0003257062,0.0033714287,0.0014794786,0.0006954438,0.00940384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009814936,0.00019120237,0.082769394,0.00033855264,0.00017724051,0.00010763701,0.0012533314,0.0048902333,0.0074846228,0.011076518,0.084473364,0.8062564],"study_design_scores_gemma":[0.0001535489,0.0004254173,0.24206194,0.00027294352,0.0002381973,0.0003313997,0.003324857,0.46129784,0.019379228,0.06219095,0.21010657,0.00021712302],"about_ca_topic_score_codex":0.0041888584,"about_ca_topic_score_gemma":0.0134027265,"teacher_disagreement_score":0.01731049,"about_ca_system_score_codex":0.0006132562,"about_ca_system_score_gemma":0.00036397937,"threshold_uncertainty_score":0.05790937},"labels":[],"label_agreement":null},{"id":"W4411120416","doi":"10.18653/v1/2025.naacl-long.92","title":"AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages","year":2025,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; DeepMind; Universität Hamburg; International Development Research Centre; Rockefeller Foundation","keywords":"Computer science; Natural language processing; Artificial intelligence; Linguistics; Speech recognition","score_opus":0.0075243181420890795,"score_gpt":0.2785270477125188,"score_spread":0.2710027295704297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411120416","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08523067,0.0035502366,0.005331473,0.0015958035,0.0008779582,0.00054567266,0.88629353,0.0050358707,0.011538815],"genre_scores_gemma":[0.047030386,0.0005791189,0.006682759,0.00018565272,0.00014397666,0.000875325,0.93989,0.00028861716,0.0043241386],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985619,0.00041935063,0.00020424151,0.00026834244,0.00030830933,0.00023784865],"domain_scores_gemma":[0.9972595,0.0007221299,0.0003460039,0.00064034597,0.00065148907,0.00038062315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001691899,0.001492983,0.0009463495,0.0061529046,0.0018728292,0.0014624483,0.0016249956,0.0019016992,0.008381484],"category_scores_gemma":[0.0058582355,0.00048852747,0.0009202048,0.0034323938,0.00057691766,0.0026678296,0.0041287593,0.0013701629,0.009879515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010373235,0.0005948276,0.056050394,0.0024617785,0.00033019643,0.0012142585,0.0027652464,0.0009641456,0.0066484436,0.0020635899,0.80538267,0.120487146],"study_design_scores_gemma":[0.00031797175,0.00022613793,0.26642138,0.0008830106,0.00024506013,0.0019091537,0.008234546,0.005418384,0.0062312842,0.0017612303,0.70808774,0.00026407625],"about_ca_topic_score_codex":0.018545171,"about_ca_topic_score_gemma":0.03694308,"teacher_disagreement_score":0.018545171,"about_ca_system_score_codex":0.0007285713,"about_ca_system_score_gemma":0.0015528883,"threshold_uncertainty_score":0.036874473},"labels":[],"label_agreement":null},{"id":"W4411312065","doi":"10.53762/28ad7w69","title":"10.53762/28ad7w69","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Freedom of expression; Expression (computer science); Linguistics; Communication; Speech recognition; Psychology; Computer science; Political science; Philosophy; Law; Programming language","score_opus":0.004007725519180681,"score_gpt":0.16191855005452932,"score_spread":0.15791082453534863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411312065","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007501356,0.00028543806,0.00084464496,0.000550019,0.00037957585,0.00008230924,0.0010586696,0.0012510072,0.9947982],"genre_scores_gemma":[0.0019093886,0.0001756355,0.00038949886,0.00024006367,0.000059050584,0.000041049298,0.00044002754,0.00022029396,0.996525],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994419,0.00004817012,0.00004407223,0.00021153924,0.0001481725,0.00010604227],"domain_scores_gemma":[0.99850935,0.00034953732,0.00006429937,0.00027320994,0.00044888307,0.00035478937],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00064196833,0.001647671,0.00081795186,0.0022407924,0.0019748586,0.0040337327,0.0012319924,0.0032017024,0.9831217],"category_scores_gemma":[0.0014221139,0.00063481904,0.00077626377,0.001910806,0.0013332525,0.0028748754,0.0031384514,0.0015340479,0.9830672],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022555773,0.00019048847,0.0010912659,0.000307248,0.000016908361,0.00039308582,0.00012850041,0.00022535647,0.0027180174,0.0033543897,0.44370526,0.547644],"study_design_scores_gemma":[0.00003494369,0.00003826758,0.0013360793,0.0002081917,0.000008490984,0.00027930157,0.0001693037,0.0002746802,0.0005381505,0.00083036546,0.99626154,0.000020630705],"about_ca_topic_score_codex":0.003912924,"about_ca_topic_score_gemma":0.0042993985,"teacher_disagreement_score":0.016878307,"about_ca_system_score_codex":0.0010272753,"about_ca_system_score_gemma":0.00072404084,"threshold_uncertainty_score":0.024074852},"labels":[],"label_agreement":null},{"id":"W4411672958","doi":"10.31219/osf.io/8ys9q_v1","title":"Moderating Tamil Content on Social Media","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tamil; Content (measure theory); Social media; Media content; Mathematics; Computer science; Multimedia; World Wide Web; Linguistics; Philosophy","score_opus":0.08144330657078255,"score_gpt":0.2743127749548669,"score_spread":0.19286946838408434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411672958","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906533,0.00015421401,0.000651195,0.0005515416,0.000031781885,0.0000990947,0.00025017277,0.00001878989,0.0075899567],"genre_scores_gemma":[0.99852717,0.00007322133,0.00040280013,0.00009947736,0.00001926969,0.00009570457,0.000083394814,0.000023456792,0.00067539234],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945832,0.0039559514,0.00014260813,0.00041986632,0.0004266589,0.00047164492],"domain_scores_gemma":[0.9542232,0.038185026,0.003568988,0.001353513,0.0017764413,0.00089274114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052378126,0.00047149853,0.0003554015,0.0015122484,0.0020034306,0.003811223,0.00050792476,0.0006086705,0.0064807674],"category_scores_gemma":[0.037334237,0.00022833461,0.00024328643,0.001137481,0.0016334272,0.0031703913,0.004869494,0.0011048575,0.000573387],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001035791,0.00063705357,0.6387191,0.0008386996,0.00035763733,0.00051990536,0.28502455,0.00030197532,0.0038295644,0.00723633,0.0024345862,0.059064828],"study_design_scores_gemma":[0.00007162146,0.0007014825,0.7473246,0.00042337112,0.00031739002,0.00019693171,0.22501764,0.0024001638,0.0037093633,0.00421534,0.015556066,0.00006596373],"about_ca_topic_score_codex":0.0023823788,"about_ca_topic_score_gemma":0.003471555,"teacher_disagreement_score":0.0064807674,"about_ca_system_score_codex":0.0008976182,"about_ca_system_score_gemma":0.00057532603,"threshold_uncertainty_score":0.027700543},"labels":[],"label_agreement":null},{"id":"W4411678433","doi":"10.1007/s44204-025-00300-z","title":"Santa, socials, and secrets: hard cases for epistemic rights","year":2025,"lang":"en","type":"article","venue":"Asian Journal of Philosophy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Epistemology; Computer science; Sociology; Philosophy","score_opus":0.014929958203836278,"score_gpt":0.2556611226171103,"score_spread":0.24073116441327405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411678433","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13604291,0.0021202227,0.13051887,0.10826945,0.00054445147,0.00012574813,0.00020731452,0.00014967077,0.6220214],"genre_scores_gemma":[0.9877045,0.00020644465,0.0044279834,0.00093011843,0.00016823094,0.0000561726,0.000021216094,0.000040385512,0.006444927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98844343,0.0068001575,0.00050284015,0.0015163342,0.0014323236,0.0013049747],"domain_scores_gemma":[0.9617742,0.026988307,0.0018964878,0.00594081,0.0015316964,0.0018685407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0144282095,0.0006617003,0.0010093004,0.0022399812,0.009551213,0.0143004265,0.0016514065,0.0077697253,0.016714387],"category_scores_gemma":[0.03863078,0.00073199923,0.0008756825,0.0012151598,0.06905584,0.028296996,0.0092474185,0.007998766,0.0009014377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000925316,0.000008687468,0.00023863441,0.000010218294,0.0000027584538,0.000032519478,0.0015809253,0.000071090944,0.00004005759,0.9959649,0.0005253634,0.0015155274],"study_design_scores_gemma":[0.000007784546,0.000004085906,0.00013354226,0.000025309226,0.0000037791297,0.00004378062,0.0014289994,0.0003964332,0.00007436185,0.99399143,0.0038828526,0.000007709484],"about_ca_topic_score_codex":0.0025784823,"about_ca_topic_score_gemma":0.0029197969,"teacher_disagreement_score":0.016714387,"about_ca_system_score_codex":0.0029644,"about_ca_system_score_gemma":0.0031452838,"threshold_uncertainty_score":0.076304615},"labels":[],"label_agreement":null},{"id":"W4412071690","doi":"10.1080/20008066.2025.2522501","title":"An examination of psychological distress and moral injury in journalists exposed to online harassment","year":2025,"lang":"en","type":"article","venue":"European journal of psychotraumatology","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"CBC (Canada); Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Harassment; Psychological distress; Psychology; Moral injury; Distress; Clinical psychology; Criminology; Social psychology; Applied psychology; Psychiatry; Mental health","score_opus":0.03001846555431741,"score_gpt":0.33073755808074357,"score_spread":0.30071909252642615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412071690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992902,0.00006442261,0.000021099611,0.000046377747,0.0000032003334,0.000022369844,0.000066060566,0.0000010213547,0.00048523323],"genre_scores_gemma":[0.9990404,0.00021200288,0.00013693511,0.00006126021,0.000018352139,0.000035960373,0.00014923509,7.63366e-7,0.00034500894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994924,0.00013371266,0.000060396287,0.000038819566,0.0001358117,0.00013885458],"domain_scores_gemma":[0.99589396,0.00047827297,0.0020971387,0.00008139076,0.0006039373,0.00084525207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000848717,0.00017384838,0.0002457204,0.0016655275,0.001235129,0.0009974408,0.0003067217,0.00045407022,0.001709556],"category_scores_gemma":[0.0037827862,0.0002146942,0.00025412295,0.0010138487,0.00064520637,0.00042440288,0.001030944,0.0007572763,0.00025130008],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006310711,0.00014803572,0.98847896,0.000054247397,0.000013975824,0.00048419487,0.006725073,0.000010728234,0.0006600667,0.000017388782,0.00018370182,0.0031603894],"study_design_scores_gemma":[0.0000013879624,0.00019295751,0.98877954,0.000011366326,0.000003963787,0.0006275589,0.0100523485,0.000012147049,0.0000500262,0.000006897406,0.0002583345,0.0000033490926],"about_ca_topic_score_codex":0.0051914263,"about_ca_topic_score_gemma":0.009713649,"teacher_disagreement_score":0.0051914263,"about_ca_system_score_codex":0.00063636614,"about_ca_system_score_gemma":0.0007693069,"threshold_uncertainty_score":0.010322392},"labels":[],"label_agreement":null},{"id":"W4412153876","doi":"10.1007/978-3-031-97623-0_3","title":"Hiding in Plain Sight: On the Robustness of AI-Generated Code Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Robustness (evolution); Sight; Code (set theory); Artificial intelligence; Computer vision; Programming language; Optics","score_opus":0.01563596583748072,"score_gpt":0.2303640308463903,"score_spread":0.21472806500890956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412153876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13816123,0.0025051478,0.8380523,0.00130356,0.00029681565,0.00008550279,0.0003157988,0.002352794,0.016926894],"genre_scores_gemma":[0.9195574,0.0007050467,0.07315389,0.00025451535,0.00031237898,0.000039374852,0.00027508466,0.0003404686,0.0053617223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99564147,0.0013486715,0.00013691439,0.0007106525,0.0016172624,0.0005449676],"domain_scores_gemma":[0.9507819,0.038341198,0.002309653,0.0056237318,0.00251435,0.00042915376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033220523,0.0011523404,0.001102378,0.0017632053,0.0007610335,0.002462993,0.0025092212,0.0022937888,0.0031994905],"category_scores_gemma":[0.038379803,0.00063184986,0.0007490373,0.0012380754,0.0036188473,0.0047290535,0.003077837,0.002484606,0.0009908057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022743738,0.00021888122,0.0037148106,0.0004450819,0.00026507216,0.0005020165,0.00069560896,0.4163831,0.043996304,0.14508714,0.0090755755,0.37734208],"study_design_scores_gemma":[0.000015244437,0.000092854396,0.0006425739,0.000029530109,0.00003539881,0.00019222236,0.000055371125,0.9270073,0.014450822,0.056568813,0.000873174,0.000036640442],"about_ca_topic_score_codex":0.0026939795,"about_ca_topic_score_gemma":0.0012354262,"teacher_disagreement_score":0.0033220523,"about_ca_system_score_codex":0.0013092852,"about_ca_system_score_gemma":0.00076545274,"threshold_uncertainty_score":0.017568886},"labels":[],"label_agreement":null},{"id":"W4412198823","doi":"10.2196/70613","title":"Expressions of “Ikizurasa” in Posts on X (Formerly Twitter) in Japan in 2023: Descriptive Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Ethnic group; Human sexuality; Psychology; Sociology; Gender studies; Anthropology; Computer science; World Wide Web","score_opus":0.044998710998555416,"score_gpt":0.38452915893532175,"score_spread":0.33953044793676634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412198823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99696285,0.000059595433,0.00006669606,0.00007645742,0.000012199284,0.000028990613,0.0015081248,0.0000051755264,0.0012799063],"genre_scores_gemma":[0.9963013,0.00018606504,0.00025294974,0.000113533584,0.000033872457,0.00013715064,0.0015974755,0.0000090830545,0.001368545],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996376,0.0000788926,0.00006021537,0.000048941307,0.000064304775,0.000109997454],"domain_scores_gemma":[0.99670285,0.0007903486,0.0014363274,0.00011785978,0.0006058626,0.00034669935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009853678,0.0002256372,0.00019866967,0.0014480643,0.0010636497,0.0010217158,0.00025124833,0.000374574,0.001674279],"category_scores_gemma":[0.0029496134,0.00016998664,0.00024350433,0.0015284381,0.000553281,0.0011114527,0.0014610008,0.00053652155,0.00057525013],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028485112,0.00006139903,0.908517,0.0003472915,0.000034084005,0.00045928094,0.07464224,0.000041780633,0.0037435354,0.00016221445,0.0019965863,0.009709722],"study_design_scores_gemma":[0.0000022236188,0.000061308005,0.9092939,0.000056822864,0.000022596903,0.00012660258,0.087407775,0.00010570407,0.0005047461,0.000022112996,0.002380594,0.000015729409],"about_ca_topic_score_codex":0.016298043,"about_ca_topic_score_gemma":0.036684297,"teacher_disagreement_score":0.016298043,"about_ca_system_score_codex":0.0005581824,"about_ca_system_score_gemma":0.0005905297,"threshold_uncertainty_score":0.03240639},"labels":[],"label_agreement":null},{"id":"W4412477565","doi":"10.1080/20414005.2025.2523184","title":"A third-world critique of the international human rights-based approach to content moderation","year":2025,"lang":"en","type":"article","venue":"Transnational Legal Theory","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hebrew University of Jerusalem; Monash University; European Research Council; Yale University; Universiteit Leiden; York University","keywords":"Moderation; Content (measure theory); Human rights; Political science; Law and economics; Law; Sociology; Social psychology; Psychology; Mathematics","score_opus":0.018983768836243267,"score_gpt":0.2617360478790813,"score_spread":0.24275227904283803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412477565","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023061877,0.0051875324,0.17372549,0.32471398,0.0016303008,0.00023150006,0.00014605444,0.0002375328,0.47106576],"genre_scores_gemma":[0.9197774,0.0021447132,0.020081608,0.03710576,0.0014047711,0.0007130976,0.000053014774,0.00030765534,0.018411951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.94696957,0.03534792,0.0013825861,0.006714997,0.007822515,0.0017623592],"domain_scores_gemma":[0.8873261,0.089941144,0.00638011,0.007965242,0.006758491,0.0016290045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05817709,0.0012110383,0.00086589146,0.005394235,0.008849087,0.017099954,0.0030609246,0.008970151,0.006727213],"category_scores_gemma":[0.07973776,0.0007030241,0.00096900563,0.0033771018,0.1058467,0.029027537,0.012165739,0.015407811,0.0009506847],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004918415,0.0000046828122,0.00015915079,0.00004351405,0.000003257071,0.000022333013,0.013416087,0.00004968911,0.000040350064,0.98241293,0.0011635539,0.0026794889],"study_design_scores_gemma":[0.000019862773,0.000021925654,0.0006593502,0.00066567946,0.000019452622,0.00016669673,0.01077396,0.00080930744,0.00051328534,0.85669625,0.12961565,0.000038542097],"about_ca_topic_score_codex":0.0045186942,"about_ca_topic_score_gemma":0.003407616,"teacher_disagreement_score":0.05817709,"about_ca_system_score_codex":0.011561463,"about_ca_system_score_gemma":0.00843,"threshold_uncertainty_score":0.30767357},"labels":[],"label_agreement":null},{"id":"W4412533364","doi":"10.5267/j.ijdns.2024.8.008","title":"Hate speech detection in Arabic social networks using deep learning and fine-tuned embeddings","year":2025,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arabic; Computer science; Deep learning; Speech recognition; Artificial intelligence; Natural language processing; Linguistics; Philosophy","score_opus":0.015813034921748896,"score_gpt":0.3073367260354198,"score_spread":0.2915236911136709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412533364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8337196,0.0017166332,0.15281837,0.0007588051,0.00024022309,0.00008567452,0.00083758665,0.0018629445,0.007960249],"genre_scores_gemma":[0.9793297,0.0002876132,0.015561077,0.00008191036,0.000040002848,0.00002476024,0.00068998366,0.000025137593,0.0039597764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961984,0.00012496818,0.000020735062,0.00008425303,0.00008108147,0.000069161775],"domain_scores_gemma":[0.9991968,0.00034764243,0.00013749112,0.00007843555,0.00018939895,0.000050257808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005150484,0.0009194289,0.0003832874,0.000943907,0.00033682963,0.0006396936,0.0003592094,0.00057982333,0.0010814582],"category_scores_gemma":[0.001974928,0.00017800413,0.00034349,0.00042734112,0.00032140894,0.0013018338,0.00076168904,0.0008221109,0.0009536199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015533312,0.0006300512,0.054817766,0.00031186643,0.0002896195,0.0011504802,0.0015648447,0.12048658,0.037168972,0.0037684536,0.012903853,0.7653543],"study_design_scores_gemma":[0.000007597412,0.0001085365,0.0074051297,0.00002571542,0.000033963166,0.00015909257,0.000391638,0.97599024,0.012377433,0.0016610441,0.0018192608,0.0000203333],"about_ca_topic_score_codex":0.004901818,"about_ca_topic_score_gemma":0.006905604,"teacher_disagreement_score":0.004901818,"about_ca_system_score_codex":0.0005698745,"about_ca_system_score_gemma":0.000278371,"threshold_uncertainty_score":0.009746611},"labels":[],"label_agreement":null},{"id":"W4412662998","doi":"10.1007/978-3-031-78925-0_3","title":"Automated Detection and Mitigation of Toxic Comments Using XLNET Fine-Tuning Model","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Environmental science","score_opus":0.015119085329080014,"score_gpt":0.23381846090928904,"score_spread":0.21869937558020902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412662998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24765387,0.00073653494,0.72234267,0.0009204361,0.000421572,0.00031010984,0.0020813467,0.014785101,0.010748348],"genre_scores_gemma":[0.93058205,0.00014547333,0.060837433,0.00017310513,0.0000866399,0.000107690576,0.0012553197,0.00017886188,0.006633362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995912,0.00007553559,0.000018279929,0.00013462722,0.00009914982,0.00008124509],"domain_scores_gemma":[0.99879444,0.00039622755,0.00013598894,0.00014275052,0.00045528208,0.00007525717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077266153,0.0007709706,0.00075600913,0.0008439296,0.00034994248,0.00083601585,0.0011869372,0.0006508345,0.0028422638],"category_scores_gemma":[0.0020615424,0.0002414392,0.00045427197,0.00034913459,0.0002754868,0.0010078287,0.0006677048,0.00077403995,0.0012073905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067181303,0.00049955194,0.021456838,0.00017452154,0.00013410734,0.0002383662,0.00009984295,0.6788483,0.016334698,0.0037386338,0.010710896,0.26709247],"study_design_scores_gemma":[0.000005156438,0.000027944645,0.0009763081,0.0000048696857,0.000009889654,0.000014710535,0.000012716479,0.9956682,0.0019489466,0.000937569,0.00038815488,0.000005556536],"about_ca_topic_score_codex":0.013273331,"about_ca_topic_score_gemma":0.015891537,"teacher_disagreement_score":0.013273331,"about_ca_system_score_codex":0.00076283887,"about_ca_system_score_gemma":0.0013196579,"threshold_uncertainty_score":0.026392162},"labels":[],"label_agreement":null},{"id":"W4412672597","doi":"10.51519/journalisi.v7i2.1141","title":"Deep Learning and Statistical Models to Analyse Online Misinformation and Hate Speech Impact on African Youth","year":2025,"lang":"en","type":"article","venue":"Journal of Information Systems and Informatics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Misinformation; Computer science; Psychology; Speech recognition; Computer security","score_opus":0.013070830039276973,"score_gpt":0.2622215821833978,"score_spread":0.24915075214412083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412672597","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9306892,0.0008593888,0.06501386,0.0009953087,0.00004382985,0.00005934806,0.0005636692,0.00016001925,0.0016153937],"genre_scores_gemma":[0.991844,0.00019688052,0.0069814785,0.00006022817,0.000014419637,0.000034802786,0.0003418966,0.000010315938,0.0005159866],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948287,0.0002619018,0.00002329106,0.0000696847,0.0000561468,0.00010616612],"domain_scores_gemma":[0.9979361,0.0014445878,0.00023292207,0.00011253272,0.00019189325,0.00008193717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017017468,0.0006123004,0.000391193,0.0009715498,0.00036662523,0.0008128262,0.00039082792,0.00048480567,0.00095103844],"category_scores_gemma":[0.0075218673,0.00025157386,0.00053298037,0.0007502124,0.00041750516,0.00095886627,0.0010182958,0.0012304216,0.00020111362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006733954,0.0008127816,0.5277991,0.00036815982,0.0003849083,0.0004528879,0.0030545034,0.20192711,0.0035595687,0.0076505425,0.0041722907,0.24914469],"study_design_scores_gemma":[0.000010797723,0.00012785279,0.06812539,0.00010402827,0.00006163641,0.000089865636,0.0020050837,0.9193888,0.0012720565,0.007170531,0.0016204295,0.000023508852],"about_ca_topic_score_codex":0.010808487,"about_ca_topic_score_gemma":0.010281036,"teacher_disagreement_score":0.010808487,"about_ca_system_score_codex":0.0005796647,"about_ca_system_score_gemma":0.0007196365,"threshold_uncertainty_score":0.02149117},"labels":[],"label_agreement":null},{"id":"W4412759584","doi":"10.1080/00083968.2025.2492838","title":"Constitutional law and internet regulation in Africa: examining legal tensions in Zimbabwe and Rwanda","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of African Studies / Revue canadienne des études africaines","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science; Law; The Internet; Computer science","score_opus":0.03339415990775767,"score_gpt":0.23192723023622644,"score_spread":0.19853307032846879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412759584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97553194,0.001998449,0.00038178827,0.0025259731,0.00001669852,0.000079163874,0.000036721205,0.000002264939,0.019426921],"genre_scores_gemma":[0.99815494,0.0008476802,0.00012455048,0.00015363659,0.0000070554365,0.00003599754,0.000013900222,0.0000017007073,0.00066053297],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99724483,0.0011891595,0.00013276846,0.00016056652,0.00030690475,0.0009657753],"domain_scores_gemma":[0.99101174,0.0057914946,0.0022356135,0.00018393871,0.0004735796,0.00030363584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039730016,0.00019652919,0.00032650674,0.004849403,0.0066820625,0.005317547,0.0007591039,0.0010967321,0.0040709074],"category_scores_gemma":[0.016115882,0.00038967072,0.00022532631,0.0058581554,0.013253543,0.004421003,0.0033646103,0.0023422139,0.00013889349],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010797792,0.00022855862,0.18268241,0.0004991177,0.000040758776,0.0037872272,0.4080495,0.00056444295,0.0021643066,0.35001737,0.0017629348,0.05009537],"study_design_scores_gemma":[0.000029053383,0.00010491611,0.40287173,0.0015698107,0.00007257373,0.0009262775,0.5320974,0.00091639627,0.0007773179,0.015080591,0.045509223,0.00004460474],"about_ca_topic_score_codex":0.05975083,"about_ca_topic_score_gemma":0.10424369,"teacher_disagreement_score":0.05975083,"about_ca_system_score_codex":0.008139139,"about_ca_system_score_gemma":0.005023285,"threshold_uncertainty_score":0.118806064},"labels":[],"label_agreement":null},{"id":"W4412866618","doi":"10.1101/2025.08.01.25332613","title":"The “Walking for Harm Reduction Through Street Engagement” Social Media Knowledge Translation Strategy","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Harm reduction; Harm; Reduction (mathematics); Social media; Business; Sociology; Psychology; Computer science; Social psychology; Medicine; World Wide Web","score_opus":0.10492976616986695,"score_gpt":0.3327761567806786,"score_spread":0.2278463906108117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412866618","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26777518,0.0010493601,0.2622536,0.0746649,0.0019973344,0.08755789,0.0029164355,0.0027790868,0.29900622],"genre_scores_gemma":[0.5385203,0.0010575224,0.3122571,0.01457164,0.0003560951,0.059056524,0.0021568774,0.00064389425,0.071380004],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97889894,0.014767742,0.0012610931,0.0011038803,0.0024925426,0.0014756768],"domain_scores_gemma":[0.9727126,0.013375574,0.0015083124,0.002521143,0.0063991984,0.0034832126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034060854,0.00091872306,0.00051245396,0.0030180083,0.0051363646,0.005722067,0.0025788846,0.0028131718,0.015546027],"category_scores_gemma":[0.030686475,0.000579011,0.00095330604,0.0018584984,0.0033068531,0.0050665853,0.012080435,0.0035504268,0.007947083],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004528853,0.002502985,0.006354764,0.0036695695,0.00006790361,0.002370719,0.15972282,0.0012347713,0.009846812,0.045071393,0.060654387,0.70805097],"study_design_scores_gemma":[0.00065138645,0.0028902548,0.014890995,0.004080942,0.00023386082,0.0012744627,0.17504187,0.0076845577,0.01913166,0.0410941,0.73278195,0.00024403335],"about_ca_topic_score_codex":0.0040003634,"about_ca_topic_score_gemma":0.0061685448,"teacher_disagreement_score":0.034060854,"about_ca_system_score_codex":0.0047801086,"about_ca_system_score_gemma":0.024463898,"threshold_uncertainty_score":0.18013316},"labels":[],"label_agreement":null},{"id":"W4413312480","doi":"10.1111/1467-9477.70017","title":"Institutional Responses to Threats and Harassment of Academics: Evidence From a Survey Among Political Scientists in Norway","year":2025,"lang":"en","type":"article","venue":"Scandinavian Political Studies","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Harassment; Politics; Political science; Survey research; Criminology; Sociology; Law; Socioeconomics","score_opus":0.07943937622692158,"score_gpt":0.3724547074165243,"score_spread":0.2930153311896027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413312480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99758434,0.0003519967,0.00012153392,0.00030573795,0.000018785173,0.000025758745,0.00016413363,0.0000023069729,0.0014254474],"genre_scores_gemma":[0.9988096,0.00047257709,0.00006549352,0.00016600444,0.000018400819,0.000033789267,0.00009063474,0.0000021498472,0.0003413676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99191624,0.003862236,0.0009031709,0.00063190307,0.0015083364,0.0011780722],"domain_scores_gemma":[0.9588505,0.01423343,0.017054422,0.0013644722,0.0039930744,0.0045042057],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009200059,0.00023424481,0.0005583195,0.002667614,0.0021541896,0.003025189,0.0006976364,0.0008464249,0.002726704],"category_scores_gemma":[0.022356708,0.00031389584,0.0002857543,0.0029711823,0.002877367,0.0011690676,0.002888371,0.0009856528,0.0004776735],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009453094,0.00012096712,0.92460465,0.00018211066,0.00002659323,0.00043318523,0.06496143,0.000046366855,0.0004952396,0.0002512248,0.0007468267,0.008036831],"study_design_scores_gemma":[0.000007874022,0.00016461038,0.8747414,0.00022071924,0.000014207844,0.00018160668,0.11969136,0.00007699925,0.00021205988,0.00020340281,0.0044615674,0.000024204663],"about_ca_topic_score_codex":0.029231448,"about_ca_topic_score_gemma":0.036571626,"teacher_disagreement_score":0.9978458,"about_ca_system_score_codex":0.0023913987,"about_ca_system_score_gemma":0.004351072,"threshold_uncertainty_score":0.058122635},"labels":[],"label_agreement":null},{"id":"W4413366170","doi":"10.5334/ijic.nacic24142","title":"So You Want to Conduct an Online Survey? Strategies for Identifying and Eliminating Fraudulent Responses","year":2025,"lang":"en","type":"article","venue":"International Journal of Integrated Care","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Knowledge management; Computer science; Process management; Internet privacy","score_opus":0.07349029380861205,"score_gpt":0.3726055618402021,"score_spread":0.29911526803159005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413366170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23556122,0.009690455,0.3626478,0.22905654,0.0028458196,0.105304725,0.0058731493,0.004655925,0.044364363],"genre_scores_gemma":[0.22321437,0.005955849,0.6311489,0.050781596,0.0007757153,0.079344444,0.0018586237,0.000425708,0.00649483],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7358869,0.20093304,0.024598455,0.006752758,0.026828863,0.004999915],"domain_scores_gemma":[0.44426933,0.38433853,0.04752548,0.023625001,0.09264927,0.0075923824],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.28445497,0.0012289118,0.0017355236,0.013418024,0.0057597933,0.006508184,0.0043345965,0.0035520399,0.007482911],"category_scores_gemma":[0.41043028,0.0013135975,0.0013845065,0.00928646,0.0030301749,0.0073816497,0.005420819,0.003281423,0.0051246188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037268765,0.0008177115,0.058556102,0.013173191,0.00022571442,0.0015935943,0.09476827,0.0004987542,0.0069429493,0.0072055063,0.08759738,0.72824806],"study_design_scores_gemma":[0.00046421765,0.0023200617,0.08528683,0.03704787,0.0005489219,0.0021212236,0.24323758,0.009324501,0.014258108,0.025002958,0.57982606,0.00056164607],"about_ca_topic_score_codex":0.0101436265,"about_ca_topic_score_gemma":0.022640184,"teacher_disagreement_score":0.71554506,"about_ca_system_score_codex":0.007217082,"about_ca_system_score_gemma":0.027810743,"threshold_uncertainty_score":0.88239443},"labels":[],"label_agreement":null},{"id":"W4413496427","doi":"10.64628/aam.7c9hmx7ee","title":"Trolling and doxxing: Graduate students sharing their research online speak out about hate","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Graduate students; Psychology; Mathematics education; Computer science; Pedagogy","score_opus":0.215161557771776,"score_gpt":0.39883799884034055,"score_spread":0.18367644106856454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413496427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9839813,0.000110311696,0.0025045439,0.0020028718,0.00019761271,0.00006904967,0.0001637842,0.0003209875,0.010649504],"genre_scores_gemma":[0.9857146,0.000154658,0.002288549,0.0012741527,0.00006853398,0.0000655005,0.00028695678,0.000061647144,0.010085489],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99812776,0.00080220203,0.000098028206,0.00026909995,0.00039504017,0.00030789297],"domain_scores_gemma":[0.9901669,0.0031789104,0.0018034736,0.0011005568,0.0009456309,0.002804544],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0023316748,0.000519445,0.0003066331,0.0009069865,0.002660666,0.0031975596,0.00052069814,0.001812545,0.006999171],"category_scores_gemma":[0.0188674,0.00041724675,0.00026933197,0.00048408713,0.0009845687,0.0034106858,0.0031496165,0.0020254557,0.002662523],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000991788,0.0019361154,0.6370236,0.00024931275,0.00017854081,0.0013908081,0.11076453,0.0003522203,0.02679442,0.0023075556,0.03426355,0.18374753],"study_design_scores_gemma":[0.00006569664,0.0017781969,0.6009285,0.0002813472,0.00021114768,0.0034256624,0.30288836,0.007198745,0.017340219,0.0043195332,0.061278753,0.00028389107],"about_ca_topic_score_codex":0.0014244058,"about_ca_topic_score_gemma":0.005051548,"teacher_disagreement_score":0.9973393,"about_ca_system_score_codex":0.00045429167,"about_ca_system_score_gemma":0.00071350904,"threshold_uncertainty_score":0.023414552},"labels":[],"label_agreement":null},{"id":"W4413503313","doi":"10.64628/aam.57md3wytk","title":"Regulating online hate will have unintended, but predictable, consequences","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Unintended consequences; Internet privacy; Computer security; Computer science; Law and economics; Business; Political science; Sociology; Law","score_opus":0.016511161948364564,"score_gpt":0.2306684558851009,"score_spread":0.21415729393673633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413503313","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8557933,0.00048292012,0.015529915,0.0040725195,0.0003547531,0.00018781441,0.0003049708,0.00013704585,0.12313686],"genre_scores_gemma":[0.99667287,0.00008177151,0.0007357162,0.0004736656,0.000029154888,0.00003349961,0.000024024599,0.0000055749842,0.0019437354],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980204,0.0005196835,0.000070140915,0.00030161176,0.0006677373,0.00042047963],"domain_scores_gemma":[0.9856736,0.005751135,0.004333096,0.0015261539,0.00183742,0.0008786284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027274615,0.00038610795,0.00027299765,0.00038868384,0.0009680995,0.002228271,0.00034196122,0.0016274729,0.006116073],"category_scores_gemma":[0.015403256,0.00025687268,0.0003121688,0.00016376471,0.0016222463,0.0010937246,0.00089078635,0.0015352161,0.00092094345],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009704805,0.0034724278,0.65126354,0.0005193253,0.00045501147,0.0007295192,0.0047860993,0.006517049,0.05357151,0.084814966,0.012518776,0.1803813],"study_design_scores_gemma":[0.000100591984,0.0024140074,0.847548,0.00039984594,0.0003121758,0.0008499652,0.0072538876,0.019341918,0.051128794,0.05368638,0.016830685,0.00013373519],"about_ca_topic_score_codex":0.0025236665,"about_ca_topic_score_gemma":0.0041258177,"teacher_disagreement_score":0.006116073,"about_ca_system_score_codex":0.0010174065,"about_ca_system_score_gemma":0.0014300621,"threshold_uncertainty_score":0.020460248},"labels":[],"label_agreement":null},{"id":"W4413506436","doi":"10.64628/aam.cd6xagdyw","title":"Canada’s legal disinformation pandemic is exposed by the ‘freedom convoy’","year":2022,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Disinformation; Pandemic; Political science; Coronavirus disease 2019 (COVID-19); Computer security; Law; Computer science; Social media; Medicine","score_opus":0.00838157308425259,"score_gpt":0.18659535478424047,"score_spread":0.1782137816999879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413506436","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25206742,0.0026442835,0.0018563863,0.36770183,0.0044400063,0.00018452591,0.0055844765,0.0004511249,0.36507],"genre_scores_gemma":[0.812696,0.0014371333,0.001319111,0.06068909,0.00052851165,0.000042662065,0.001751779,0.00012583799,0.12140986],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968766,0.00019584007,0.00003823603,0.00015910392,0.0016175695,0.0011125634],"domain_scores_gemma":[0.9933316,0.00078644697,0.0006012614,0.00020851611,0.0030668993,0.0020051317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013886152,0.000296201,0.000258437,0.001808646,0.01085604,0.0055512697,0.00090911915,0.003601539,0.012261032],"category_scores_gemma":[0.008697551,0.0003189952,0.000433125,0.0014278397,0.0036544327,0.0013135644,0.0020013028,0.005293535,0.0012123667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116806754,0.000084981395,0.09330718,0.0000912501,0.000054783617,0.000908515,0.008184203,0.0004738347,0.0007509741,0.028069671,0.81118757,0.056770336],"study_design_scores_gemma":[0.000033092987,0.000057414247,0.18374404,0.00022250692,0.00003586237,0.0003069685,0.021257186,0.001215455,0.0009170244,0.00353666,0.78855294,0.00012086399],"about_ca_topic_score_codex":0.98719573,"about_ca_topic_score_gemma":0.99441904,"teacher_disagreement_score":0.030632658,"about_ca_system_score_codex":0.030632658,"about_ca_system_score_gemma":0.094353825,"threshold_uncertainty_score":0.22225654},"labels":[],"label_agreement":null},{"id":"W4413521891","doi":"10.64628/aam.keffky5f9","title":"Far-right Hindu nationalists are using digital propaganda to delegitimize India’s wrestler protests","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hinduism; Law; Political science; Sociology; History; Religious studies; Philosophy","score_opus":0.024547093132362575,"score_gpt":0.25723241149687187,"score_spread":0.23268531836450929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413521891","genre_codex":"empirical","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6662184,0.0013287971,0.0016058089,0.020195514,0.0009196044,0.00007504064,0.0002994329,0.0003045901,0.30905282],"genre_scores_gemma":[0.9568478,0.00058461924,0.0009815388,0.0049626497,0.00016203795,0.000025172421,0.000115433046,0.000038370843,0.036282256],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99879384,0.00030811783,0.00002686269,0.000081451086,0.0003654268,0.00042427419],"domain_scores_gemma":[0.9964946,0.0012787317,0.00072802504,0.00030431806,0.0004345698,0.000759716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012032631,0.00034822346,0.00013447875,0.0014910231,0.0063931625,0.0054276097,0.00053105975,0.0010566637,0.012363831],"category_scores_gemma":[0.0034510137,0.00022694402,0.00019739401,0.001251039,0.0026916591,0.0013371056,0.002165109,0.0030358022,0.0025217312],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004092548,0.0007907468,0.22116852,0.00079240877,0.00012067739,0.0023879763,0.1713561,0.00040707673,0.012575959,0.105113134,0.16424067,0.32063743],"study_design_scores_gemma":[0.0000311284,0.000283591,0.20044191,0.0005117992,0.00009700412,0.002166781,0.15787609,0.00083256856,0.009039627,0.0051071486,0.62347984,0.00013256932],"about_ca_topic_score_codex":0.009841061,"about_ca_topic_score_gemma":0.032017305,"teacher_disagreement_score":0.012363831,"about_ca_system_score_codex":0.0018748705,"about_ca_system_score_gemma":0.0026563583,"threshold_uncertainty_score":0.041361094},"labels":[],"label_agreement":null},{"id":"W4413526150","doi":"10.64628/aam.hxtpwgk5h","title":"Cyberbullying girls with pornographic deepfakes is a form of misogyny","year":2023,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Psychology","score_opus":0.010631360994976179,"score_gpt":0.21418249805350634,"score_spread":0.20355113705853015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413526150","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9924148,0.00019471788,0.0001908847,0.0005263584,0.000042152,0.00002656922,0.00014542128,0.000014570955,0.0064445166],"genre_scores_gemma":[0.99696904,0.00019811236,0.00015800694,0.0001465677,0.0000207435,0.000009657421,0.00011266215,0.000011210435,0.0023740472],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948657,0.00008046477,0.000033188302,0.00010340516,0.00015238473,0.00014385606],"domain_scores_gemma":[0.9980793,0.00018744722,0.0010824043,0.000090105656,0.00020141611,0.00035937343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026902658,0.0002693985,0.00035328482,0.0011501192,0.0014782104,0.0010984234,0.00031201108,0.00062413677,0.0064462814],"category_scores_gemma":[0.0028070228,0.00023987456,0.00018867615,0.00078405236,0.0009770618,0.00079647294,0.001143498,0.0011439824,0.0007642793],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011104863,0.00019837721,0.9705734,0.00005069661,0.000019328927,0.0047296346,0.0058164294,0.00002632866,0.002555813,0.000688482,0.0017575783,0.013472763],"study_design_scores_gemma":[0.000005650587,0.000095379306,0.9613702,0.00008340746,0.000022978498,0.012146791,0.02059232,0.00016249636,0.00090628356,0.00037737782,0.004222024,0.000014985229],"about_ca_topic_score_codex":0.005164935,"about_ca_topic_score_gemma":0.010551499,"teacher_disagreement_score":0.0064462814,"about_ca_system_score_codex":0.0004508338,"about_ca_system_score_gemma":0.00060129724,"threshold_uncertainty_score":0.0215649},"labels":[],"label_agreement":null},{"id":"W4413544951","doi":"10.64628/aam.6qnd47nm6","title":"Scapegoating George Soros: How media-savvy far-right activists spread lies","year":2019,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Scapegoating; George (robot); Far right; Political science; Law; Media studies; Criminology; Sociology; History; Politics; Art history","score_opus":0.0071716931020534575,"score_gpt":0.1972422132328398,"score_spread":0.19007052013078635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413544951","genre_codex":"empirical","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7840423,0.000985445,0.0041624242,0.038240712,0.0012450732,0.00007191572,0.0001310339,0.00029829209,0.17082287],"genre_scores_gemma":[0.96178675,0.00029015532,0.00077373575,0.0041632513,0.000111108675,0.000024944691,0.00006368769,0.00013017267,0.032656144],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815553,0.00088130136,0.000030863546,0.00020987031,0.0002977575,0.00042474247],"domain_scores_gemma":[0.99504143,0.0022324978,0.0005348364,0.00029650773,0.00048119825,0.0014135026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001803339,0.00050238793,0.00026174073,0.0011370091,0.010838552,0.0064259497,0.001055745,0.0039556627,0.01578905],"category_scores_gemma":[0.010473878,0.000286451,0.0002452565,0.00057089626,0.004405899,0.0048837997,0.0049698395,0.004978295,0.0033000456],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038149822,0.00046500584,0.062627815,0.00022786057,0.000088051565,0.004019982,0.6438257,0.00056624645,0.0068586273,0.037406527,0.10767993,0.13585281],"study_design_scores_gemma":[0.000020959362,0.00015257651,0.021901386,0.0003557925,0.000050682414,0.0013510255,0.7189096,0.0020382383,0.0022667015,0.013264644,0.23960389,0.00008450821],"about_ca_topic_score_codex":0.009476551,"about_ca_topic_score_gemma":0.026361277,"teacher_disagreement_score":0.01578905,"about_ca_system_score_codex":0.0013279287,"about_ca_system_score_gemma":0.0015653193,"threshold_uncertainty_score":0.05281967},"labels":[],"label_agreement":null},{"id":"W4413571627","doi":"10.64628/aam.7fs7y5vhp","title":"Not two different worlds: QAnon and the offline dangers of online speech","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Roads University","funders":"","keywords":"Online and offline; Computer science","score_opus":0.016913004330799272,"score_gpt":0.25493637429312,"score_spread":0.2380233699623207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413571627","genre_codex":"empirical","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8015702,0.004887616,0.03697029,0.055586923,0.0005231386,0.00003879153,0.0003090724,0.00008243444,0.10003156],"genre_scores_gemma":[0.9938845,0.0003505257,0.0012757882,0.0006838937,0.00013544143,0.0000056840913,0.00002497037,0.000022322625,0.0036168194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988846,0.00052588264,0.000029968503,0.00022089119,0.00019389177,0.00014469498],"domain_scores_gemma":[0.9914688,0.005566583,0.0010257072,0.000809711,0.0006285975,0.00050072087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019898952,0.00023542969,0.00044314176,0.0013444037,0.0021356356,0.0051533733,0.0004950195,0.0025372042,0.010771232],"category_scores_gemma":[0.013737003,0.00044685302,0.0002863314,0.00072053104,0.0050498974,0.012090585,0.0026343523,0.0037066909,0.0006313325],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014446814,0.0004945708,0.2511183,0.00024925507,0.00019570898,0.002814119,0.040013313,0.002712596,0.009172691,0.4685583,0.01383414,0.20939231],"study_design_scores_gemma":[0.000057520047,0.00031711513,0.19071655,0.0003807803,0.00013234602,0.004752035,0.049429294,0.027032305,0.0030872282,0.69844764,0.025484405,0.0001626636],"about_ca_topic_score_codex":0.004891534,"about_ca_topic_score_gemma":0.0071583544,"teacher_disagreement_score":0.010771232,"about_ca_system_score_codex":0.0006390403,"about_ca_system_score_gemma":0.0005347729,"threshold_uncertainty_score":0.036033392},"labels":[],"label_agreement":null},{"id":"W4413574652","doi":"10.64628/aam.5pyjdyurw","title":"Facebook’s latest federal election integrity initiative is just another marketing tactic","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Federal election; Internet privacy; Business; Computer security; Advertising; Political science; Computer science; Politics; Law","score_opus":0.046623310677841194,"score_gpt":0.27952649269823643,"score_spread":0.23290318202039523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413574652","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13270995,0.0011840711,0.022104079,0.08566318,0.007406718,0.0004132938,0.00244652,0.0054045743,0.74266756],"genre_scores_gemma":[0.65607053,0.0004517928,0.014675141,0.021879869,0.003759013,0.00027011248,0.0019045691,0.0009746178,0.30001444],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99602747,0.0011823684,0.00010948403,0.000391824,0.0018279231,0.00046098235],"domain_scores_gemma":[0.98567885,0.0059728017,0.0014904838,0.0030528584,0.002075802,0.0017292231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003632062,0.00053490157,0.0004837974,0.0017664983,0.004974692,0.005823067,0.0006522614,0.0032980999,0.03264888],"category_scores_gemma":[0.015694985,0.0005040752,0.00031645532,0.0007960014,0.0012633499,0.0051404135,0.0021021804,0.0041370434,0.015170179],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004891239,0.00064885244,0.03079267,0.00014033375,0.000086728396,0.0002761094,0.0024328604,0.0002809282,0.009612689,0.058405437,0.6365624,0.2602719],"study_design_scores_gemma":[0.000042050404,0.0002338397,0.04769177,0.000120294055,0.00007708208,0.00068934675,0.002315847,0.003955082,0.019246899,0.015677128,0.9098208,0.00012994056],"about_ca_topic_score_codex":0.004721113,"about_ca_topic_score_gemma":0.016735442,"teacher_disagreement_score":0.03264888,"about_ca_system_score_codex":0.0010087289,"about_ca_system_score_gemma":0.0008922308,"threshold_uncertainty_score":0.1092214},"labels":[],"label_agreement":null},{"id":"W4413576249","doi":"10.64628/aam.ghreychak","title":"Mandatory COVID-19 vaccination in schools this fall? Ontario’s 1982 legislation spurred organized opposition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Opposition (politics); Legislation; Coronavirus disease 2019 (COVID-19); Political science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Law; Medicine; Outbreak; Politics; Infectious disease (medical specialty)","score_opus":0.02348769294009901,"score_gpt":0.2658098374409726,"score_spread":0.24232214450087358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413576249","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40779927,0.0014303548,0.001415869,0.3077762,0.004613784,0.00056712824,0.0012278743,0.00022318367,0.2749463],"genre_scores_gemma":[0.67925906,0.00066071685,0.0010413019,0.07385289,0.00057144894,0.00022800751,0.00027874962,0.00009474279,0.24401322],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99042517,0.0007135703,0.00019883287,0.00064729614,0.0026678748,0.005347295],"domain_scores_gemma":[0.98830473,0.00170585,0.00088395126,0.0003108028,0.0032499686,0.005544685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048234942,0.00034176765,0.0004591586,0.001088163,0.016579365,0.004481506,0.0015451486,0.009812448,0.014194048],"category_scores_gemma":[0.010504896,0.00082124537,0.00068840175,0.0009595645,0.0036008356,0.0014430329,0.0029939532,0.008698921,0.0016138363],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006146366,0.0006199658,0.16425376,0.00042067486,0.00006881079,0.0041743102,0.06630922,0.0004822293,0.009314794,0.09215651,0.5974055,0.064179614],"study_design_scores_gemma":[0.00011558645,0.00022824497,0.26964888,0.00035596424,0.000058470763,0.00020057384,0.02860199,0.0005252887,0.0028499202,0.0021427537,0.6951739,0.000098341596],"about_ca_topic_score_codex":0.92638,"about_ca_topic_score_gemma":0.9800433,"teacher_disagreement_score":0.07362002,"about_ca_system_score_codex":0.053573407,"about_ca_system_score_gemma":0.09571633,"threshold_uncertainty_score":0.38870412},"labels":[],"label_agreement":null},{"id":"W4413674321","doi":"10.64628/aam.un5vqykf4","title":"How not to defend free speech","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Free speech; Computer science; Speech recognition; Political science; Law","score_opus":0.025415540351721846,"score_gpt":0.25307872007716836,"score_spread":0.22766317972544653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413674321","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13039485,0.004765455,0.16668075,0.48941132,0.0069641373,0.00017324233,0.0011492164,0.0020444975,0.19841664],"genre_scores_gemma":[0.908411,0.0011707104,0.024268404,0.018867278,0.0011761402,0.00008594347,0.00035166624,0.0005814514,0.04508737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945332,0.0024911163,0.0002318809,0.0010892127,0.0011556965,0.0004988767],"domain_scores_gemma":[0.9637804,0.019348253,0.0015993484,0.0060304175,0.00668265,0.0025588097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007965381,0.0005984694,0.0008877799,0.0010217805,0.0023373542,0.007189593,0.0013828751,0.008635274,0.013207209],"category_scores_gemma":[0.06695744,0.0005160105,0.00039287872,0.00043089973,0.0058140308,0.013610538,0.0026394704,0.0077356007,0.009154082],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052521797,0.00039673861,0.02171362,0.00043923547,0.00029436167,0.0004959445,0.0047025452,0.004967296,0.007789301,0.3249271,0.20187156,0.4318771],"study_design_scores_gemma":[0.00006801669,0.00008139119,0.0055579483,0.00028164723,0.0001087617,0.00097431976,0.007379257,0.016710633,0.005883287,0.860276,0.10256079,0.0001178887],"about_ca_topic_score_codex":0.0044006375,"about_ca_topic_score_gemma":0.0038514226,"teacher_disagreement_score":0.013207209,"about_ca_system_score_codex":0.0010668128,"about_ca_system_score_gemma":0.002424385,"threshold_uncertainty_score":0.04418248},"labels":[],"label_agreement":null},{"id":"W4413675714","doi":"10.64628/aam.xshjj5fs3","title":"Police on Twitter: Talking to the community, or just to themselves?","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Internet privacy; Criminology; Sociology; Media studies; Political science; Computer science","score_opus":0.08162295964097428,"score_gpt":0.3272566120693359,"score_spread":0.2456336524283616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413675714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80305386,0.0047302,0.006416127,0.10704723,0.0018278674,0.00014613646,0.002990237,0.00021156052,0.07357677],"genre_scores_gemma":[0.98579675,0.0016891051,0.0011579328,0.0038946406,0.0009250273,0.000058641763,0.00047609783,0.00006707019,0.0059347954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987191,0.00050084747,0.00004238629,0.00021091926,0.00023601078,0.0002907881],"domain_scores_gemma":[0.99473023,0.002019124,0.001320836,0.00031971518,0.0006568394,0.00095317245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012957995,0.00063955644,0.00055978727,0.0015934244,0.0018242421,0.004680705,0.0007760511,0.0027913435,0.015121294],"category_scores_gemma":[0.015706915,0.00044196396,0.00028897155,0.0012058412,0.0013533902,0.0074158562,0.0014317163,0.002973056,0.0035526494],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013108269,0.0007691036,0.5802032,0.00057204464,0.00029305488,0.0011660561,0.02014206,0.00047690235,0.0045796405,0.016051095,0.106185324,0.26825064],"study_design_scores_gemma":[0.00012909735,0.00056087546,0.64532775,0.0022894554,0.00077895325,0.005106116,0.16470931,0.019677732,0.0071819685,0.051902317,0.101951756,0.00038471466],"about_ca_topic_score_codex":0.008950796,"about_ca_topic_score_gemma":0.015103133,"teacher_disagreement_score":0.015121294,"about_ca_system_score_codex":0.00066365814,"about_ca_system_score_gemma":0.00080775033,"threshold_uncertainty_score":0.050585747},"labels":[],"label_agreement":null},{"id":"W4414151137","doi":"10.22329/il.v45i3.10214","title":"Munazara and (non-)Authoritarian Argumentation","year":2025,"lang":"en","type":"article","venue":"Informal Logic","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Argumentation theory; Operationalization; Normative; Argumentative; Toolbox; Scope (computer science)","score_opus":0.006152138736075241,"score_gpt":0.23654655328800342,"score_spread":0.2303944145519282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414151137","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07910816,0.0028043876,0.56521064,0.025468646,0.00063468685,0.00017022964,0.00006302217,0.00025915419,0.32628107],"genre_scores_gemma":[0.9460122,0.00036115025,0.04193864,0.0013314579,0.00030366963,0.00019016105,0.000026459637,0.000052122912,0.009784144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97383267,0.016828643,0.0010854924,0.0029986384,0.0042410335,0.001013654],"domain_scores_gemma":[0.9506,0.033129185,0.0047674836,0.006079332,0.004459717,0.00096426334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01873369,0.00051756663,0.00086089043,0.0027385328,0.0041785003,0.008803633,0.0018870619,0.00455353,0.0035967764],"category_scores_gemma":[0.03913929,0.00039872873,0.0010303863,0.0013418109,0.02332065,0.009752637,0.0052698855,0.004522668,0.0006999129],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007423206,0.000010932019,0.00016706747,0.0000246543,0.0000047305703,0.00002169709,0.0013241282,0.00026457786,0.00008601652,0.99385285,0.00020887717,0.0040271273],"study_design_scores_gemma":[0.000009101536,0.000011854576,0.00023718449,0.000070692564,0.000007946696,0.000055579003,0.00047001822,0.0020588166,0.00030506455,0.98643976,0.010322223,0.000011646905],"about_ca_topic_score_codex":0.00070477027,"about_ca_topic_score_gemma":0.0008210787,"teacher_disagreement_score":0.01873369,"about_ca_system_score_codex":0.0029182963,"about_ca_system_score_gemma":0.0025119989,"threshold_uncertainty_score":0.09907442},"labels":[],"label_agreement":null},{"id":"W4414321359","doi":"10.15273/jue.v15i2.12513","title":"The Gag City Grammar Police: Language and Algorithmic Community on Stan Twitter","year":2025,"lang":"en","type":"article","venue":"Journal for Undergraduate Ethnography","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Netnography; Ethnography; Speech community; Grammar; Sociolinguistics; Field (mathematics); Variation (astronomy); Subculture (biology)","score_opus":0.02507600480938458,"score_gpt":0.31941686933619845,"score_spread":0.29434086452681385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414321359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95947224,0.000066012566,0.0008353781,0.0015294685,0.000022285947,0.00002270204,0.000067680565,0.000040336516,0.03794402],"genre_scores_gemma":[0.9969523,0.000041596457,0.0002422641,0.00009321959,0.0000072991493,0.000009422704,0.000033302087,0.000033313743,0.0025872868],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9984499,0.00088622526,0.000050205494,0.00016101281,0.0002053157,0.00024729173],"domain_scores_gemma":[0.99659437,0.0017839549,0.000588411,0.0002796308,0.0003444364,0.0004092201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014569577,0.00017760266,0.00019481854,0.0018265522,0.005786133,0.005102011,0.0004236339,0.00062456937,0.0056360266],"category_scores_gemma":[0.007423063,0.00021400953,0.00009552936,0.0012400516,0.00715453,0.0038564843,0.003176544,0.0010023663,0.0007018901],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093394585,0.000057611487,0.05850031,0.000048942333,0.000007201604,0.0006896471,0.87750417,0.000120653924,0.0021227964,0.029707782,0.003910397,0.027237045],"study_design_scores_gemma":[0.0000089841715,0.000041125928,0.046575554,0.00005862932,0.000007961152,0.0001925317,0.90431476,0.00088299514,0.00082399225,0.005116704,0.041951478,0.000025269263],"about_ca_topic_score_codex":0.021209283,"about_ca_topic_score_gemma":0.042474832,"teacher_disagreement_score":0.021209283,"about_ca_system_score_codex":0.0022236893,"about_ca_system_score_gemma":0.0012705165,"threshold_uncertainty_score":0.042171657},"labels":[],"label_agreement":null},{"id":"W4414363206","doi":"10.31269/3s4fqf49","title":"Fediverse Blocklists: Moderation in Noncapitalist Social Media","year":2025,"lang":"en","type":"article","venue":"tripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Moderation; Listing (finance); Social media; Key (lock); Selection (genetic algorithm); Content analysis","score_opus":0.02518963768928012,"score_gpt":0.3848646850803606,"score_spread":0.35967504739108047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414363206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7757223,0.0004660711,0.10511154,0.006347417,0.00029304696,0.00075511914,0.00026882938,0.0012733227,0.10976228],"genre_scores_gemma":[0.96731097,0.000102887636,0.017490914,0.00042510725,0.00006780007,0.00045733136,0.0001452507,0.00024849718,0.013751249],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9863051,0.009904675,0.0004270063,0.001167711,0.0015447411,0.00065075146],"domain_scores_gemma":[0.9233518,0.05537696,0.005645447,0.00969092,0.0038115566,0.0021231824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020699035,0.00038415543,0.00028959318,0.0020642232,0.0044189133,0.0062174937,0.001335004,0.0014385215,0.007977784],"category_scores_gemma":[0.06752527,0.00042631797,0.00028601242,0.0014365879,0.0057403157,0.010332813,0.008025125,0.0016502669,0.0011822099],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003480881,0.00021704909,0.05087156,0.0004910421,0.00003853364,0.001206797,0.5773244,0.0006120712,0.01065087,0.15714788,0.0072358153,0.19385585],"study_design_scores_gemma":[0.00015303532,0.00079173915,0.037518457,0.0010868333,0.00009609127,0.0011875833,0.26210544,0.010093856,0.015562183,0.0809871,0.59022725,0.00019049694],"about_ca_topic_score_codex":0.00242046,"about_ca_topic_score_gemma":0.0042961575,"teacher_disagreement_score":0.020699035,"about_ca_system_score_codex":0.0024527397,"about_ca_system_score_gemma":0.0017958465,"threshold_uncertainty_score":0.10946828},"labels":[],"label_agreement":null},{"id":"W4414451479","doi":"10.64628/aam.cn75c5gsk","title":"Bill C-4 privacy enhancements are modest and fail to regulate politicians’ use of social bots","year":2025,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Information privacy; Confidentiality; Action (physics); Key (lock); Legislation","score_opus":0.020310782093877693,"score_gpt":0.258857437646333,"score_spread":0.23854665555245533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414451479","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08245657,0.00037883324,0.01195515,0.0850609,0.0043286937,0.00057163724,0.0017022128,0.0039145504,0.80963147],"genre_scores_gemma":[0.426324,0.00021441534,0.0076932563,0.14969997,0.0011054116,0.0006439117,0.00086892146,0.00069355423,0.4127565],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99085414,0.0013134802,0.00024153035,0.0009990378,0.00443568,0.0021560485],"domain_scores_gemma":[0.9762399,0.0117028,0.001253191,0.0034492165,0.0043885605,0.0029663255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049914196,0.000574226,0.00048192,0.0013834297,0.0051347152,0.0055295085,0.0015125263,0.01056482,0.048725616],"category_scores_gemma":[0.030158453,0.00097179826,0.0009847404,0.00058224757,0.0025889555,0.0031088945,0.0019511712,0.009059886,0.019413074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049845345,0.0004984891,0.014452227,0.00013007227,0.00007636628,0.00051761535,0.00091177743,0.0006922443,0.011442011,0.12399105,0.764036,0.082753636],"study_design_scores_gemma":[0.00021450939,0.000616399,0.051959604,0.00026290564,0.00015134476,0.0009893632,0.0014215376,0.0049980204,0.018742312,0.027029332,0.89340365,0.00021106652],"about_ca_topic_score_codex":0.012254508,"about_ca_topic_score_gemma":0.019480176,"teacher_disagreement_score":0.98774546,"about_ca_system_score_codex":0.002409103,"about_ca_system_score_gemma":0.004752621,"threshold_uncertainty_score":0.16300339},"labels":[],"label_agreement":null},{"id":"W4414471943","doi":"10.1177/29768640251377862","title":"How to lose a culture war: Social media, trans disinformation and the authoritarian right","year":2025,"lang":"en","type":"article","venue":"Dialogues on Digital Society","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Disinformation; Authoritarianism; Opposition (politics); Politics; Social media; Democracy; Sketch; State (computer science)","score_opus":0.007822411471734793,"score_gpt":0.2052748635848559,"score_spread":0.1974524521131211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414471943","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016384246,0.0113045685,0.0034015721,0.9099991,0.0097358115,0.000018186514,0.00003136635,0.000040551648,0.04908463],"genre_scores_gemma":[0.6586289,0.0127976695,0.0019725123,0.28537083,0.012931113,0.00007313782,0.00003383389,0.00016308652,0.028028868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.98930687,0.007933425,0.00021231298,0.0006993132,0.0012624948,0.00058559806],"domain_scores_gemma":[0.97589785,0.019728448,0.0010239836,0.0006349,0.0017767824,0.000938002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075226263,0.00064537086,0.00037288893,0.0015774741,0.009579859,0.012672309,0.0015113537,0.01222198,0.0030094031],"category_scores_gemma":[0.020655338,0.00025989165,0.0004217198,0.0008909934,0.02964965,0.015892746,0.003508293,0.014486353,0.0006230667],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000616837,0.000046825186,0.0017463085,0.000270468,0.00002487984,0.00079206727,0.15464166,0.00016705533,0.00049412774,0.62193596,0.17699876,0.042820223],"study_design_scores_gemma":[0.0000143861,0.00005453725,0.0014227565,0.0011962942,0.000025074423,0.0006247348,0.21751356,0.00086904457,0.0010463676,0.15081096,0.62633574,0.00008657067],"about_ca_topic_score_codex":0.006665726,"about_ca_topic_score_gemma":0.01423593,"teacher_disagreement_score":0.012672309,"about_ca_system_score_codex":0.0031890387,"about_ca_system_score_gemma":0.002788071,"threshold_uncertainty_score":0.039783955},"labels":[],"label_agreement":null},{"id":"W4414510444","doi":"10.1287/isre.2024.1140","title":"Content Moderation with Shadowbanning","year":2025,"lang":"en","type":"article","venue":"Information Systems Research","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Moderation; User-generated content; User engagement; Content (measure theory); Social media; Perception; Content creation; Welfare","score_opus":0.0834027796304613,"score_gpt":0.33143802125267185,"score_spread":0.24803524162221055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414510444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9391645,0.00037914634,0.033413228,0.0011823863,0.00013311477,0.00031960363,0.00014618544,0.000532989,0.024728877],"genre_scores_gemma":[0.99339855,0.000055443328,0.0049589197,0.00018039194,0.00003721565,0.00012768802,0.000035881327,0.00004717375,0.0011587297],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9946883,0.002884654,0.00019575823,0.00088332396,0.0008890264,0.00045888234],"domain_scores_gemma":[0.90915275,0.06838631,0.010149413,0.008385496,0.0024221526,0.0015038453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007858012,0.00087064615,0.00050460163,0.00068007846,0.0013251941,0.0024040316,0.000845776,0.0010219008,0.008778868],"category_scores_gemma":[0.055751406,0.00034759258,0.0005711591,0.00044874786,0.0026834104,0.0037029155,0.0031923132,0.0016003898,0.00084676256],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008212755,0.004239604,0.31973186,0.003707359,0.0009633304,0.0010999847,0.034245167,0.020799693,0.12558457,0.091310225,0.0073826443,0.3827228],"study_design_scores_gemma":[0.0012871649,0.01080098,0.47464034,0.0011365269,0.002249698,0.00091174693,0.016582008,0.10419702,0.1293291,0.18288788,0.07539411,0.0005834868],"about_ca_topic_score_codex":0.0009977763,"about_ca_topic_score_gemma":0.0014345762,"teacher_disagreement_score":0.008778868,"about_ca_system_score_codex":0.0010001899,"about_ca_system_score_gemma":0.0011125627,"threshold_uncertainty_score":0.04155761},"labels":[],"label_agreement":null},{"id":"W4414856475","doi":"10.1109/tnnls.2025.3611832","title":"FedMPS: Federated Learning in a Synergy of Multi-Level Prototype-Based Contrastive Learning and Soft Label Generation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Feature (linguistics); Raw data; Code (set theory); Feature extraction; Convergence (economics); Key (lock); Federated learning","score_opus":0.02173048643077047,"score_gpt":0.24939542187979474,"score_spread":0.22766493544902427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414856475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014551233,0.00019927432,0.9779146,0.00022617356,0.000052450418,0.00012609264,0.00012342253,0.0059437915,0.000862943],"genre_scores_gemma":[0.36981413,0.000108146254,0.6248047,0.0007117027,0.00006482086,0.00043517986,0.0009554893,0.00044370393,0.0026622326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762493,0.0007560564,0.00012852608,0.0007868313,0.00049684785,0.00020677435],"domain_scores_gemma":[0.99665207,0.0011790331,0.00018753065,0.001280004,0.0005172441,0.00018409299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037490486,0.00128845,0.0016721444,0.0011367336,0.0009408775,0.0018702389,0.0046738344,0.0025419584,0.0029906884],"category_scores_gemma":[0.008937034,0.000705247,0.0014342504,0.0010466679,0.0016616099,0.004768773,0.005511537,0.003189906,0.0011443436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000846503,0.0009454692,0.00332321,0.00026221861,0.00022211228,0.00031662168,0.00038024326,0.22011837,0.017260272,0.015970789,0.010376939,0.72997737],"study_design_scores_gemma":[0.00006523317,0.000116732605,0.00018237138,0.000013737387,0.000020151097,0.00007624771,0.00003340158,0.97529763,0.0055616954,0.01692436,0.0016884704,0.000019993136],"about_ca_topic_score_codex":0.0031209858,"about_ca_topic_score_gemma":0.0038830764,"teacher_disagreement_score":0.0046738344,"about_ca_system_score_codex":0.0011978511,"about_ca_system_score_gemma":0.0020971939,"threshold_uncertainty_score":0.019827068},"labels":[],"label_agreement":null},{"id":"W4415199658","doi":"10.1016/j.mlwa.2025.100749","title":"Longitudinal abuse and sentiment analysis of Hollywood movie dialogues using language models","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Hollywood; Narrative; Content (measure theory); Content analysis; Sentiment analysis; Social media; Poison control; Personality psychology; Suicide prevention","score_opus":0.012566404627104906,"score_gpt":0.2560036965289335,"score_spread":0.2434372919018286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415199658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708561,0.0010475848,0.018767407,0.00072757166,0.00016674655,0.00010285603,0.0049907225,0.00043299238,0.002908088],"genre_scores_gemma":[0.9786345,0.00034593372,0.010840008,0.00011356427,0.000100080106,0.00012990813,0.0075134886,0.000056833956,0.0022656703],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920744,0.0003994295,0.00005382757,0.00015062434,0.00011335423,0.000075352655],"domain_scores_gemma":[0.9982876,0.001068063,0.0002194389,0.00009290475,0.00025823023,0.00007367019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012812811,0.0006383373,0.0003577247,0.0011199483,0.00035583752,0.0008844618,0.00033162846,0.00040819915,0.0013065602],"category_scores_gemma":[0.0032713711,0.0001800584,0.0010263128,0.00065796525,0.00026503188,0.0008689256,0.00056752877,0.00089236256,0.0008589182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019503031,0.0012867552,0.44968793,0.0011292322,0.0011913509,0.002442022,0.0086664865,0.09245328,0.06444101,0.0040758983,0.033620704,0.33905503],"study_design_scores_gemma":[0.00004553942,0.00030072674,0.26787674,0.000098231365,0.00016828155,0.00037107326,0.0030474267,0.70338446,0.0066356533,0.0021023343,0.015869766,0.00009986244],"about_ca_topic_score_codex":0.008808486,"about_ca_topic_score_gemma":0.011672782,"teacher_disagreement_score":0.008808486,"about_ca_system_score_codex":0.00055552996,"about_ca_system_score_gemma":0.00029091787,"threshold_uncertainty_score":0.017514467},"labels":[],"label_agreement":null},{"id":"W4415225763","doi":"10.1093/pnasnexus/pgaf327","title":"Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis","year":2025,"lang":"en","type":"article","venue":"PNAS Nexus","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Disinformation; Replication (statistics); Morality; Emotional contagion; Population; Phenomenon","score_opus":0.028558726422665166,"score_gpt":0.29980318871383776,"score_spread":0.2712444622911726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415225763","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22530775,0.5869561,0.14368641,0.0051403013,0.00507195,0.008976415,0.016179482,0.002051986,0.0066296402],"genre_scores_gemma":[0.9460407,0.010955053,0.030517878,0.0014674994,0.000393533,0.0071241916,0.0022649327,0.00031098264,0.00092535885],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.8799768,0.0913535,0.010824481,0.011913592,0.0048731375,0.0010585006],"domain_scores_gemma":[0.70257163,0.25016192,0.012633275,0.026116535,0.007375209,0.0011414159],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10327832,0.0041324706,0.010429622,0.0045664716,0.0013370578,0.0041596913,0.005111277,0.0044764606,0.007045081],"category_scores_gemma":[0.28606737,0.002476756,0.049187187,0.004872323,0.0029191968,0.0037606463,0.0029722801,0.0061435746,0.0011441829],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054436657,0.00016239667,0.033356912,0.048511043,0.8924305,0.00046127517,0.00038669107,0.0026679013,0.001118509,0.001056759,0.0016537114,0.012750642],"study_design_scores_gemma":[0.0025609385,0.0009655055,0.011157518,0.0036150047,0.9733646,0.00024227629,0.00015550714,0.0023279183,0.0008135498,0.002344728,0.002382455,0.00006999424],"about_ca_topic_score_codex":0.0050407844,"about_ca_topic_score_gemma":0.006035541,"teacher_disagreement_score":0.89672166,"about_ca_system_score_codex":0.00192641,"about_ca_system_score_gemma":0.0022448525,"threshold_uncertainty_score":0.54619443},"labels":[],"label_agreement":null},{"id":"W4415255545","doi":"10.1109/eurousec69254.2025.00015","title":"\"Lying makes me look suspicious\": Users’ Perspectives and Analysis of Security Questions","year":2025,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Security through obscurity; Security information and event management; Set (abstract data type); Cloud computing security; Critical security studies; Human-computer interaction in information security; Perception; Security service; Key (lock)","score_opus":0.006350457121286807,"score_gpt":0.25039637656444413,"score_spread":0.24404591944315732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415255545","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.982464,0.0005281434,0.004203844,0.004530098,0.000054061464,0.000054238957,0.000056297216,0.000053392,0.008055803],"genre_scores_gemma":[0.9972234,0.00022104349,0.00090494216,0.0007995086,0.000021440916,0.00003386823,0.000027687292,0.00002380463,0.00074446143],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.96926427,0.026157614,0.0008262389,0.00088608597,0.0017811869,0.0010846184],"domain_scores_gemma":[0.9056799,0.079182886,0.005011742,0.0024304138,0.005533501,0.002161522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026855912,0.00079087145,0.00055889797,0.0028758394,0.006872937,0.009136013,0.0010803841,0.0036950556,0.0021274367],"category_scores_gemma":[0.06992055,0.0006488826,0.00061199005,0.0013715448,0.00912854,0.011223877,0.006617841,0.004394729,0.00050527207],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042582877,0.000020035275,0.0095930165,0.000045400102,0.0000062888744,0.00021082797,0.98338735,0.000034389162,0.0005802363,0.0020386188,0.00050442555,0.003536873],"study_design_scores_gemma":[0.000008437726,0.000070595146,0.0061478307,0.00012428888,0.000013101538,0.0003995856,0.9784889,0.00082859094,0.0004985742,0.0018424426,0.011526018,0.000051688872],"about_ca_topic_score_codex":0.0063870405,"about_ca_topic_score_gemma":0.0049491366,"teacher_disagreement_score":0.026855912,"about_ca_system_score_codex":0.0027993943,"about_ca_system_score_gemma":0.0017714942,"threshold_uncertainty_score":0.14202935},"labels":[],"label_agreement":null},{"id":"W4415444011","doi":"10.1109/access.2025.3624312","title":"Mitigating Authorship Attribution in Social Media Texts: A Privacy by Translation Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Authorship attribution; Social media; Adversarial system; Attribution; Identification (biology); Process (computing); Differential privacy","score_opus":0.04959407869852116,"score_gpt":0.31471745203608514,"score_spread":0.265123373337564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415444011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10371954,0.0009081397,0.88602686,0.0026738713,0.00021048219,0.00026942889,0.00026146215,0.001067931,0.00486231],"genre_scores_gemma":[0.876344,0.0004832134,0.1170016,0.00050121377,0.0004139751,0.00017469696,0.0003483218,0.00012509355,0.004607777],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9875656,0.0069756624,0.0007026405,0.001535109,0.0027610848,0.00046002967],"domain_scores_gemma":[0.95792514,0.023008157,0.004201123,0.011330102,0.0029553494,0.0005801607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009539028,0.0010705354,0.0011940309,0.0020953834,0.0015982,0.0037883052,0.0015013738,0.0021971036,0.0017213237],"category_scores_gemma":[0.049341682,0.00044672674,0.0011952178,0.0022418236,0.0033271792,0.0060594715,0.003954658,0.002538043,0.0010652149],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078331464,0.00057891174,0.027114581,0.00041825412,0.00028309034,0.0009336709,0.0022152278,0.08557996,0.021419238,0.061393224,0.0051567247,0.7941238],"study_design_scores_gemma":[0.000068744455,0.00032908496,0.005407005,0.00008921843,0.00013858502,0.0017200282,0.0007790696,0.8235109,0.0347677,0.12603112,0.0070747207,0.00008383535],"about_ca_topic_score_codex":0.0006090443,"about_ca_topic_score_gemma":0.0006717559,"teacher_disagreement_score":0.009539028,"about_ca_system_score_codex":0.0011992235,"about_ca_system_score_gemma":0.0015805922,"threshold_uncertainty_score":0.050447762},"labels":[],"label_agreement":null},{"id":"W4415500927","doi":"10.1108/ijssp-03-2025-0179","title":"Revised online hate speech law in Japan and early evidence of its effectiveness","year":2025,"lang":"en","type":"article","venue":"International Journal of Sociology and Social Policy","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Legislation; Work (physics); Empirical evidence; Survey data collection; Empirical research; Computer-assisted web interviewing; Online research methods; Observational study","score_opus":0.021084837616452728,"score_gpt":0.3460654606281039,"score_spread":0.32498062301165115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415500927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.985438,0.0010465394,0.00065903843,0.0011692629,0.00009108805,0.000047687845,0.00018215603,0.000020968359,0.011345203],"genre_scores_gemma":[0.997593,0.0006230414,0.00024442316,0.00026502274,0.000045442765,0.00001727394,0.00009695955,0.000008889947,0.0011058718],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967698,0.0010684087,0.00034903333,0.00047917612,0.00091177784,0.00042197527],"domain_scores_gemma":[0.9646537,0.0103475535,0.014278044,0.0024180384,0.006063633,0.0022390077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004497293,0.00024747272,0.00030721806,0.0014501192,0.0017273863,0.0022810183,0.0005837793,0.0006976138,0.003926052],"category_scores_gemma":[0.023163088,0.00030968824,0.00030832159,0.0013317855,0.0024672647,0.0020201432,0.0015006871,0.0012565372,0.00047782605],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029963863,0.0006055881,0.8750556,0.0008343664,0.00013083553,0.0008449765,0.028756928,0.00019810494,0.0024782268,0.0034844368,0.003246914,0.08406427],"study_design_scores_gemma":[0.000010148432,0.00022991336,0.97159374,0.00025448334,0.00009015746,0.00022794843,0.016327588,0.00056651555,0.0011455001,0.0003293586,0.009185962,0.000038830563],"about_ca_topic_score_codex":0.029628953,"about_ca_topic_score_gemma":0.043118346,"teacher_disagreement_score":0.029628953,"about_ca_system_score_codex":0.0022642403,"about_ca_system_score_gemma":0.002124755,"threshold_uncertainty_score":0.058912992},"labels":[],"label_agreement":null},{"id":"W4415622504","doi":"10.15353/joci.v21i1.6644","title":"Pin the tail on the researcher","year":2025,"lang":"","type":"article","venue":"The Journal of Community Informatics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accountability; Foregrounding; Social media; Sociotechnical system; Social engineering (security); Democracy","score_opus":0.05616053219459451,"score_gpt":0.30848288197266693,"score_spread":0.25232234977807244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415622504","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027265896,0.013958391,0.011043268,0.53282267,0.13285775,0.00033321662,0.00048817435,0.0010504724,0.30471945],"genre_scores_gemma":[0.08667384,0.006471211,0.004254064,0.30745506,0.02084788,0.00069801015,0.00029772165,0.0013942828,0.5719079],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9869039,0.007298086,0.00063177594,0.0019761533,0.0021420696,0.0010480433],"domain_scores_gemma":[0.97486025,0.008561132,0.0014793205,0.0039347475,0.007548899,0.003615665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009223717,0.0007764739,0.0010266145,0.0017571603,0.0070854304,0.0124379685,0.0019159593,0.006117896,0.103846475],"category_scores_gemma":[0.06600358,0.0005840473,0.0006755173,0.0015032615,0.013282958,0.014265228,0.011136051,0.01314689,0.07208449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057433717,0.000020407208,0.00036771476,0.00026410917,0.000013777165,0.0004088091,0.019833121,0.00003911491,0.00037438807,0.12541367,0.8152825,0.037925024],"study_design_scores_gemma":[0.0000049752757,0.000009441378,0.000117293785,0.0004022563,0.000003843789,0.0001346922,0.008849567,0.00002717319,0.000070280126,0.009988636,0.9803827,0.000009102686],"about_ca_topic_score_codex":0.0043308237,"about_ca_topic_score_gemma":0.0060630054,"teacher_disagreement_score":0.103846475,"about_ca_system_score_codex":0.004029702,"about_ca_system_score_gemma":0.007223511,"threshold_uncertainty_score":0.34740108},"labels":[],"label_agreement":null},{"id":"W4415650151","doi":"10.26522/ssj.v19i3.5137","title":"Hate Speech on Trial","year":2025,"lang":"en","type":"article","venue":"Studies in Social Justice","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"University of Waterloo","keywords":"Exploit; Free speech; Omnipresence; Censorship; Diversity (politics); Popularity; Social media; Indirect speech","score_opus":0.04970482628598438,"score_gpt":0.37062224330717325,"score_spread":0.32091741702118887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415650151","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.333383,0.002283046,0.032052916,0.024606543,0.0027793902,0.0003111251,0.00026666743,0.0004899711,0.6038274],"genre_scores_gemma":[0.9126759,0.0006431003,0.0023344744,0.007554754,0.0004643688,0.00013097437,0.00006654494,0.00014799503,0.07598189],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9910987,0.0045522777,0.0003356378,0.0010015193,0.0023216459,0.00069020357],"domain_scores_gemma":[0.9674943,0.018948866,0.0036106755,0.005337902,0.0030142698,0.0015939671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007521212,0.0003425198,0.00044235215,0.0006997522,0.004615886,0.0065137637,0.0007423267,0.0024030178,0.021242682],"category_scores_gemma":[0.05164124,0.0002359965,0.00033255163,0.00037109648,0.006809321,0.0046028253,0.0038627326,0.0038117138,0.005121843],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005729839,0.00035086798,0.027336748,0.0006487485,0.00012469917,0.004367539,0.17024857,0.00047464992,0.009371992,0.42975253,0.08369856,0.27305204],"study_design_scores_gemma":[0.000064199296,0.00051076064,0.018513046,0.0013998386,0.00011802024,0.0060136095,0.07999667,0.002712134,0.012142417,0.11241938,0.76592743,0.00018252662],"about_ca_topic_score_codex":0.0013339447,"about_ca_topic_score_gemma":0.0017108253,"teacher_disagreement_score":0.021242682,"about_ca_system_score_codex":0.0012116353,"about_ca_system_score_gemma":0.0012262254,"threshold_uncertainty_score":0.07106382},"labels":[],"label_agreement":null},{"id":"W4416125942","doi":"10.1108/dprg-07-2025-0211","title":"The impact of legislation on online toxic content","year":2025,"lang":"en","type":"article","venue":"Digital Policy Regulation and Governance","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Legislation; Context (archaeology); Deterrence theory; Unintended consequences; Content analysis; Moderation; Digital content; Work (physics)","score_opus":0.016929181750196143,"score_gpt":0.2778688466241476,"score_spread":0.2609396648739515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416125942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.967276,0.00029291125,0.0027252024,0.0014363568,0.00009046474,0.00035225527,0.0010481456,0.00012938988,0.026649226],"genre_scores_gemma":[0.99545765,0.00014875308,0.0011041736,0.00049388927,0.000054960645,0.00019242865,0.0003277676,0.000029942832,0.0021904993],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99267685,0.0026223287,0.00049998984,0.0010368901,0.002561054,0.0006028815],"domain_scores_gemma":[0.948932,0.02175345,0.016988093,0.004901309,0.0057194056,0.0017057341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049156183,0.00030404085,0.00035315804,0.0011410237,0.00155472,0.0034159233,0.0005744617,0.0008368053,0.0068764566],"category_scores_gemma":[0.03144524,0.00022306644,0.00046354113,0.0011066366,0.0027395554,0.0030450933,0.0026831885,0.0013688665,0.0009730286],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009464751,0.0012365731,0.7926566,0.0017893001,0.00022995213,0.0012317251,0.020579018,0.0023435773,0.021824054,0.0117164515,0.0058272146,0.13961901],"study_design_scores_gemma":[0.00003387796,0.0014069014,0.93327814,0.0003486055,0.00015751868,0.00024266791,0.0156220365,0.0028608108,0.010513661,0.0026793757,0.03276517,0.00009125432],"about_ca_topic_score_codex":0.007052677,"about_ca_topic_score_gemma":0.010478812,"teacher_disagreement_score":0.007052677,"about_ca_system_score_codex":0.002326382,"about_ca_system_score_gemma":0.002302026,"threshold_uncertainty_score":0.025996625},"labels":[],"label_agreement":null},{"id":"W4416405636","doi":"10.36151/rcdi.2025.806.06","title":"Derecho al honor versus libertad de expresión en redes sociales. el comportamiento beligerante y agresivo de las comunicaciones en redes sociales","year":2025,"lang":"","type":"article","venue":"Revista crítica de derecho inmobiliario/Revista crítica de derecho inmobiliario","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Honor","score_opus":0.03039314514752996,"score_gpt":0.3237498690221543,"score_spread":0.29335672387462436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416405636","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18734978,0.007902936,0.0238944,0.03243988,0.00094680476,0.00008349289,0.00009547007,0.00009896803,0.74718827],"genre_scores_gemma":[0.97200274,0.0012384148,0.001075701,0.0011698203,0.00020826604,0.000041391144,0.000020853584,0.000031481726,0.02421128],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9878973,0.007229406,0.00039473586,0.0011483246,0.0020455567,0.0012846461],"domain_scores_gemma":[0.99294394,0.002859577,0.001385878,0.0008811578,0.0011145727,0.00081492576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053973976,0.0005364364,0.0003723412,0.0018719353,0.0059583513,0.011815625,0.0012011073,0.002549457,0.007898409],"category_scores_gemma":[0.010797441,0.0002859339,0.000430514,0.0012751743,0.054037474,0.009273917,0.008337619,0.003847687,0.0010478396],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026913385,0.000013673931,0.001854025,0.0000818746,0.0000093932595,0.00018092977,0.09496042,0.00006683283,0.00035985783,0.8907796,0.0012073269,0.010459081],"study_design_scores_gemma":[0.00004047962,0.00012628867,0.0113155395,0.00083592196,0.00006394763,0.00092637516,0.22924985,0.00055092253,0.0016623145,0.4054496,0.34967726,0.00010150318],"about_ca_topic_score_codex":0.00794656,"about_ca_topic_score_gemma":0.007072335,"teacher_disagreement_score":0.011815625,"about_ca_system_score_codex":0.005733244,"about_ca_system_score_gemma":0.003712819,"threshold_uncertainty_score":0.041597724},"labels":[],"label_agreement":null},{"id":"W4416504162","doi":"10.1038/s41598-025-25879-4","title":"Tackling toxicity in Arabic social media through advanced detection techniques","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Minia University","keywords":"Arabic; Social media; Embedding; Interpersonal communication; Encoder; Transformer","score_opus":0.00994106582999158,"score_gpt":0.2583990237387625,"score_spread":0.24845795790877093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416504162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7224727,0.007590564,0.23126699,0.002435159,0.00087602745,0.000858043,0.009547527,0.007429189,0.017523685],"genre_scores_gemma":[0.8526012,0.0025982538,0.12124892,0.00045221267,0.00035036623,0.00042153653,0.011868233,0.00015696094,0.010302296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989441,0.00029219087,0.0001105785,0.00022272953,0.00033635934,0.00009396433],"domain_scores_gemma":[0.9980102,0.0006322734,0.00038173882,0.0002543351,0.0006439573,0.000077474804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080477964,0.0016619033,0.0006375794,0.003900923,0.0007832305,0.0011447673,0.0008352638,0.000952194,0.0012617548],"category_scores_gemma":[0.0038172745,0.00019395184,0.00070379034,0.0014226658,0.000462388,0.0020172219,0.0014330967,0.0009847784,0.0015005184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039229417,0.0005542429,0.051398676,0.00084197323,0.00017103212,0.0016679881,0.0010477377,0.019736819,0.035180334,0.002439034,0.020959288,0.8656105],"study_design_scores_gemma":[0.00004327525,0.00057570235,0.058625154,0.00030083876,0.00021410846,0.002973133,0.0033736145,0.7930874,0.07283101,0.010663132,0.05715061,0.00016208028],"about_ca_topic_score_codex":0.004244744,"about_ca_topic_score_gemma":0.006780138,"teacher_disagreement_score":0.004244744,"about_ca_system_score_codex":0.0005848461,"about_ca_system_score_gemma":0.0005406502,"threshold_uncertainty_score":0.008440077},"labels":[],"label_agreement":null},{"id":"W4416513286","doi":"10.1109/tvcg.2025.3634629","title":"Conch: Competitive Debate Analysis via Visualizing Clash Points and Hierarchical Strategies","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"Fundamental Research Funds for the Central Universities; Huazhong University of Science and Technology","keywords":"Visualization; Leverage (statistics); Argumentative; Data visualization; Sensemaking; Usability; Context (archaeology); Semantics (computer science); Argumentation theory","score_opus":0.010636038412855809,"score_gpt":0.2722574091315758,"score_spread":0.26162137071872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416513286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11614273,0.00088746456,0.75904286,0.0025696862,0.0003448283,0.0013548373,0.011437345,0.08333282,0.024887294],"genre_scores_gemma":[0.37544376,0.00050656084,0.6050244,0.0003558045,0.00013094835,0.0013712457,0.005959641,0.0041669393,0.0070407554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991861,0.00033682765,0.000046432524,0.00013246977,0.00022882195,0.00006936052],"domain_scores_gemma":[0.9949538,0.0030540165,0.00036429067,0.00066956034,0.0006883451,0.00026991943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026317379,0.0012821772,0.00052119297,0.004245468,0.00092622556,0.0032703762,0.0013704703,0.0014217517,0.0137935495],"category_scores_gemma":[0.010267836,0.00044362742,0.0006262345,0.0017427108,0.000829758,0.0027499017,0.0036621755,0.0013112499,0.0019320053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013853871,0.0005852827,0.018685639,0.0029954459,0.0002166646,0.0014294875,0.036624745,0.016746473,0.054098442,0.062413648,0.17120636,0.63361245],"study_design_scores_gemma":[0.0005038006,0.00040653793,0.022184966,0.0007846289,0.00015130242,0.0010502029,0.012354811,0.51338243,0.060924776,0.113905884,0.2739197,0.00043092636],"about_ca_topic_score_codex":0.002506314,"about_ca_topic_score_gemma":0.0044062375,"teacher_disagreement_score":0.0137935495,"about_ca_system_score_codex":0.0006544439,"about_ca_system_score_gemma":0.0013998642,"threshold_uncertainty_score":0.04614407},"labels":[],"label_agreement":null},{"id":"W4416524129","doi":"10.48550/arxiv.2506.23930","title":"Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Metaphor; Bengali; Convolutional neural network; Face (sociological concept); Social media; Word (group theory); Deep learning; Voice activity detection","score_opus":0.01275843996889185,"score_gpt":0.23455247151172962,"score_spread":0.22179403154283778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416524129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5685238,0.001262289,0.4085216,0.0010537765,0.00039202702,0.00019536966,0.00072879513,0.013731973,0.005590334],"genre_scores_gemma":[0.94508886,0.0002097915,0.05119754,0.00025405647,0.00006683887,0.00007336383,0.0006770208,0.00019086109,0.0022416802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993963,0.00025216464,0.00003058512,0.0001556074,0.00009053037,0.00007474979],"domain_scores_gemma":[0.99803156,0.00094008545,0.00017167297,0.00028046433,0.00042654344,0.0001496242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008530957,0.000973623,0.00054183,0.00066386815,0.0003690923,0.0008290606,0.00050707266,0.00059853756,0.0014894671],"category_scores_gemma":[0.0062204436,0.00020508353,0.00045615653,0.00025443643,0.00049102516,0.0020885286,0.0013775456,0.0013388749,0.001198508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014884496,0.00062342576,0.028339779,0.00074714696,0.00009760263,0.00079615874,0.0021129984,0.044142462,0.15996625,0.005022984,0.009451887,0.74721086],"study_design_scores_gemma":[0.00008528023,0.0012025278,0.013590223,0.00009018149,0.0000992763,0.00061600306,0.0014086781,0.863484,0.09690258,0.01154136,0.01084119,0.00013879706],"about_ca_topic_score_codex":0.0016393095,"about_ca_topic_score_gemma":0.0029991157,"teacher_disagreement_score":0.0016393095,"about_ca_system_score_codex":0.00034945807,"about_ca_system_score_gemma":0.00075991056,"threshold_uncertainty_score":0.0049827695},"labels":[],"label_agreement":null},{"id":"W4416598778","doi":"10.1177/00111287251384670","title":"Approaches to Defining the “Hate Element” of a Behavior: A Data-Driven Typology","year":2025,"lang":"en","type":"article","venue":"Crime & Delinquency","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Public Safety Canada","keywords":"Typology; Diversity (politics); Prejudice (legal term); Element (criminal law); Linguistic typology; Poison control","score_opus":0.1162184594939914,"score_gpt":0.31121300935961543,"score_spread":0.19499454986562403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416598778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57534206,0.0038709235,0.35614696,0.00975382,0.00033515086,0.0017370997,0.00470197,0.0001964862,0.0479155],"genre_scores_gemma":[0.8459973,0.0011809263,0.14563754,0.00062788546,0.00007218607,0.0019862228,0.003417396,0.00007678958,0.0010036548],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9769562,0.014341075,0.0025046053,0.0023793343,0.0032855736,0.0005332966],"domain_scores_gemma":[0.922506,0.04880328,0.009018712,0.008635368,0.009833031,0.0012034934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023540845,0.0008692991,0.0008222367,0.022563096,0.0031545428,0.0072262785,0.002400198,0.001319325,0.0017371237],"category_scores_gemma":[0.06405291,0.00046440758,0.0011232932,0.019140255,0.010549737,0.011070302,0.0065234513,0.0029629103,0.0003631053],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017191585,0.00024668482,0.432449,0.0013931461,0.00024670834,0.00043914866,0.09619374,0.005364077,0.0018059678,0.3070405,0.004145253,0.15050381],"study_design_scores_gemma":[0.000061727405,0.00019757573,0.17149489,0.003365959,0.00013802313,0.0009165594,0.22033747,0.041906092,0.0026946322,0.4815279,0.077083915,0.0002753068],"about_ca_topic_score_codex":0.0093180025,"about_ca_topic_score_gemma":0.009832357,"teacher_disagreement_score":0.023540845,"about_ca_system_score_codex":0.0075367223,"about_ca_system_score_gemma":0.004298318,"threshold_uncertainty_score":0.124497354},"labels":[],"label_agreement":null},{"id":"W4416773353","doi":"10.1007/978-3-032-11733-5_16","title":"Identification of Social Media Users that Perpetuate Xenophobic Attitudes and Hate Speech Narratives in South Africa","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Narrative; Social media; Politics; Identification (biology); Hatred; Focus (optics); Racism","score_opus":0.03433457820265787,"score_gpt":0.2740530658754147,"score_spread":0.23971848767275686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416773353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99183637,0.00043820476,0.00007557079,0.0006863768,0.000012651652,0.0000115970715,0.000028122586,0.0000013665034,0.0069097686],"genre_scores_gemma":[0.99608374,0.00077129545,0.00010521622,0.000119727505,0.000005737928,0.000015271502,0.000019341955,0.0000040357236,0.0028756696],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99964607,0.00014496653,0.00001455727,0.000030788426,0.00004102589,0.00012268187],"domain_scores_gemma":[0.99906117,0.00045276788,0.00026602275,0.000026194477,0.0000665645,0.00012727287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079329696,0.00018541748,0.00014202986,0.0010284676,0.0037182043,0.0021613766,0.00033682148,0.00051957,0.0035581312],"category_scores_gemma":[0.0021296234,0.00024640898,0.00008760001,0.001320103,0.0019518592,0.0019237256,0.0017000806,0.0008690337,0.0003085992],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003839625,0.00004674506,0.056278013,0.0001017331,0.0000043180585,0.0011690195,0.9157828,0.000012405701,0.0020619272,0.00427463,0.0007576561,0.019472336],"study_design_scores_gemma":[0.0000019591373,0.000028520957,0.112161666,0.00021245379,0.000006042671,0.00053124217,0.8762417,0.00003867241,0.00041077836,0.0006449208,0.009714032,0.000007990936],"about_ca_topic_score_codex":0.02204656,"about_ca_topic_score_gemma":0.05870665,"teacher_disagreement_score":0.02204656,"about_ca_system_score_codex":0.0012630352,"about_ca_system_score_gemma":0.0016933454,"threshold_uncertainty_score":0.043836474},"labels":[],"label_agreement":null},{"id":"W4416799470","doi":"10.1109/snpd65828.2025.11252553","title":"Towards Safer Online Platforms: Explainable and Adversarial-Resistant Toxic Comment Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Adversarial system; SAFER; Filter (signal processing); Vulnerability (computing); Adversarial machine learning; Symbol (formal); Interface (matter)","score_opus":0.012116470220873856,"score_gpt":0.24277111817967922,"score_spread":0.23065464795880536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416799470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13796683,0.0007969297,0.82529604,0.0033668876,0.000286759,0.00048627448,0.0013631728,0.024314685,0.0061223838],"genre_scores_gemma":[0.8075786,0.00020965977,0.18381882,0.0006947392,0.00017682859,0.00017061905,0.0016432351,0.00044813665,0.0052593006],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971559,0.0012156673,0.00010870242,0.0005527561,0.0007709998,0.00019597866],"domain_scores_gemma":[0.9898331,0.0055175447,0.001270644,0.0018227304,0.001222287,0.00033372903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003513755,0.0014178313,0.00071942154,0.0013718943,0.0007167653,0.0016045781,0.0021700799,0.0021926684,0.0025689884],"category_scores_gemma":[0.01802149,0.0003701499,0.0007035094,0.00038909313,0.0011789083,0.0025945185,0.0025277464,0.0030182924,0.001942036],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010656256,0.00077605946,0.039431863,0.00065074756,0.00020528142,0.0014507372,0.0021731008,0.2131887,0.031724606,0.021531757,0.04524621,0.6425553],"study_design_scores_gemma":[0.000017439235,0.000063387284,0.001431149,0.000037153462,0.000014814835,0.00014838159,0.00013055526,0.9771548,0.0077661513,0.010030555,0.003181291,0.00002433959],"about_ca_topic_score_codex":0.0022750318,"about_ca_topic_score_gemma":0.0032737243,"teacher_disagreement_score":0.003513755,"about_ca_system_score_codex":0.00091509573,"about_ca_system_score_gemma":0.0011530629,"threshold_uncertainty_score":0.018582702},"labels":[],"label_agreement":null},{"id":"W4417338869","doi":"10.1109/ictai66417.2025.00123","title":"Detecting Machine-Generated Text using Grammatical Features","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Punctuation; Natural language; Robustness (evolution); Classifier (UML); Adversarial system; Language model; Artificial neural network","score_opus":0.016277619948173422,"score_gpt":0.2686858042519755,"score_spread":0.2524081843038021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417338869","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62565035,0.0011001052,0.34680697,0.0015502258,0.00046799012,0.0002724918,0.005686002,0.00934764,0.009118171],"genre_scores_gemma":[0.92594886,0.00029793143,0.066122085,0.00018676513,0.00013884582,0.00007756457,0.00407158,0.00025212127,0.0029041157],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992612,0.00024529212,0.00004684827,0.00021950736,0.00018035118,0.000046872177],"domain_scores_gemma":[0.9942279,0.003196336,0.0009760745,0.00070590136,0.0007798645,0.00011396159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007588273,0.00078615604,0.000353016,0.0019924073,0.00030543888,0.0009259251,0.0004885384,0.00074794283,0.0014523],"category_scores_gemma":[0.0072040255,0.00022302347,0.00036370708,0.00070578314,0.0004978001,0.0023175406,0.00064683275,0.0008227551,0.0017380518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041148832,0.00042434636,0.072381504,0.00073996134,0.00015591682,0.0014884598,0.0010725233,0.050594226,0.077943474,0.013066906,0.024724763,0.7569964],"study_design_scores_gemma":[0.000027513803,0.00021956627,0.01902148,0.00008307768,0.00005130074,0.0008412703,0.0003803396,0.8951832,0.05574556,0.015310352,0.013085613,0.00005064585],"about_ca_topic_score_codex":0.0009330226,"about_ca_topic_score_gemma":0.0014700056,"teacher_disagreement_score":0.0019924073,"about_ca_system_score_codex":0.0005607669,"about_ca_system_score_gemma":0.00042436202,"threshold_uncertainty_score":0.004858434},"labels":[],"label_agreement":null},{"id":"W68298479","doi":"10.1609/icwsm.v7i1.14434","title":"Classifying Political Orientation on Twitter: It’s Not Easy!","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Inference; Biology and political orientation; Politics; Orientation (vector space); Computer science; Transferability; Data science; Artificial intelligence; Annotation; Machine learning; Political science; Mathematics; Law","score_opus":0.05188406507447452,"score_gpt":0.2800748337800538,"score_spread":0.2281907687055793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68298479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7404068,0.0037375987,0.11050986,0.07038804,0.0026123743,0.00047093444,0.02381647,0.0021869491,0.04587098],"genre_scores_gemma":[0.9134202,0.0016936598,0.061922275,0.0041306457,0.0017489278,0.00029367636,0.011329135,0.0002954912,0.0051659695],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968971,0.0014736244,0.00021811848,0.0004166546,0.0007759019,0.00021862412],"domain_scores_gemma":[0.987773,0.0059269057,0.0013675177,0.0022602412,0.0021346405,0.0005376472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044550938,0.00058985216,0.0005134754,0.0022126588,0.0011581624,0.0031642895,0.0007781438,0.0012023315,0.0028206273],"category_scores_gemma":[0.02325722,0.0002614487,0.00046095136,0.0026581376,0.00084305555,0.0063508567,0.001610493,0.0022005548,0.0038246363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028640573,0.00018110612,0.55909973,0.00037381687,0.00024371238,0.00014559088,0.0032197873,0.0024659543,0.0041245082,0.008703227,0.06839121,0.35276505],"study_design_scores_gemma":[0.00005387406,0.00033234322,0.5560018,0.0012139938,0.00027578033,0.0007660719,0.025829019,0.091430314,0.014467038,0.08372006,0.22559959,0.00031014121],"about_ca_topic_score_codex":0.0039381147,"about_ca_topic_score_gemma":0.008087569,"teacher_disagreement_score":0.0044550938,"about_ca_system_score_codex":0.000514202,"about_ca_system_score_gemma":0.00043987628,"threshold_uncertainty_score":0.02356106},"labels":[],"label_agreement":null},{"id":"W6887708422","doi":"10.17605/osf.io/zuc6p","title":"About the United States of America (U.S.A) “Cancel Culture” and Racism on Twitter - #Michael Ayele (a.k.a) W October 28th 2022 - #Association for the Advancement of Civil Liberties (AACL)","year":2023,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Racism; Civil liberties; Government (linguistics); Misinformation; Subject (documents); German; Residence; Censorship","score_opus":0.0136477368985734,"score_gpt":0.25208079529024924,"score_spread":0.23843305839167583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887708422","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004449243,0.003907098,0.00037671268,0.046158597,0.014280153,0.00019913897,0.028778326,0.0014882517,0.9003625],"genre_scores_gemma":[0.018745266,0.008269119,0.0005773215,0.016835514,0.0038273172,0.0002694219,0.011865191,0.00075242075,0.93885845],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9994844,0.00010083334,0.000042095406,0.0000695678,0.00022459375,0.000078467325],"domain_scores_gemma":[0.99757296,0.00034852847,0.0002714033,0.00019411014,0.0007259701,0.00088701],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00070698984,0.00056470296,0.0003927853,0.0011928458,0.0032248683,0.0040897904,0.00032996928,0.001794774,0.39397493],"category_scores_gemma":[0.003627823,0.0002722657,0.00024889485,0.0019746094,0.00073126605,0.00405553,0.002300479,0.0019758944,0.3105237],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008823347,0.000007345164,0.00045547893,0.00004292238,0.0000010950364,0.000019971576,0.00018618423,0.000004040832,0.00010113302,0.00042533156,0.9862076,0.012540038],"study_design_scores_gemma":[0.0000024089459,0.000007415626,0.0017352534,0.00009683812,0.000001930937,0.00002288755,0.0005473481,0.000014787885,0.000076224926,0.00009383823,0.9973942,0.000006875943],"about_ca_topic_score_codex":0.009894336,"about_ca_topic_score_gemma":0.017207308,"teacher_disagreement_score":0.39397493,"about_ca_system_score_codex":0.0008528917,"about_ca_system_score_gemma":0.0013873152,"threshold_uncertainty_score":0.8644212},"labels":[],"label_agreement":null},{"id":"W6887819335","doi":"10.17605/osf.io/8mgph","title":"About the Immigration Status of David DePape After the Attempted Murder on Paul Pelosi Dated October 28th 2022 - #How “Cancel Culture” in the United State of America (U.S.A) is Really Enabling Racism on Twitter &amp; Elsewhere - #Michael Ayele (a.k.a) W","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Immigration; State (computer science); Residence; Commission; Law enforcement; Alien; Enforcement; Civil liberties; Deportation","score_opus":0.034505729964616594,"score_gpt":0.2751299501481784,"score_spread":0.24062422018356183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887819335","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2415247,0.005648709,0.00042920298,0.30793503,0.013310925,0.00015156166,0.0012798886,0.00016867275,0.4295513],"genre_scores_gemma":[0.48787174,0.0057185907,0.00038752766,0.09351742,0.003615279,0.0001352774,0.0009034311,0.00009076683,0.40776],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997297,0.000052868872,0.000014266014,0.00004036715,0.00006538964,0.00009732006],"domain_scores_gemma":[0.9991824,0.00016958061,0.00012413185,0.000036326914,0.00019766213,0.00029001132],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004385877,0.00024400283,0.00016954278,0.00038411078,0.00820981,0.0014846001,0.0004285002,0.0020588464,0.019049412],"category_scores_gemma":[0.0035923054,0.00016178873,0.00011373249,0.00024049998,0.0012164267,0.0013683322,0.0017028312,0.004293796,0.004232755],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008180515,0.00008029301,0.023599727,0.0001310198,0.0000140038765,0.008216798,0.055115364,0.00007097066,0.0012842766,0.0076168813,0.8688919,0.034896966],"study_design_scores_gemma":[0.000010066435,0.00006893516,0.041601807,0.00041763627,0.00001453803,0.0019634804,0.09506433,0.00014732852,0.0009873258,0.0005867647,0.85908616,0.000051634383],"about_ca_topic_score_codex":0.061773885,"about_ca_topic_score_gemma":0.16639894,"teacher_disagreement_score":0.9809506,"about_ca_system_score_codex":0.0014666966,"about_ca_system_score_gemma":0.0013441521,"threshold_uncertainty_score":0.12282866},"labels":[],"label_agreement":null},{"id":"W6893188177","doi":"10.5281/zenodo.14781299","title":"Automatic image moderation","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Drive & Gear (Canada)","funders":"","keywords":"Image (mathematics); Moderation; Image processing; Social media; Studio","score_opus":0.0291849387415199,"score_gpt":0.25010499405623554,"score_spread":0.22092005531471565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893188177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31682065,0.0029900938,0.53880036,0.0011006404,0.0013405443,0.0020349901,0.0071353777,0.07549205,0.054285243],"genre_scores_gemma":[0.5834057,0.0009229932,0.3707279,0.00034529576,0.0007580416,0.0009280016,0.009554807,0.0036173728,0.029739909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989047,0.00021260716,0.00004272015,0.0002816724,0.00035814437,0.0002000874],"domain_scores_gemma":[0.9969805,0.0006114399,0.00023518078,0.000559787,0.0013341936,0.0002788527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013944793,0.000995827,0.00090773695,0.0034879693,0.00072062237,0.000852676,0.000659896,0.0009521047,0.020168243],"category_scores_gemma":[0.004554959,0.0003016795,0.0005269796,0.0010395651,0.00032344204,0.001266927,0.0025963392,0.0008590291,0.01209655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018650569,0.00025107342,0.012089239,0.00077322964,0.00013296287,0.00048708974,0.00074692006,0.00089226663,0.245878,0.0021501381,0.05319429,0.6815398],"study_design_scores_gemma":[0.00022715333,0.0011707966,0.09987741,0.0002572729,0.00035904185,0.0028798173,0.0016418394,0.13320461,0.5152361,0.006253688,0.23866045,0.00023186194],"about_ca_topic_score_codex":0.00056487555,"about_ca_topic_score_gemma":0.0010587177,"teacher_disagreement_score":0.020168243,"about_ca_system_score_codex":0.0002562103,"about_ca_system_score_gemma":0.00034750445,"threshold_uncertainty_score":0.06746948},"labels":[],"label_agreement":null},{"id":"W6893737342","doi":"10.5281/zenodo.3860016","title":"Сінофобія та кіберагресія в умовах глобального виклику COVID-19: комунікаційні аспекти","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Novelty; Ethnic group; Racism; Aggression; Phenomenon; Content analysis","score_opus":0.05431886618320625,"score_gpt":0.25320088870893215,"score_spread":0.1988820225257259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893737342","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10698717,0.0363342,0.038551923,0.02361336,0.003715212,0.00035243636,0.0005618415,0.00042002997,0.7894638],"genre_scores_gemma":[0.7609815,0.033543542,0.029844707,0.0015747622,0.0008142906,0.00034834468,0.00044997755,0.0005063032,0.17193659],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984261,0.00037014406,0.00008394945,0.00020320255,0.00067247497,0.00024418655],"domain_scores_gemma":[0.99866986,0.00038418206,0.0001566578,0.00011376357,0.00043132677,0.00024422028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017239855,0.00046188492,0.00026124858,0.0019576354,0.0032824432,0.0060547376,0.00040902666,0.0010864566,0.022687806],"category_scores_gemma":[0.0029570002,0.0005088477,0.00034346976,0.0022570547,0.0030493333,0.0022960454,0.0022513855,0.0022778215,0.00719326],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000143848,0.00014825026,0.011293134,0.0011233346,0.00003262436,0.0034017563,0.050284553,0.0008721885,0.008630707,0.49896646,0.053279806,0.37182346],"study_design_scores_gemma":[0.000008808443,0.000033594726,0.007688164,0.00041783424,0.000017403085,0.0011768271,0.0106443325,0.00026375678,0.001911809,0.02579471,0.9519955,0.000047304868],"about_ca_topic_score_codex":0.010669983,"about_ca_topic_score_gemma":0.017969457,"teacher_disagreement_score":0.022687806,"about_ca_system_score_codex":0.0027141,"about_ca_system_score_gemma":0.005917475,"threshold_uncertainty_score":0.07589823},"labels":[],"label_agreement":null},{"id":"W6907477828","doi":"10.20381/ruor-27608","title":"Automating Hate: Exploring Toxic Reddit Norms with Google Perspective","year":2022,"lang":"en","type":"article","venue":"uO Research (University of Ottawa)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Moderation; Context (archaeology); Operationalization; User-generated content; Online community; Content analysis; Perspective (graphical); Social media; Test (biology); Legislation","score_opus":0.06270395740232802,"score_gpt":0.26713235756876746,"score_spread":0.20442840016643943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6907477828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8617186,0.000526117,0.045254454,0.005383848,0.000087158594,0.00033880846,0.00077985757,0.00032925024,0.085581996],"genre_scores_gemma":[0.9850761,0.00015830455,0.011139648,0.00027750692,0.000020220108,0.000097074204,0.00021819709,0.00008788753,0.0029250744],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993652,0.0036497246,0.00013950653,0.0005232737,0.0016635213,0.00037189716],"domain_scores_gemma":[0.9725317,0.019155059,0.0028048803,0.0022742618,0.0027582797,0.00047571812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007859929,0.00050855626,0.00031830592,0.0052831178,0.0037026943,0.0054539954,0.0010288311,0.0010488909,0.0024767017],"category_scores_gemma":[0.026144266,0.00028820444,0.00034469986,0.0032852355,0.008397181,0.0068684896,0.004429972,0.0014723773,0.0003756013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002046147,0.000090960144,0.104620405,0.00050961704,0.000038229773,0.0007615985,0.68860936,0.00097600336,0.0056635295,0.122733034,0.004325008,0.07146773],"study_design_scores_gemma":[0.00005296681,0.0002031915,0.13208236,0.0006278456,0.00010941296,0.0005984249,0.61486554,0.01883869,0.008447252,0.08537537,0.13858847,0.00021046432],"about_ca_topic_score_codex":0.03996897,"about_ca_topic_score_gemma":0.06281823,"teacher_disagreement_score":0.03996897,"about_ca_system_score_codex":0.0048846104,"about_ca_system_score_gemma":0.0027159515,"threshold_uncertainty_score":0.07947266},"labels":[],"label_agreement":null},{"id":"W6911291967","doi":"10.5281/zenodo.11661647","title":"+[ L.i.v.e — S.t.r.e.a.m ]* Le Mans 24 Hours 2024 L i v e F r e e ON TV Channel","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Channel (broadcasting); Test (biology); Reading (process); Thursday; Race (biology); Time lag","score_opus":0.025796541624844266,"score_gpt":0.22945226893331352,"score_spread":0.20365572730846926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911291967","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033902933,0.00025656258,0.0008707218,0.0013309491,0.0024277514,0.00009553875,0.0015719106,0.0039225514,0.9891851],"genre_scores_gemma":[0.0010707264,0.00016138019,0.0003282797,0.00052923255,0.00013373645,0.000039100778,0.0008293533,0.0007771777,0.99613094],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999759,0.000026899932,0.0000073668566,0.000060817736,0.00008736916,0.00005853305],"domain_scores_gemma":[0.9994267,0.00003980178,0.000013969807,0.000060885257,0.00028861038,0.00017012689],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00035635062,0.001047153,0.0006138632,0.00051994924,0.0020366518,0.003902954,0.0011881635,0.001297426,0.88741386],"category_scores_gemma":[0.001424258,0.00038769358,0.00046203527,0.00046660495,0.00038905084,0.0021484524,0.0025485512,0.00201012,0.8647157],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026289366,0.000014512636,0.000040933774,0.000024019979,8.867062e-7,0.000016819547,0.00003373047,0.000010305545,0.00019669866,0.000788169,0.9815329,0.017314851],"study_design_scores_gemma":[0.0000041903504,0.000009428406,0.00010600273,0.000021581836,5.417255e-7,0.000017758694,0.00005305874,0.000020007485,0.00007715254,0.0001182025,0.9995696,0.0000024886954],"about_ca_topic_score_codex":0.005193375,"about_ca_topic_score_gemma":0.009665587,"teacher_disagreement_score":0.11258614,"about_ca_system_score_codex":0.00071817555,"about_ca_system_score_gemma":0.0006985848,"threshold_uncertainty_score":0.16059041},"labels":[],"label_agreement":null},{"id":"W6912601858","doi":"10.5281/zenodo.3860015","title":"Сінофобія та кіберагресія в умовах глобального виклику COVID-19: комунікаційні аспекти","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Novelty; Ethnic group; Racism; Aggression; Phenomenon; Content analysis","score_opus":0.05431886618320625,"score_gpt":0.25320088870893215,"score_spread":0.1988820225257259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912601858","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10698717,0.0363342,0.038551923,0.02361336,0.003715212,0.00035243636,0.0005618415,0.00042002997,0.7894638],"genre_scores_gemma":[0.7609815,0.033543542,0.029844707,0.0015747622,0.0008142906,0.00034834468,0.00044997755,0.0005063032,0.17193659],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9984261,0.00037014406,0.00008394945,0.00020320255,0.00067247497,0.00024418655],"domain_scores_gemma":[0.99866986,0.00038418206,0.0001566578,0.00011376357,0.00043132677,0.00024422028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017239855,0.00046188492,0.00026124858,0.0019576354,0.0032824432,0.0060547376,0.00040902666,0.0010864566,0.022687806],"category_scores_gemma":[0.0029570002,0.0005088477,0.00034346976,0.0022570547,0.0030493333,0.0022960454,0.0022513855,0.0022778215,0.00719326],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000143848,0.00014825026,0.011293134,0.0011233346,0.00003262436,0.0034017563,0.050284553,0.0008721885,0.008630707,0.49896646,0.053279806,0.37182346],"study_design_scores_gemma":[0.000008808443,0.000033594726,0.007688164,0.00041783424,0.000017403085,0.0011768271,0.0106443325,0.00026375678,0.001911809,0.02579471,0.9519955,0.000047304868],"about_ca_topic_score_codex":0.010669983,"about_ca_topic_score_gemma":0.017969457,"teacher_disagreement_score":0.022687806,"about_ca_system_score_codex":0.0027141,"about_ca_system_score_gemma":0.005917475,"threshold_uncertainty_score":0.07589823},"labels":[],"label_agreement":null},{"id":"W6912993244","doi":"10.5281/zenodo.8238782","title":"About the Immigration Status of David DePape After the Attempted Murder on Paul Pelosi Dated October 28th 2022 - #How \"Cancel Culture\" in the United State of America (U.S.A) is Really Enabling Racism on Twitter & Elsewhere - #Michael Ayele (a.k.a) W","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Immigration; State (computer science); Residence; Commission; Law enforcement; Alien; Enforcement; Deportation; Civil liberties","score_opus":0.027186504507037978,"score_gpt":0.25381401261933245,"score_spread":0.22662750811229448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912993244","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28430942,0.006184073,0.00039663137,0.31473267,0.011332209,0.00014063058,0.0009319367,0.00014569618,0.38182673],"genre_scores_gemma":[0.5503749,0.0050506294,0.00032357877,0.102645405,0.0026588233,0.000120150566,0.0005760567,0.00008052392,0.3381698],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960965,0.00007323726,0.000017348968,0.00006159654,0.000098813514,0.00013941014],"domain_scores_gemma":[0.99904364,0.00020249806,0.00014911946,0.0000409088,0.0002231291,0.00034066566],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004981288,0.00027648616,0.00019692893,0.00045232294,0.01071162,0.0017924153,0.0005319676,0.0024119779,0.015516768],"category_scores_gemma":[0.004055688,0.00019793172,0.00012840123,0.00026217083,0.0016749741,0.0015511246,0.002266013,0.0056532435,0.0034047232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008876023,0.000080460435,0.024122743,0.00014536249,0.000017184308,0.010473829,0.0766562,0.00007468006,0.0015311148,0.008456752,0.84444475,0.033908263],"study_design_scores_gemma":[0.000011433444,0.00007050564,0.036703065,0.00040719952,0.000015750924,0.0025716494,0.12743756,0.00012270325,0.001078381,0.0005886651,0.8309363,0.000056747118],"about_ca_topic_score_codex":0.07370515,"about_ca_topic_score_gemma":0.1996445,"teacher_disagreement_score":0.98448324,"about_ca_system_score_codex":0.0019561015,"about_ca_system_score_gemma":0.0016468194,"threshold_uncertainty_score":0.14655226},"labels":[],"label_agreement":null},{"id":"W6920851246","doi":"10.60770/q3mf-ht32","title":"Online hate speech against gender variance","year":2024,"lang":"en","type":"article","venue":"MRU-Repo","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Variance (accounting); Social media; Preference; Noise (video); Affect (linguistics)","score_opus":0.020517580871869043,"score_gpt":0.25447377288080975,"score_spread":0.2339561920089407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920851246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97449297,0.00020144797,0.004507574,0.00019912097,0.00012455681,0.00003643595,0.0013725178,0.0001828293,0.018882561],"genre_scores_gemma":[0.9971204,0.00002815974,0.0006496883,0.000033970373,0.000050329163,0.000015556789,0.00039645398,0.000031541207,0.001674018],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998085,0.0006085472,0.00007872764,0.00040571476,0.0005407639,0.00028115854],"domain_scores_gemma":[0.9887123,0.006702484,0.0019291744,0.0008106758,0.001205215,0.0006401843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019136873,0.0005158055,0.00047884104,0.0013653777,0.0006632064,0.0014641136,0.00040996334,0.000840474,0.011215154],"category_scores_gemma":[0.015377985,0.00017825584,0.00035120934,0.0005999646,0.00052123744,0.0012532251,0.0013086313,0.0007757989,0.0044666515],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002106611,0.0007443448,0.8216741,0.0001714063,0.00039766706,0.00030538702,0.0024306304,0.0016919227,0.022439921,0.0029725546,0.0059988415,0.13906671],"study_design_scores_gemma":[0.000019116722,0.0007023702,0.95556563,0.00007216041,0.00022221165,0.00074554235,0.0024554683,0.022767773,0.011346084,0.0012864156,0.00476575,0.00005140152],"about_ca_topic_score_codex":0.0015460205,"about_ca_topic_score_gemma":0.0023405154,"teacher_disagreement_score":0.011215154,"about_ca_system_score_codex":0.00023417712,"about_ca_system_score_gemma":0.0002583136,"threshold_uncertainty_score":0.03751844},"labels":[],"label_agreement":null},{"id":"W6931621983","doi":"10.5281/zenodo.8238673","title":"About the United States of America (U.S.A) \"Cancel Culture\" and Racism on Twitter - #Michael Ayele (a.k.a) W October 28th 2022 - #Association for the Advancement of Civil Liberties (AACL)","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Racism; Civil liberties; Government (linguistics); Misinformation; Subject (documents); German; Residence; Censorship","score_opus":0.023865926290868218,"score_gpt":0.2443710531068599,"score_spread":0.22050512681599166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931621983","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058244863,0.0057411403,0.00033888026,0.06734518,0.017089078,0.00018256516,0.028749024,0.0012815128,0.87344813],"genre_scores_gemma":[0.027381979,0.012375295,0.00056966895,0.02529281,0.0046073073,0.0003078252,0.012038995,0.0007166784,0.9167096],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99951816,0.000104428,0.000039690716,0.00006329416,0.00019831701,0.000076129654],"domain_scores_gemma":[0.9976527,0.0003472807,0.00028555474,0.00016913637,0.00068996946,0.000855334],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006914994,0.0005398978,0.00037805614,0.0011294704,0.0032869363,0.0038769688,0.00031201466,0.0018693669,0.32763225],"category_scores_gemma":[0.0034248335,0.00026969917,0.00023304074,0.0019194521,0.0008029439,0.0039812885,0.0022396573,0.0020283107,0.23997317],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000812604,0.0000070857586,0.00054759544,0.000042687505,0.0000011572171,0.000019498431,0.00022133032,0.0000037330708,0.00007914545,0.00043164068,0.98738897,0.011249074],"study_design_scores_gemma":[0.0000026880057,0.00000827047,0.0024323198,0.00012590394,0.0000023159905,0.000025712892,0.0008011299,0.000014647621,0.000070599504,0.00010992706,0.99639845,0.00000783089],"about_ca_topic_score_codex":0.012160898,"about_ca_topic_score_gemma":0.020559825,"teacher_disagreement_score":0.32763225,"about_ca_system_score_codex":0.00088239025,"about_ca_system_score_gemma":0.0014040953,"threshold_uncertainty_score":0.959051},"labels":[],"label_agreement":null},{"id":"W6958730148","doi":"10.6084/m9.figshare.22229713.v2","title":"Can-SWaP_V1","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Watershed; Surface water; Water resources; Water quality; Water source; Hydrology (agriculture); Water use; Work (physics)","score_opus":0.0332472362323894,"score_gpt":0.2529462102916842,"score_spread":0.21969897405929478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958730148","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024253094,0.000048986545,0.00005712385,0.000033912933,0.000010311816,0.000012596669,0.99865973,0.00039771677,0.00053710863],"genre_scores_gemma":[0.00048700458,0.00003232108,0.00020033153,0.000015860009,0.0000021378714,0.000025616355,0.99882144,0.000038695365,0.00037659207],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924636,0.000061213555,0.00005657604,0.00020805174,0.0002323011,0.00019556025],"domain_scores_gemma":[0.9982672,0.00022180685,0.00010289834,0.00030166074,0.0008781655,0.00022829605],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005883003,0.002473056,0.0014553949,0.0039464715,0.0015839745,0.0023077137,0.003492105,0.0018464468,0.029268818],"category_scores_gemma":[0.003534408,0.0007507438,0.0012521108,0.007908627,0.0007293431,0.0008685916,0.0016820665,0.0018491404,0.0406772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004958401,0.000020428199,0.0015380786,0.0003671571,0.000035937675,0.000030393778,0.000033349406,0.00059060275,0.00011255265,0.00043003564,0.99444157,0.0023503602],"study_design_scores_gemma":[0.00022550146,0.000015738779,0.014078266,0.00027620955,0.00004804904,0.000083424005,0.00019082092,0.0021095427,0.0005953585,0.00093424,0.9813829,0.000060067032],"about_ca_topic_score_codex":0.7668905,"about_ca_topic_score_gemma":0.8803525,"teacher_disagreement_score":0.9707312,"about_ca_system_score_codex":0.0067899516,"about_ca_system_score_gemma":0.012875833,"threshold_uncertainty_score":0.46896458},"labels":[],"label_agreement":null},{"id":"W6962459277","doi":"10.17613/2he9d-mxb32","title":"Online Hate on YouTube: Anti-immigrant Rhetoric Against Syrian Refugees in Canada and Turkey","year":2020,"lang":"en","type":"article","venue":"Knowledge Commons (Lakehead University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rhetoric; Refugee; Turkish; Syrian refugees; Racism; National identity; Identity (music); Asylum seeker; Discourse analysis","score_opus":0.017144164747960634,"score_gpt":0.2069749978284144,"score_spread":0.18983083308045376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6962459277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98853964,0.00063265976,0.00011634996,0.00088050606,0.000038767546,0.000034140405,0.0007243636,0.000023997327,0.009009617],"genre_scores_gemma":[0.9938392,0.00083524146,0.00024299588,0.00030947468,0.000023703878,0.000024928118,0.00053832395,0.000020517871,0.0041655614],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9992379,0.0002196038,0.00003122904,0.00007850545,0.00022261469,0.00021014939],"domain_scores_gemma":[0.99727386,0.0010628105,0.00042825358,0.000067229426,0.0007747773,0.00039307005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010110096,0.00032377214,0.0002446703,0.0028076929,0.0049527306,0.0028166454,0.00042017427,0.0005847315,0.0019663984],"category_scores_gemma":[0.003912719,0.00010672125,0.00010359477,0.0029196562,0.0022906186,0.0010460234,0.0012532395,0.0006345483,0.00032039752],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005487094,0.00013680346,0.3012627,0.00088856346,0.000056979992,0.0034427466,0.5714561,0.00029713032,0.007347738,0.0032092175,0.03792206,0.07343136],"study_design_scores_gemma":[0.000009793564,0.00006599704,0.25015855,0.0003237183,0.00004191712,0.00038073544,0.6940563,0.00082361134,0.002203349,0.00014016415,0.051738247,0.00005764094],"about_ca_topic_score_codex":0.7482028,"about_ca_topic_score_gemma":0.8879268,"teacher_disagreement_score":0.2517972,"about_ca_system_score_codex":0.007857413,"about_ca_system_score_gemma":0.006513621,"threshold_uncertainty_score":0.50656015},"labels":[],"label_agreement":null},{"id":"W6966759462","doi":"10.48448/4bbn-ea10","title":"Large Language Models for Legally Enforceable Hate Speech Detection","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Voice activity detection; Language model; Free speech; Natural language; Language identification","score_opus":0.02691930106746422,"score_gpt":0.2868045838022572,"score_spread":0.259885282734793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6966759462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1636192,0.0049723447,0.7865788,0.006159718,0.00079540705,0.00024792197,0.011174996,0.018132215,0.008319336],"genre_scores_gemma":[0.84265935,0.000994346,0.12515044,0.00087987655,0.00061925064,0.00035699853,0.013731791,0.0010461007,0.014561817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873894,0.0006027963,0.000068864065,0.00029999376,0.00015381945,0.00013560675],"domain_scores_gemma":[0.99119616,0.0071922243,0.00021538977,0.00065977976,0.00054563064,0.00019076852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002795989,0.001411461,0.0012975786,0.0014234512,0.0010360146,0.0017276355,0.0020444335,0.0023207024,0.007263675],"category_scores_gemma":[0.01162897,0.00097173074,0.0016478216,0.0010466245,0.0006703026,0.003007218,0.001774067,0.0041528014,0.0047062053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012984562,0.0010263369,0.007129846,0.00037890507,0.00047828915,0.00059727643,0.00037069878,0.47389555,0.0072739306,0.02068608,0.05342077,0.43344384],"study_design_scores_gemma":[0.000017512382,0.000017177133,0.00030461253,0.000009045345,0.000022736458,0.000020445521,0.000020040778,0.99047273,0.00042500213,0.007886005,0.0007957623,0.000008888362],"about_ca_topic_score_codex":0.019071857,"about_ca_topic_score_gemma":0.031107394,"teacher_disagreement_score":0.019071857,"about_ca_system_score_codex":0.0013390324,"about_ca_system_score_gemma":0.0015218875,"threshold_uncertainty_score":0.037921667},"labels":[],"label_agreement":null},{"id":"W6982720535","doi":"","title":"Jodi Lazare: Are Ag-gag Laws Unconstitutional?","year":2021,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Constitutionality; Work (physics); Freedom of expression; Human rights","score_opus":0.014038336717197342,"score_gpt":0.22996947693246692,"score_spread":0.21593114021526957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6982720535","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015422064,0.012416778,0.00090540317,0.9184523,0.0057350514,0.000024230076,0.00012629958,0.000083053535,0.0607146],"genre_scores_gemma":[0.048341077,0.010011957,0.001324618,0.7818043,0.003788426,0.00010960836,0.00017501389,0.00015184618,0.15429318],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.995073,0.0009457059,0.00014771345,0.0007652081,0.0023459555,0.000722352],"domain_scores_gemma":[0.9884382,0.006540311,0.00049155334,0.00027053553,0.0031226757,0.0011367395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049366057,0.00045099691,0.00061841746,0.0015659074,0.010456369,0.008018819,0.0019323161,0.013409387,0.014625384],"category_scores_gemma":[0.024618268,0.00048015694,0.00038192183,0.0009989867,0.0062746634,0.007771743,0.0025338999,0.015072146,0.004775542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060980033,0.000016355825,0.00059366337,0.000024630961,0.0000023841098,0.00005017322,0.00077663135,0.000012483423,0.000035788482,0.03300499,0.95509815,0.010378675],"study_design_scores_gemma":[0.000010445217,0.000010396858,0.001873934,0.00022602815,0.000009342793,0.00010097014,0.0041962685,0.00011433997,0.00020572738,0.013700316,0.9795196,0.000032640575],"about_ca_topic_score_codex":0.08074462,"about_ca_topic_score_gemma":0.14505365,"teacher_disagreement_score":0.08074462,"about_ca_system_score_codex":0.00501324,"about_ca_system_score_gemma":0.008506464,"threshold_uncertainty_score":0.16054928},"labels":[],"label_agreement":null},{"id":"W6986648382","doi":"","title":"Putting Canada’s hate crime data in context","year":2020,"lang":"en","type":"book-chapter","venue":"Leiden Repository (Leiden University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.01980361192962882,"score_gpt":0.18206284227733752,"score_spread":0.1622592303477087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986648382","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015549277,0.017255992,0.023001706,0.06543684,0.0023633775,0.00014603129,0.5089021,0.00821835,0.35912633],"genre_scores_gemma":[0.18241121,0.022147896,0.060181435,0.00899627,0.0010691704,0.0002227703,0.44783208,0.0066484036,0.27049074],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99733573,0.00023195911,0.00008625591,0.00026827783,0.0017336649,0.00034416787],"domain_scores_gemma":[0.99315464,0.0014782097,0.00029710575,0.0006941146,0.00384053,0.0005354898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015770895,0.0007860209,0.00058942335,0.010034855,0.004080523,0.012732096,0.0018732623,0.0013499479,0.022643127],"category_scores_gemma":[0.012616404,0.0006158653,0.00054601126,0.02287839,0.0018219139,0.0041852356,0.0022693165,0.001915684,0.009064413],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018605993,0.000011476931,0.0049321293,0.00013648061,0.000025526653,0.00011028067,0.0008727114,0.00068524276,0.00018958867,0.017884623,0.90940845,0.06572498],"study_design_scores_gemma":[0.000003102335,0.0000016120663,0.0051025357,0.00015410123,0.000014786841,0.00004316655,0.001334821,0.000522381,0.00036020044,0.0037907725,0.9886513,0.00002118715],"about_ca_topic_score_codex":0.98269606,"about_ca_topic_score_gemma":0.9935954,"teacher_disagreement_score":0.02891937,"about_ca_system_score_codex":0.02891937,"about_ca_system_score_gemma":0.06240778,"threshold_uncertainty_score":0.2098257},"labels":[],"label_agreement":null},{"id":"W6991065435","doi":"","title":"The Expansion of Canadian Hate Speech Legislation","year":2017,"lang":"en","type":"article","venue":"SMU Scholar (Southern Methodist University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislation; Government (linguistics); Constitution; Derogation; Free speech","score_opus":0.024898375990051846,"score_gpt":0.23435104041735097,"score_spread":0.20945266442729912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991065435","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27790743,0.0048346613,0.0026689337,0.1846372,0.0026110706,0.0003060206,0.0037442383,0.00033443395,0.522956],"genre_scores_gemma":[0.7685243,0.0026284852,0.0023565523,0.034522526,0.000369543,0.000094953015,0.0008568901,0.00011311071,0.19053365],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99041593,0.0005753761,0.0001540113,0.0005677808,0.0050634523,0.0032234963],"domain_scores_gemma":[0.97361064,0.0025260483,0.0010545918,0.0005627862,0.01795449,0.0042915796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046187486,0.0004556472,0.00039225654,0.0041682785,0.015120918,0.0063953158,0.0018069751,0.0033279497,0.01618637],"category_scores_gemma":[0.01684444,0.00050204544,0.00070843485,0.0037401894,0.0032897617,0.001597653,0.0021157728,0.004595248,0.0010397804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024630583,0.0002071475,0.08884933,0.00043937037,0.00008172506,0.0011348095,0.022920705,0.0011527262,0.004136813,0.20601332,0.5084695,0.16634823],"study_design_scores_gemma":[0.0000241705,0.000049537823,0.20160027,0.00023872474,0.000049490885,0.00019713142,0.012343795,0.0008810358,0.0013345884,0.001523868,0.7816634,0.000093976254],"about_ca_topic_score_codex":0.99488527,"about_ca_topic_score_gemma":0.9982127,"teacher_disagreement_score":0.13667074,"about_ca_system_score_codex":0.13667074,"about_ca_system_score_gemma":0.22887821,"threshold_uncertainty_score":0.99162024},"labels":[],"label_agreement":null},{"id":"W6995953302","doi":"","title":"Prosecuting Hate Speech: Keegstra, Zundel, and the Criminal Law's Ability to Protect Vulnerable Communities","year":2025,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"BC Cancer Agency","keywords":"Racism; Criminal law; Scholarship; Supreme court; Criminal code; Antisemitism; Terrorism; Legislation","score_opus":0.01026094845891354,"score_gpt":0.16758027843349216,"score_spread":0.15731932997457862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995953302","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24659891,0.029330036,0.0009525608,0.36710194,0.0010100821,0.00009421291,0.000036614736,0.000041263746,0.35483444],"genre_scores_gemma":[0.91784793,0.01327696,0.0006550151,0.038168363,0.00016954537,0.000034928857,0.000012520743,0.000018639243,0.02981598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99579877,0.0016010519,0.00010198955,0.00033007516,0.0008502953,0.0013177533],"domain_scores_gemma":[0.99147075,0.0052918415,0.0007216862,0.00029071266,0.0014937666,0.0007313187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005657032,0.00024499872,0.0003020378,0.0015085334,0.018458493,0.010690273,0.0018234156,0.00699439,0.0024605903],"category_scores_gemma":[0.014290468,0.00030666584,0.0002610325,0.0009274879,0.019711833,0.008231161,0.0035519893,0.007494561,0.00034037887],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000388313,0.0001582954,0.01674045,0.00024187942,0.000031517884,0.001655042,0.18419345,0.00036675527,0.00037327482,0.5971663,0.09710093,0.10193332],"study_design_scores_gemma":[0.000037543574,0.00012679769,0.019056594,0.0024182796,0.00010180555,0.0018895261,0.43630552,0.0007793117,0.0009517295,0.13746248,0.40070552,0.00016493018],"about_ca_topic_score_codex":0.41784918,"about_ca_topic_score_gemma":0.6924433,"teacher_disagreement_score":0.5821508,"about_ca_system_score_codex":0.0202068,"about_ca_system_score_gemma":0.030970555,"threshold_uncertainty_score":0.83083403},"labels":[],"label_agreement":null},{"id":"W6999448015","doi":"","title":"The court of public opinion: fandom, celebrity, and networked harassment on TikTok","year":2024,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Harassment; Legislation; Government (linguistics); Context (archaeology); Confidentiality","score_opus":0.018491804461811492,"score_gpt":0.2483489934756615,"score_spread":0.22985718901385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999448015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8539926,0.000636614,0.000044359633,0.03369597,0.00059965055,0.000030349349,0.00021607874,0.0000066976218,0.110777766],"genre_scores_gemma":[0.9780676,0.00044018257,0.000014132277,0.0025030228,0.00012187485,0.00001299847,0.00006404737,0.000012680806,0.018763525],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99880826,0.00033664345,0.000021569595,0.00006408338,0.00023044302,0.00053907017],"domain_scores_gemma":[0.994584,0.001782367,0.00086073275,0.00014007259,0.00088844926,0.0017443975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013576929,0.00015573269,0.00027700356,0.00073327683,0.009674459,0.004129634,0.00059791666,0.0018385866,0.026476737],"category_scores_gemma":[0.010651436,0.00017617509,0.00014731695,0.00079018367,0.002392408,0.002072983,0.0018033594,0.00427331,0.0018528872],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006903941,0.001698298,0.34866413,0.00011339589,0.00006934767,0.003382097,0.22082205,0.00020170795,0.00048696718,0.025205854,0.33814496,0.060520794],"study_design_scores_gemma":[0.00003253856,0.00011285081,0.433066,0.00026402524,0.00004167059,0.0004252642,0.4936637,0.0003048361,0.00016924167,0.00295983,0.068917386,0.000042709744],"about_ca_topic_score_codex":0.098906614,"about_ca_topic_score_gemma":0.23306133,"teacher_disagreement_score":0.098906614,"about_ca_system_score_codex":0.003249148,"about_ca_system_score_gemma":0.0028063757,"threshold_uncertainty_score":0.19666183},"labels":[],"label_agreement":null},{"id":"W7006197657","doi":"","title":"Terrorism and its legal aftermath: The limits on freedom of expression in Canada’s Anti-terrorism Act &amp; National Security Act","year":2019,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Freedom of expression; Charter; Legislation; Proportionality (law); Expression (computer science); National security; Terrorism; Speech act","score_opus":0.0466280636431675,"score_gpt":0.27268615364934434,"score_spread":0.22605809000617683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006197657","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27262974,0.0021288556,0.005617192,0.018369438,0.0002412221,0.0003978988,0.0009621896,0.000054950822,0.6995985],"genre_scores_gemma":[0.97181827,0.0008679523,0.0011568902,0.0027942928,0.00005264941,0.00008411226,0.00019800146,0.000012185255,0.023015728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9901231,0.00083191524,0.00029117183,0.00046389003,0.0057081794,0.0025818287],"domain_scores_gemma":[0.9925459,0.003360547,0.001051626,0.0004311504,0.0021045143,0.0005061676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034682013,0.00033107246,0.00042218599,0.0026608051,0.007592546,0.008629304,0.001221969,0.0027387307,0.0034215786],"category_scores_gemma":[0.011188103,0.00037900516,0.00081646297,0.0019352563,0.011889456,0.0022209086,0.0016507895,0.005166932,0.00029357063],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044413027,0.00007752532,0.015388305,0.000056855533,0.000016215805,0.0005416898,0.005066758,0.0015760554,0.00038476085,0.95356786,0.010092171,0.013187514],"study_design_scores_gemma":[0.000100683224,0.00030877133,0.30985424,0.0015239216,0.00028761555,0.0009080549,0.037448376,0.019267967,0.0050864136,0.29927653,0.32556683,0.00037054325],"about_ca_topic_score_codex":0.9080516,"about_ca_topic_score_gemma":0.94599056,"teacher_disagreement_score":0.09194839,"about_ca_system_score_codex":0.04313081,"about_ca_system_score_gemma":0.06611664,"threshold_uncertainty_score":0.31293744},"labels":[],"label_agreement":null},{"id":"W7009178908","doi":"","title":"Die Verwundbarkeit des Datenschutzes und der vertrauenswurdigen Inhaltserzeugung in KI-Modellen","year":2025,"lang":"en","type":"dissertation","venue":"mediaTUM  (Technical University of Munich)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Trustworthiness; Key (lock); Unintended consequences; Content (measure theory); Component (thermodynamics); Information privacy","score_opus":0.01138536920616166,"score_gpt":0.23047627936984388,"score_spread":0.21909091016368223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009178908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060334858,0.0003905114,0.93102634,0.0015795779,0.00009267722,0.000080730424,0.00026318626,0.0013040132,0.004928194],"genre_scores_gemma":[0.8672976,0.0007432678,0.1238833,0.00046767495,0.000038537983,0.00017370623,0.00047945362,0.00046366386,0.0064527784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965599,0.0011130556,0.00022772506,0.0007611684,0.0009852676,0.00035274483],"domain_scores_gemma":[0.9849939,0.008952102,0.00081563933,0.0037087174,0.0012772618,0.0002523862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050116703,0.0008194857,0.0008912906,0.0007989511,0.0009796008,0.0046481267,0.0014871343,0.0016459485,0.00332603],"category_scores_gemma":[0.033403754,0.0008462774,0.0010595743,0.00069188763,0.0030831762,0.010300476,0.003922464,0.0055794464,0.0012305676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047777584,0.00022112271,0.003668316,0.0003187272,0.00015305262,0.00028460412,0.0009747603,0.6018968,0.0069965282,0.2731357,0.0026740455,0.10919852],"study_design_scores_gemma":[0.000012503752,0.00004791487,0.00025473657,0.00005502559,0.000023474964,0.000074764255,0.00012180098,0.833825,0.0059584156,0.15760317,0.0020046304,0.00001866616],"about_ca_topic_score_codex":0.005129038,"about_ca_topic_score_gemma":0.004680798,"teacher_disagreement_score":0.005129038,"about_ca_system_score_codex":0.0032851745,"about_ca_system_score_gemma":0.0027433229,"threshold_uncertainty_score":0.026504517},"labels":[],"label_agreement":null},{"id":"W7009761377","doi":"","title":"The First Amendment in Cross-Cultural Perspective: A Comparative Legal Analysis of the Freedom of Speech","year":2006,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Free speech; First amendment; Democracy; Constitution; Constitutional law; Free will; Sign (mathematics)","score_opus":0.012417106729997327,"score_gpt":0.2757273850982987,"score_spread":0.26331027836830134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009761377","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20363495,0.028522648,0.007406671,0.011855397,0.0004038207,0.00006104972,0.00009661704,0.000013943835,0.7480049],"genre_scores_gemma":[0.9854798,0.0066051586,0.0012723503,0.0009975394,0.00015228568,0.000070412665,0.000052456056,0.000018627665,0.005351421],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98898953,0.008708935,0.00015580308,0.0004617363,0.0010226604,0.00066134584],"domain_scores_gemma":[0.9844247,0.012935198,0.00070704176,0.00061059435,0.001046705,0.00027581526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009161096,0.00042013364,0.0005090136,0.009351829,0.009043197,0.0097739715,0.0013892712,0.0032546453,0.0045883395],"category_scores_gemma":[0.013602116,0.00023879044,0.0005179022,0.0063017155,0.036690954,0.01194994,0.004193473,0.0038033123,0.00028251688],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017812807,0.000025870637,0.00084153307,0.000071973845,0.0000129679065,0.00024699885,0.057719417,0.00013917836,0.0001062793,0.93069005,0.0009542714,0.009173704],"study_design_scores_gemma":[0.000040718885,0.00018826498,0.023425583,0.0028456596,0.00012300225,0.0008452197,0.30652687,0.001958351,0.0014957708,0.40549162,0.256968,0.00009096063],"about_ca_topic_score_codex":0.018261306,"about_ca_topic_score_gemma":0.022073414,"teacher_disagreement_score":0.018261306,"about_ca_system_score_codex":0.009784763,"about_ca_system_score_gemma":0.0028922078,"threshold_uncertainty_score":0.07099372},"labels":[],"label_agreement":null},{"id":"W7019206216","doi":"","title":"Freedom of Expression or Freedom From Hate: A Canadian Perspective","year":2018,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Expression (computer science); Freedom of expression; Government (linguistics)","score_opus":0.01293879187381404,"score_gpt":0.237649355085981,"score_spread":0.22471056321216698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019206216","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014613817,0.03657064,0.0064535965,0.14910595,0.00102651,0.000067666944,0.00053061696,0.00003142271,0.79159975],"genre_scores_gemma":[0.8271911,0.052948553,0.005294269,0.023187578,0.0009669648,0.000091941794,0.0003134635,0.0001359719,0.08987019],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99163437,0.0014916583,0.000264316,0.0011497833,0.002978458,0.0024813516],"domain_scores_gemma":[0.99098927,0.0028816967,0.00040718136,0.00029166762,0.0041846586,0.0012454642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053005423,0.0013743796,0.0017410741,0.011310779,0.025376093,0.01625383,0.003961922,0.009370517,0.014807923],"category_scores_gemma":[0.012189734,0.0008651534,0.0010791225,0.0117966775,0.05416056,0.010752834,0.004093266,0.012977372,0.00070048077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011509326,0.000012246358,0.00043876874,0.000036947768,0.0000047223825,0.000041612628,0.0031705243,0.00017332824,0.000033183278,0.98054403,0.007939502,0.0075936946],"study_design_scores_gemma":[0.00004171158,0.000034893066,0.010028446,0.00152532,0.00007213228,0.0002040118,0.027034676,0.0018299923,0.0002653702,0.507977,0.45076126,0.00022526662],"about_ca_topic_score_codex":0.99387324,"about_ca_topic_score_gemma":0.9937463,"teacher_disagreement_score":0.17721623,"about_ca_system_score_codex":0.17721623,"about_ca_system_score_gemma":0.16018082,"threshold_uncertainty_score":0.95431256},"labels":[],"label_agreement":null},{"id":"W7021937828","doi":"","title":"Le monteur-auteur","year":2005,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"ESPACE; Assyria; Ile de france","score_opus":0.003105372402736651,"score_gpt":0.130677621919007,"score_spread":0.12757224951627036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021937828","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0086352695,0.0018419158,0.014620767,0.0069264565,0.0029933478,0.000034191788,0.00026433376,0.0006990737,0.9639848],"genre_scores_gemma":[0.15233281,0.0012374159,0.007654906,0.0031681699,0.00063912,0.00006106932,0.00028669674,0.0008095716,0.83381027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980368,0.0004042399,0.000057071236,0.00061170186,0.0006140097,0.00027613313],"domain_scores_gemma":[0.99883634,0.00022289864,0.000074790114,0.0002464368,0.000444969,0.00017462143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000977498,0.0008066572,0.0003766657,0.0009138497,0.004251162,0.0071144057,0.0010114016,0.002478783,0.08870231],"category_scores_gemma":[0.0027648387,0.00045271014,0.0005803102,0.0005857071,0.0044223126,0.0055734254,0.0034129345,0.0033919038,0.017602492],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013475784,0.000015107015,0.0010883121,0.00012812644,0.000012617834,0.00041526035,0.0072537847,0.00018304479,0.003041593,0.8389999,0.08974356,0.058983926],"study_design_scores_gemma":[0.000004149157,0.000016464866,0.0007423481,0.000058529768,0.000004639304,0.00029432063,0.001362617,0.00012944976,0.00068900303,0.0076395916,0.9890404,0.000018678465],"about_ca_topic_score_codex":0.034903165,"about_ca_topic_score_gemma":0.04126252,"teacher_disagreement_score":0.08870231,"about_ca_system_score_codex":0.0039070216,"about_ca_system_score_gemma":0.0015450153,"threshold_uncertainty_score":0.2967388},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W7023541083","doi":"","title":"Optimization of the Double Quarter Wave Crab Cavity prototype for testing at SPS","year":2014,"lang":"en","type":"other","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. Department of Energy","keywords":"Upgrade; Large Hadron Collider; Luminosity; Quarter (Canadian coin); Synchrotron; Proton Synchrotron","score_opus":0.01576510743104715,"score_gpt":0.21895369062314815,"score_spread":0.203188583192101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023541083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8249421,0.00043634017,0.12179508,0.0010506321,0.00046049387,0.0006785778,0.0009147561,0.0032824283,0.04643948],"genre_scores_gemma":[0.9563608,0.00006831034,0.035072554,0.00007773259,0.00001748282,0.00020170877,0.00022376921,0.00022119006,0.0077564237],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995018,0.000049673137,0.000013136265,0.000062194835,0.00029048856,0.00008268028],"domain_scores_gemma":[0.99878126,0.00014271775,0.00012342136,0.00021910795,0.0005956869,0.00013778172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076586753,0.00063086994,0.00056934624,0.00033628228,0.0006276243,0.0008714732,0.0011245742,0.00095657643,0.0068617724],"category_scores_gemma":[0.0013226748,0.00036169606,0.00039702177,0.00021639215,0.0005209042,0.0005907558,0.0007156729,0.0005746843,0.0019065993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010991005,0.00022169549,0.0055230814,0.0005591157,0.00007071758,0.0009076075,0.0004119497,0.05671748,0.86771035,0.00400002,0.008170374,0.05460848],"study_design_scores_gemma":[0.00048992044,0.0067819376,0.017066961,0.000103991144,0.00015116704,0.0013140906,0.0005394215,0.1920588,0.72432184,0.00109641,0.05587482,0.00020058414],"about_ca_topic_score_codex":0.0015023649,"about_ca_topic_score_gemma":0.0018184988,"teacher_disagreement_score":0.0068617724,"about_ca_system_score_codex":0.0008783751,"about_ca_system_score_gemma":0.0015448807,"threshold_uncertainty_score":0.02295494},"labels":[],"label_agreement":null},{"id":"W7024104247","doi":"","title":"Prior learning assessment and recognition (PLAR) and the impact of globalization : a Canadian case study","year":2007,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Government of Canada; Government of Ontario; Government of Alberta","keywords":"Participant observation; Process (computing); Globalization; Narrative; Ask price; Formal learning","score_opus":0.016148508131897665,"score_gpt":0.2944461677392921,"score_spread":0.27829765960739444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024104247","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9531789,0.0005143924,0.00068378454,0.0053692623,0.000041525014,0.00016741159,0.0000624763,0.0000113627175,0.039970953],"genre_scores_gemma":[0.9890275,0.0007374161,0.0007698745,0.00039541666,0.000008520392,0.000044233468,0.00002331258,0.0000119547685,0.008981687],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99413824,0.0024321498,0.00015613936,0.00043926638,0.0014426992,0.0013914347],"domain_scores_gemma":[0.99055195,0.004034873,0.00070383295,0.00044860685,0.0019830072,0.0022778322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006617678,0.00048948673,0.00046564874,0.0017009993,0.030090574,0.0045437794,0.0018971078,0.0023998008,0.0042253817],"category_scores_gemma":[0.013497534,0.00031911384,0.00032382895,0.0026199329,0.011355947,0.002377964,0.004942819,0.0032696056,0.00026172277],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000597453,0.00017880747,0.015409701,0.00009044177,0.0000056329472,0.0064006923,0.94057316,0.00037555772,0.00068649364,0.0138615435,0.0030541366,0.019304052],"study_design_scores_gemma":[0.000014383436,0.000108755354,0.017351458,0.00020735954,0.000011676011,0.0016142948,0.92320967,0.00056558615,0.00056948146,0.00088020327,0.05540874,0.000058424663],"about_ca_topic_score_codex":0.91714287,"about_ca_topic_score_gemma":0.96072567,"teacher_disagreement_score":0.9436677,"about_ca_system_score_codex":0.05633229,"about_ca_system_score_gemma":0.054866653,"threshold_uncertainty_score":0.40872127},"labels":[],"label_agreement":null},{"id":"W7024514747","doi":"","title":"Segment Choice Models: Feature-Rich Models for Global Distortion in Statistical Machine Translation","year":2006,"lang":"en","type":"article","venue":"NPARC","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Distortion (music); Machine translation; Perplexity; Metric (unit); Phrase; Probabilistic logic; Translation (biology); Decoding methods","score_opus":0.019304538666729393,"score_gpt":0.251782996754317,"score_spread":0.23247845808758758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024514747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028717551,0.0004664896,0.96808225,0.0003649812,0.000046551075,0.0000781219,0.000399535,0.0009262561,0.0009182673],"genre_scores_gemma":[0.7738498,0.0005855834,0.21756089,0.00037448073,0.00018773162,0.00041305466,0.0017378413,0.0005455042,0.004745126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998599,0.00075363135,0.000063496955,0.00026454014,0.00024268948,0.00007668433],"domain_scores_gemma":[0.9948862,0.0037538428,0.0003473073,0.00057440746,0.000324118,0.000114137365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00325718,0.0011390293,0.001113481,0.00087742816,0.00047967702,0.0009698259,0.0018873385,0.0010956044,0.0027355354],"category_scores_gemma":[0.009509595,0.0005932969,0.0010057689,0.0012138492,0.0010760332,0.0026069393,0.0013721171,0.002580256,0.0011776771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009770173,0.00015326106,0.0050020614,0.00014848099,0.00022796904,0.00025228085,0.00043997724,0.68339217,0.006100898,0.050568476,0.0052268547,0.24751051],"study_design_scores_gemma":[0.000018069222,0.000039084982,0.0003815152,0.0000065469108,0.000014260641,0.000042614993,0.000010266453,0.9752959,0.0009564943,0.02232099,0.00089794316,0.000016281396],"about_ca_topic_score_codex":0.004539858,"about_ca_topic_score_gemma":0.005314133,"teacher_disagreement_score":0.004539858,"about_ca_system_score_codex":0.0011543911,"about_ca_system_score_gemma":0.00088953483,"threshold_uncertainty_score":0.017225862},"labels":[],"label_agreement":null},{"id":"W7027744237","doi":"","title":"A Comparative Perspective on the First Amendment: Free Speech, Militant Democracy, and the Primacy of Dignity as a Preferred Constitutional Value in Germany","year":2003,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Washington and Lee University","keywords":"Proscription; Dignity; Value (mathematics); Free speech; Incitement; Militant; Enforcement; Constitutional law; Racism","score_opus":0.018127999992894682,"score_gpt":0.2542028324547996,"score_spread":0.2360748324619049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027744237","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11748258,0.06033067,0.0024599798,0.056905765,0.0009475442,0.000042890613,0.00012914522,0.000027107046,0.76167434],"genre_scores_gemma":[0.96409756,0.008784726,0.00039848362,0.0060770684,0.0002723367,0.000029288172,0.000030149371,0.000014934131,0.020295348],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99527717,0.0023153743,0.000105724524,0.0003403086,0.00035461306,0.0016068349],"domain_scores_gemma":[0.99878746,0.0008112262,0.00014688772,0.000059565165,0.000071709655,0.00012321878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003683409,0.00059944415,0.0005500999,0.0028226022,0.008098475,0.011954095,0.0014082185,0.008112376,0.00493743],"category_scores_gemma":[0.0030541571,0.0003088129,0.00060878776,0.002403754,0.018928837,0.0074706567,0.0033126257,0.0038406132,0.00037437477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010399383,0.0000073386414,0.00010884672,0.000019829948,0.0000030472306,0.0001850177,0.002383522,0.00013448545,0.00004367936,0.99418855,0.0010286596,0.0018867027],"study_design_scores_gemma":[0.00006161374,0.00016587328,0.007761994,0.001261764,0.00006231762,0.00054542173,0.025225623,0.0011580572,0.0011489817,0.51926255,0.443224,0.00012172269],"about_ca_topic_score_codex":0.0671428,"about_ca_topic_score_gemma":0.093429744,"teacher_disagreement_score":0.0671428,"about_ca_system_score_codex":0.02264361,"about_ca_system_score_gemma":0.004097621,"threshold_uncertainty_score":0.16429168},"labels":[],"label_agreement":null},{"id":"W7033421917","doi":"","title":"Proposed technical changes for the next edition of the National Building Code","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Code (set theory); Building code; Code of practice; Work (physics)","score_opus":0.05274078890602832,"score_gpt":0.2628093693323997,"score_spread":0.21006858042637136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033421917","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047831334,0.0021087767,0.64409417,0.0448848,0.04323664,0.005210919,0.005752402,0.029144673,0.17773636],"genre_scores_gemma":[0.1226913,0.0014893148,0.45996004,0.01311927,0.0029402955,0.0026376906,0.009707353,0.005324172,0.38213056],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98823184,0.0019695181,0.00097482424,0.0012295481,0.0065406985,0.0010534896],"domain_scores_gemma":[0.9688194,0.0026112294,0.0010950414,0.005446569,0.021078026,0.0009497263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049798,0.0010024676,0.0006313397,0.0020957272,0.0033263683,0.0042739287,0.0018105084,0.004559387,0.029395746],"category_scores_gemma":[0.022900593,0.0008286321,0.0011943164,0.0011260562,0.0015886744,0.00253115,0.0017244575,0.0045362287,0.023315499],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007273832,0.0005101076,0.0049736113,0.00051497575,0.00006197509,0.0010101076,0.0016658226,0.0031539816,0.06533155,0.06280351,0.42838055,0.43086645],"study_design_scores_gemma":[0.000107709166,0.0003278822,0.006184961,0.00019665448,0.0000670759,0.00068934954,0.00037825128,0.005978276,0.028377615,0.004144705,0.9533737,0.00017379242],"about_ca_topic_score_codex":0.03726293,"about_ca_topic_score_gemma":0.041161753,"teacher_disagreement_score":0.03726293,"about_ca_system_score_codex":0.0042108325,"about_ca_system_score_gemma":0.008544173,"threshold_uncertainty_score":0.098338604},"labels":[],"label_agreement":null},{"id":"W7033920321","doi":"","title":"Search for the decay (B)over-bar(0) -&gt; D*(0)gamma","year":2000,"lang":"en","type":"article","venue":"KU ScholarWorks (The University of Kansas)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alfred P. Sloan Foundation; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Branching fraction; Particle decay; Radiative transfer; Meson; Detector; B meson; Electron; Limit (mathematics)","score_opus":0.011146126582545993,"score_gpt":0.2066811122976185,"score_spread":0.19553498571507252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033920321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99269015,0.0001771723,0.0033340892,0.000081766404,0.000007660017,0.000018009292,0.0003252525,0.00010924548,0.0032566919],"genre_scores_gemma":[0.99137723,0.00014568116,0.006444229,0.00006643652,0.00000845042,0.00002015661,0.00062965986,0.00001839349,0.0012897316],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996815,0.00004700315,0.000014468845,0.00011315953,0.0000817657,0.000062158695],"domain_scores_gemma":[0.99908555,0.0002616866,0.00028243547,0.00008990563,0.00012950251,0.00015101212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007253409,0.0003853844,0.00018391797,0.0008104147,0.00089953427,0.0005739686,0.00051634095,0.00062727294,0.0030552142],"category_scores_gemma":[0.0011284697,0.00022527411,0.00018935921,0.00049228343,0.00031924527,0.00038038753,0.0004894622,0.0003548145,0.00085288804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018212133,0.0004748944,0.3350478,0.00019342882,0.00011899973,0.0027137576,0.0006276484,0.0012454027,0.58150256,0.009231943,0.002620982,0.064401336],"study_design_scores_gemma":[0.00021510749,0.002227041,0.22504276,0.00006486252,0.00022283198,0.010879121,0.0010837348,0.024744159,0.6962864,0.004848315,0.03425089,0.00013477949],"about_ca_topic_score_codex":0.0015925742,"about_ca_topic_score_gemma":0.0028161604,"teacher_disagreement_score":0.0030552142,"about_ca_system_score_codex":0.00030197448,"about_ca_system_score_gemma":0.00033684762,"threshold_uncertainty_score":0.010220706},"labels":[],"label_agreement":null},{"id":"W7036182319","doi":"","title":"231 -As Canada Turns","year":2022,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kindness; Government (linguistics); World War II; Judaism","score_opus":0.003419523261296993,"score_gpt":0.16106957380625211,"score_spread":0.15765005054495512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036182319","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016760165,0.0019700183,0.0002124942,0.03176613,0.005726714,0.000059647016,0.0027264846,0.0003857826,0.95547664],"genre_scores_gemma":[0.0034222638,0.00031672735,0.000052105417,0.003862588,0.00010962287,0.000007624503,0.00029591518,0.000102129685,0.9918311],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845934,0.000053463395,0.000021355796,0.00019696305,0.00066551886,0.0006035021],"domain_scores_gemma":[0.997957,0.000074106705,0.00002819388,0.00010561494,0.0008539816,0.0009811254],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006057586,0.0006793937,0.00049539073,0.0010445591,0.012197994,0.011478381,0.0009911829,0.004224928,0.39747635],"category_scores_gemma":[0.002514559,0.00040374644,0.000365711,0.0016356005,0.002396381,0.003157995,0.0031390109,0.0036446725,0.17022148],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010611117,0.0000064302867,0.00042737953,0.000017531653,0.0000013721562,0.00006337381,0.00033868465,0.000015701924,0.000093166265,0.009019046,0.97605497,0.013951637],"study_design_scores_gemma":[8.279236e-7,0.0000010964293,0.00045620365,0.00001173933,4.8225985e-7,0.000010441733,0.0003201208,0.0000065515983,0.000019948488,0.00021272239,0.9989573,0.0000025493816],"about_ca_topic_score_codex":0.8841842,"about_ca_topic_score_gemma":0.9543066,"teacher_disagreement_score":0.39747635,"about_ca_system_score_codex":0.018359967,"about_ca_system_score_gemma":0.038554896,"threshold_uncertainty_score":0.85942686},"labels":[],"label_agreement":null},{"id":"W7038754591","doi":"","title":"Las representaciones sociales que tienen los migrantes “temporales” respecto a los habitantes de Saint-Rémi, Quebec","year":2015,"lang":"es","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Participant observation; Ethnography; Qualitative research; Meaning (existential)","score_opus":0.02170417902866503,"score_gpt":0.26807042154401817,"score_spread":0.24636624251535313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038754591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9308747,0.0008912008,0.00037124284,0.0055151675,0.000046246107,0.000019715324,0.00046920902,0.000019512263,0.061792936],"genre_scores_gemma":[0.981443,0.00066194625,0.00016855556,0.0002595095,0.0000060866355,0.000011954629,0.00010123142,0.0000059980175,0.01734177],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9995932,0.00012726293,0.00000926168,0.000049737886,0.00006688115,0.00015371981],"domain_scores_gemma":[0.9994248,0.000070466725,0.000096176445,0.00003320936,0.00018108117,0.00019431622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059303996,0.00016898305,0.00012601123,0.00074901804,0.010548607,0.0028618944,0.00048628738,0.00041365606,0.0064278375],"category_scores_gemma":[0.0007635665,0.00009358558,0.00012181373,0.0012366234,0.004202497,0.0008324375,0.0017199051,0.00066836143,0.00021634305],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000730818,0.00003828024,0.19099094,0.00009822721,0.000024718807,0.0008218058,0.7161185,0.00017420182,0.0015766254,0.02938532,0.019320857,0.04137745],"study_design_scores_gemma":[0.0000034918612,0.000019582569,0.25123507,0.0001642018,0.000022790522,0.0001760582,0.62727976,0.00016904583,0.00022867267,0.0006261121,0.12004841,0.00002673607],"about_ca_topic_score_codex":0.96765554,"about_ca_topic_score_gemma":0.9875365,"teacher_disagreement_score":0.03234446,"about_ca_system_score_codex":0.017449662,"about_ca_system_score_gemma":0.014844725,"threshold_uncertainty_score":0.12660676},"labels":[],"label_agreement":null},{"id":"W7094409855","doi":"","title":"9780472903795.pdf","year":2023,"lang":"en","type":"other","venue":"OAPEN (The OAPEN Foundation)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Free speech; Natural (archaeology); Democracy; Relevance (law); Expression (computer science); Freedom of expression","score_opus":0.011763842099816537,"score_gpt":0.23642949872647157,"score_spread":0.22466565662665502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7094409855","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002298468,0.000702892,0.00073002756,0.0006776826,0.00087273633,0.000020973615,0.0010315068,0.000726482,0.99500775],"genre_scores_gemma":[0.0011631146,0.00080578274,0.00046778333,0.00029730232,0.00018137989,0.000024899513,0.0009693893,0.00044573218,0.99564457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99943405,0.00006830134,0.000028084867,0.000095240226,0.00031318434,0.000061021143],"domain_scores_gemma":[0.99867874,0.00037480387,0.000086053136,0.0002165766,0.0003834708,0.00026027267],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00060165813,0.00066378055,0.00047786548,0.0020841397,0.0013318127,0.009357128,0.00074415415,0.0013994963,0.78773123],"category_scores_gemma":[0.0033546903,0.00040925623,0.00043494656,0.0037866484,0.00067032047,0.003994086,0.0026771596,0.0016134529,0.77295256],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018153774,0.000022680362,0.00022875247,0.000109200875,0.0000026150472,0.000046292742,0.00013872779,0.000054473716,0.00024405276,0.008205871,0.7656661,0.22526313],"study_design_scores_gemma":[0.0000012426101,0.0000043799064,0.00024369764,0.000046840796,7.261295e-7,0.000037232257,0.000052047937,0.000020648225,0.000053891887,0.00066079583,0.99887604,0.0000024794122],"about_ca_topic_score_codex":0.0015672,"about_ca_topic_score_gemma":0.002888651,"teacher_disagreement_score":0.21226877,"about_ca_system_score_codex":0.00065017265,"about_ca_system_score_gemma":0.00092379167,"threshold_uncertainty_score":0.30277562},"labels":[],"label_agreement":null},{"id":"W7102431493","doi":"","title":"AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages","year":2025,"lang":"","type":"article","venue":"arXiv (Cornell University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada First Research Excellence Fund","keywords":"Embedding; Benchmarking; Component (thermodynamics); Adaptation (eye); Languages of Africa; Cluster analysis","score_opus":0.03903952360992188,"score_gpt":0.19957265775649946,"score_spread":0.16053313414657758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7102431493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58624756,0.019751437,0.18562658,0.0046518976,0.0070355097,0.0022053176,0.062156867,0.088860475,0.043464407],"genre_scores_gemma":[0.53990144,0.0024846974,0.21680783,0.0022132776,0.0005859394,0.0014281548,0.20779426,0.0045840195,0.024200335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741346,0.0010808719,0.0001953304,0.0007771085,0.0002983408,0.00023489752],"domain_scores_gemma":[0.99681705,0.0013380762,0.00011415621,0.00094690686,0.00058197434,0.00020179489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004962017,0.0038491474,0.0011137043,0.00310812,0.0014875783,0.0021899305,0.0029775994,0.00271516,0.008268106],"category_scores_gemma":[0.013096029,0.0006579496,0.002045513,0.0018746064,0.0011171487,0.0047998913,0.00390684,0.003757605,0.008958159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017688473,0.001418965,0.011579583,0.0021235615,0.0010180873,0.0007790488,0.0009788632,0.10443346,0.01577384,0.004651711,0.20473458,0.65073943],"study_design_scores_gemma":[0.0007181301,0.0015908015,0.008233532,0.0005179506,0.00036994703,0.0012091727,0.0017478092,0.8227309,0.033450413,0.011130262,0.11797767,0.00032347938],"about_ca_topic_score_codex":0.013682301,"about_ca_topic_score_gemma":0.024295764,"teacher_disagreement_score":0.013682301,"about_ca_system_score_codex":0.0014613868,"about_ca_system_score_gemma":0.0013921367,"threshold_uncertainty_score":0.027659595},"labels":[],"label_agreement":null},{"id":"W7102547881","doi":"10.1108/jpbm-08-2024-5374","title":"Assessing hatred and anti-brand activism within online brand communities: the roles of ideological imbalance and online brand avoidance","year":2025,"lang":"en","type":"article","venue":"Journal of Product & Brand Management","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College","funders":"","keywords":"Hatred; Ideology; Context (archaeology); Brand community; Empirical research; Online and offline; Brand awareness","score_opus":0.01618310374116315,"score_gpt":0.27384600309083507,"score_spread":0.2576628993496719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7102547881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99581134,0.00006978745,0.0005987353,0.000048606533,0.0000047912117,0.000026850526,0.000018932411,0.0000025130203,0.0034184733],"genre_scores_gemma":[0.9991284,0.000044533772,0.0004235704,0.000025559257,0.0000065922404,0.000019412404,0.00002162238,0.0000011951133,0.00032911747],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843687,0.00055975106,0.00014737682,0.00016175346,0.0004962942,0.00019797611],"domain_scores_gemma":[0.9794372,0.006498035,0.010086838,0.000929427,0.0016356627,0.0014129088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031068663,0.00028019896,0.0002813637,0.0011505883,0.0010053411,0.0019713626,0.00027919843,0.00043437185,0.0024977042],"category_scores_gemma":[0.010305071,0.00021609607,0.00037863373,0.00055886176,0.0011400344,0.001456393,0.0017877342,0.0010222577,0.00019462914],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002057917,0.00041955768,0.9549855,0.0001592229,0.00009041688,0.00010874617,0.014415435,0.00011886355,0.0031516983,0.0007886312,0.00010019525,0.025456041],"study_design_scores_gemma":[0.000006856612,0.00020122966,0.97177935,0.00006872439,0.000050028535,0.00018593033,0.024151878,0.0007036038,0.0011184273,0.00080649194,0.0009054942,0.000022029513],"about_ca_topic_score_codex":0.0010474684,"about_ca_topic_score_gemma":0.002220502,"teacher_disagreement_score":0.0031068663,"about_ca_system_score_codex":0.00061373186,"about_ca_system_score_gemma":0.00062579836,"threshold_uncertainty_score":0.016430855},"labels":[],"label_agreement":null},{"id":"W7113337691","doi":"","title":"Moderating Tamil Content on Social Media","year":2025,"lang":"","type":"preprint","venue":"OSF Preprints (OSF Preprints)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tamil; Social media; Moderation; Indigenous; Diaspora; Content (measure theory)","score_opus":0.046363195416106624,"score_gpt":0.2696226809201968,"score_spread":0.2232594855040902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113337691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98894227,0.00018393222,0.0008234148,0.0006565462,0.00003539834,0.00011096708,0.00027930315,0.000022997965,0.0089451615],"genre_scores_gemma":[0.99845934,0.000078287405,0.00045796265,0.0001061585,0.000019500067,0.00010372871,0.00008342342,0.000027357284,0.00066429377],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937962,0.0045402423,0.00015877816,0.0004871758,0.00049351476,0.0005240751],"domain_scores_gemma":[0.9477634,0.043504283,0.0041158693,0.0016340965,0.0019754127,0.0010069974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059239324,0.00052544737,0.0003790744,0.0016213683,0.0021446457,0.0040712142,0.00055119605,0.00063995493,0.007128276],"category_scores_gemma":[0.041270163,0.00025120954,0.00025657823,0.0012523755,0.0019404673,0.0035498228,0.0053080316,0.0011555826,0.0006059807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010052805,0.0006178882,0.5957929,0.00092300633,0.00036725478,0.0005365025,0.32650256,0.0003020876,0.003968336,0.008632814,0.0024194117,0.058932003],"study_design_scores_gemma":[0.00007832699,0.00068331056,0.72829384,0.00048046486,0.0003270476,0.00021470718,0.24174356,0.0023211848,0.003884383,0.005234806,0.016662735,0.0000756063],"about_ca_topic_score_codex":0.0022358939,"about_ca_topic_score_gemma":0.00308322,"teacher_disagreement_score":0.007128276,"about_ca_system_score_codex":0.0009838481,"about_ca_system_score_gemma":0.000639002,"threshold_uncertainty_score":0.031329095},"labels":[],"label_agreement":null},{"id":"W7115169649","doi":"10.36939/ir.202512151530","title":"MMLTC: A novel Tolerance-Based Clustering Framework for Multimodal Sentiment and Harmful Meme Classification in Multilingual Settings","year":2025,"lang":"","type":"dissertation","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Cluster analysis; Class (philosophy); Wilcoxon signed-rank test; Benchmark (surveying); Rank (graph theory); Discriminative model; Feature (linguistics); Sentiment analysis","score_opus":0.02817526755323154,"score_gpt":0.30616588892660807,"score_spread":0.27799062137337655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115169649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034125313,0.00060931675,0.9554125,0.00033640378,0.00012753886,0.00030936382,0.001067234,0.0033982042,0.0046141543],"genre_scores_gemma":[0.4391561,0.00046039326,0.5425993,0.00053768937,0.0003131858,0.00088058144,0.0052351356,0.0008932428,0.009924432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989077,0.00023251622,0.00007000832,0.00032490192,0.0003118707,0.00015299901],"domain_scores_gemma":[0.9988238,0.00024465052,0.00017910697,0.00015536364,0.00048687897,0.0001102226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246026,0.0012401561,0.0010605999,0.003473728,0.0014111014,0.001955668,0.0020309745,0.00129241,0.0031325582],"category_scores_gemma":[0.0037019008,0.00031685774,0.0015124967,0.0022441456,0.0008916669,0.0022146895,0.0021637927,0.0017312091,0.0021087774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005971334,0.00041923972,0.011799099,0.00044491468,0.0003970031,0.00033622794,0.0017907778,0.06889501,0.026369976,0.022240845,0.029534219,0.83717555],"study_design_scores_gemma":[0.00003253171,0.00017429676,0.0073553724,0.00008824401,0.00010277891,0.00023577358,0.0009106501,0.9399935,0.00714949,0.029393254,0.014456856,0.000107295535],"about_ca_topic_score_codex":0.011671192,"about_ca_topic_score_gemma":0.019224795,"teacher_disagreement_score":0.011671192,"about_ca_system_score_codex":0.0014581146,"about_ca_system_score_gemma":0.0013218542,"threshold_uncertainty_score":0.023206532},"labels":[],"label_agreement":null},{"id":"W7116346131","doi":"10.5281/zenodo.17982436","title":"Attacks on Approximate Caches in Text-to-Image Diffusion Models","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code (set theory); Embedding; Scripting language; Upload; Artifact (error); Training (meteorology); Channel (broadcasting)","score_opus":0.03160548786039124,"score_gpt":0.2550456261858237,"score_spread":0.22344013832543247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116346131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24226725,0.003521711,0.71807706,0.004413514,0.0005481945,0.0003261929,0.0032032686,0.013239084,0.014403621],"genre_scores_gemma":[0.9215257,0.0007648058,0.062177792,0.00064936647,0.00010488342,0.00017005729,0.0026215918,0.00068755104,0.011298248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990164,0.00031641487,0.00005873843,0.00024146964,0.00023075499,0.00013613285],"domain_scores_gemma":[0.9974974,0.0011055311,0.00020159646,0.0008076557,0.00030264567,0.00008521506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015840997,0.0010892028,0.0009955842,0.00055082503,0.0005856622,0.0011916352,0.0011394585,0.0014495503,0.004062674],"category_scores_gemma":[0.010565144,0.00039772384,0.0008789549,0.0006688062,0.00089433946,0.0035575023,0.0015181387,0.0024460626,0.0013904255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008833714,0.00020809277,0.0047948896,0.00031590156,0.00016936952,0.00037719385,0.00038057534,0.7098486,0.012126953,0.094017774,0.0470214,0.12985593],"study_design_scores_gemma":[0.000023187738,0.00004276037,0.00024991037,0.000014124709,0.000010127355,0.00008377417,0.000024365403,0.982621,0.0030423212,0.011512953,0.0023635554,0.000011896829],"about_ca_topic_score_codex":0.009712622,"about_ca_topic_score_gemma":0.010243825,"teacher_disagreement_score":0.009712622,"about_ca_system_score_codex":0.0016681746,"about_ca_system_score_gemma":0.0010615011,"threshold_uncertainty_score":0.019312203},"labels":[],"label_agreement":null},{"id":"W7117733562","doi":"10.1109/wincom65874.2025.11313421","title":"Leveraging Transformer-Based Models for Cyberbullying Detection in the Moroccan Arabic Dialect","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Arabic; Offensive; Annotation; Social media; Modern Standard Arabic","score_opus":0.026621177702115285,"score_gpt":0.2591674399008343,"score_spread":0.23254626219871902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117733562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8793638,0.0042151464,0.09109739,0.001464429,0.00059497566,0.0003324577,0.005565886,0.0068714446,0.010494452],"genre_scores_gemma":[0.9670243,0.00038944618,0.020603767,0.00027820814,0.000086324864,0.000083655446,0.0066714836,0.00011935644,0.0047433036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995714,0.00013527357,0.000026496511,0.00014958419,0.00004465246,0.00007260622],"domain_scores_gemma":[0.99920124,0.00040558787,0.000045766436,0.000078701065,0.00021566982,0.00005303934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095752603,0.0014046156,0.0005679863,0.001412021,0.00058769307,0.0010068829,0.0007740917,0.00064208364,0.0018197707],"category_scores_gemma":[0.0022441489,0.00019287044,0.0007291096,0.00054745603,0.0003025139,0.0006785008,0.00080767594,0.0012277442,0.0017166776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027190577,0.0011892093,0.101423964,0.00048474822,0.0005361674,0.0011853002,0.0011256426,0.18274097,0.028739715,0.0023722728,0.03214662,0.6453364],"study_design_scores_gemma":[0.000029226054,0.00016719825,0.012389138,0.00004607183,0.000114491326,0.000166879,0.0004724119,0.9751418,0.0058501973,0.0015135803,0.0040747295,0.000034273286],"about_ca_topic_score_codex":0.03345648,"about_ca_topic_score_gemma":0.04140863,"teacher_disagreement_score":0.03345648,"about_ca_system_score_codex":0.0010500471,"about_ca_system_score_gemma":0.00077919563,"threshold_uncertainty_score":0.06652349},"labels":[],"label_agreement":null},{"id":"W7117874067","doi":"10.5220/0013918900004919","title":"MemeCheck: Automated Meme Analysis for Identifying Offensive Text and Visuals","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Offensive; Visualization; Feature (linguistics); Data visualization","score_opus":0.029712699581951274,"score_gpt":0.32924881610027934,"score_spread":0.29953611651832807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117874067","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23241664,0.003349157,0.31143934,0.000626523,0.0016295395,0.0016667929,0.09702328,0.31957185,0.032276902],"genre_scores_gemma":[0.47593474,0.0010540705,0.3795904,0.0005064472,0.00068239117,0.0019847783,0.07484936,0.007782794,0.057614975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955994,0.00005198085,0.000027211601,0.0001238737,0.00016586368,0.00007114854],"domain_scores_gemma":[0.9992349,0.00024477224,0.00009040351,0.00013850104,0.00020938159,0.000081955135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051580794,0.0013498391,0.00064474443,0.0044706077,0.00060683896,0.0010991582,0.00088050886,0.0010340307,0.013640659],"category_scores_gemma":[0.0022797058,0.0003242287,0.00054293795,0.001198907,0.00028540933,0.0013737398,0.0018424016,0.0004559659,0.00822872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011450632,0.00036275736,0.009615626,0.0011083774,0.00041058625,0.00097204256,0.0007492632,0.0016688926,0.1476388,0.0017248287,0.18278673,0.6518171],"study_design_scores_gemma":[0.00042586678,0.001251938,0.11315349,0.00035560317,0.00046886917,0.0034575835,0.0023516659,0.26029283,0.35277718,0.009267463,0.25573722,0.00046025225],"about_ca_topic_score_codex":0.0018716984,"about_ca_topic_score_gemma":0.0037185238,"teacher_disagreement_score":0.013640659,"about_ca_system_score_codex":0.00021043775,"about_ca_system_score_gemma":0.00032934072,"threshold_uncertainty_score":0.0456326},"labels":[],"label_agreement":null},{"id":"W7117883860","doi":"10.5220/0013908200004919","title":"Leveraging Deep Neural Networks for Real-Time Detection of Cyberbullying and Offensive Memes in Social Network","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Offensive; Artificial neural network; Social network (sociolinguistics); Social network analysis; Key (lock); The Internet","score_opus":0.011288494618406832,"score_gpt":0.2414606463409914,"score_spread":0.23017215172258457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117883860","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87091196,0.0021934137,0.11390881,0.0010676584,0.00044397675,0.00011494435,0.002502827,0.0022088224,0.006647684],"genre_scores_gemma":[0.9819157,0.00025692635,0.013474109,0.00013137,0.00011177823,0.000037752237,0.0011857928,0.00002467122,0.002861894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964404,0.000071101815,0.000013910661,0.00010393957,0.00007594945,0.000091096415],"domain_scores_gemma":[0.99926573,0.00033612736,0.0001194765,0.00005641582,0.00014641274,0.00007585542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051531557,0.00083047105,0.0004857724,0.0012622187,0.00031608835,0.0005458109,0.0006327532,0.00081049505,0.001129981],"category_scores_gemma":[0.0017950687,0.00023811337,0.00029755392,0.0006624976,0.00018141187,0.00084791996,0.0008031624,0.0009089975,0.0008225082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010398735,0.0018102133,0.10167926,0.00025040034,0.00043999637,0.000730305,0.0004561175,0.06581618,0.05485959,0.0018749165,0.018617963,0.7524251],"study_design_scores_gemma":[0.000011722683,0.00012285858,0.025802905,0.000017868206,0.000052267602,0.00013725956,0.00011337666,0.96387035,0.006592925,0.0017778715,0.0014781879,0.000022404338],"about_ca_topic_score_codex":0.0058436217,"about_ca_topic_score_gemma":0.012085642,"teacher_disagreement_score":0.0058436217,"about_ca_system_score_codex":0.00037755692,"about_ca_system_score_gemma":0.0003350718,"threshold_uncertainty_score":0.01161921},"labels":[],"label_agreement":null},{"id":"W7122777692","doi":"10.1145/3777867.3778259","title":"Overview of the HASOC Track at FIRE 2025: Abusive Meme Identification — Shadows Behind the Laughter","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Identification (biology); Social media; Key (lock); Laughter; Track (disk drive)","score_opus":0.026704592118101084,"score_gpt":0.2762430397133004,"score_spread":0.2495384475951993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7122777692","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20429681,0.007456926,0.04824562,0.0034580308,0.00660232,0.009892011,0.54770565,0.1011627,0.071179956],"genre_scores_gemma":[0.10218598,0.00077139324,0.053972363,0.0010938862,0.00083484204,0.004083872,0.8081793,0.0022422173,0.026636135],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99597627,0.0008986369,0.00031214606,0.001032243,0.0012598118,0.000520887],"domain_scores_gemma":[0.99347806,0.0009032448,0.00032737796,0.0015783936,0.0024592201,0.001253749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004402967,0.0022498372,0.0015082124,0.0043517333,0.0029427637,0.0034259472,0.0023521918,0.0020813884,0.012401771],"category_scores_gemma":[0.00629455,0.00047556567,0.0012788945,0.0020065487,0.0010580008,0.002148072,0.00442115,0.001865893,0.026217075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011864487,0.0010213362,0.024809524,0.0025860935,0.00030404687,0.00064602814,0.0016193108,0.0018083826,0.022250222,0.0011693798,0.7332194,0.20937982],"study_design_scores_gemma":[0.0004031519,0.0012184941,0.12234979,0.00070622226,0.00028934944,0.0017454638,0.0041835634,0.032559685,0.037872728,0.0025249447,0.79575324,0.00039333364],"about_ca_topic_score_codex":0.02943438,"about_ca_topic_score_gemma":0.056190036,"teacher_disagreement_score":0.02943438,"about_ca_system_score_codex":0.0014903232,"about_ca_system_score_gemma":0.0024897747,"threshold_uncertainty_score":0.0585261},"labels":[],"label_agreement":null},{"id":"W7124145569","doi":"10.1109/codit66093.2025.11321840","title":"Cyber-Troll Detection using Deep Learning and NLP: A Comparative Study","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Deep learning; Social media; Convolutional neural network; Moderation; Architecture; Big data","score_opus":0.02163450871137487,"score_gpt":0.2982843447318579,"score_spread":0.27664983602048304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124145569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9689846,0.0018978174,0.017898941,0.0008522819,0.000077238554,0.000090250396,0.0014511214,0.0008895569,0.007858197],"genre_scores_gemma":[0.9889081,0.00042414104,0.0069918903,0.00008796535,0.00003746937,0.000024668929,0.002049531,0.000038096357,0.001438159],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99814904,0.0009201725,0.00009651361,0.00026618468,0.00039130796,0.00017682144],"domain_scores_gemma":[0.9908461,0.006396751,0.0004879859,0.0007109032,0.0012984893,0.00025989083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029819643,0.0007549609,0.0003687295,0.0018012329,0.00043710184,0.00078994385,0.0007271213,0.00097386795,0.0012478301],"category_scores_gemma":[0.009659846,0.00019520229,0.00040282033,0.0009311386,0.0006237232,0.0027022003,0.0009687514,0.0011932162,0.0007421329],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015926998,0.0018991663,0.196354,0.001031367,0.00040421513,0.0008382412,0.0019707887,0.07811309,0.011159943,0.003266167,0.015121341,0.68824893],"study_design_scores_gemma":[0.000048166406,0.00065628893,0.09829427,0.00015446122,0.0001192105,0.0004266479,0.002312757,0.87593216,0.010994975,0.0033134404,0.007678414,0.00006923802],"about_ca_topic_score_codex":0.010574419,"about_ca_topic_score_gemma":0.017374907,"teacher_disagreement_score":0.010574419,"about_ca_system_score_codex":0.0009130421,"about_ca_system_score_gemma":0.0005792119,"threshold_uncertainty_score":0.021025777},"labels":[],"label_agreement":null},{"id":"W7125577906","doi":"10.1109/cascon66301.2025.00021","title":"Beyond Monolithic LLMs: Modular AI for Online Harassment Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"Alliance de recherche numérique du Canada","keywords":"Modular design; Harassment; Extensibility; Architecture; Blocking (statistics); Hamming distance","score_opus":0.010509937131299779,"score_gpt":0.27552641752092283,"score_spread":0.26501648038962305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125577906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08554363,0.0013646156,0.74489474,0.0024811558,0.00055760157,0.0007581024,0.0025446862,0.14754348,0.01431208],"genre_scores_gemma":[0.50151825,0.00044190855,0.47277796,0.0020495334,0.00018649038,0.0005191159,0.0063267737,0.0018021401,0.014377913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986118,0.0003169262,0.000078889476,0.0005997939,0.00026679592,0.00012578689],"domain_scores_gemma":[0.9969529,0.0010286975,0.00020203748,0.0010470262,0.0005304381,0.00023897117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025772501,0.0016841962,0.00074420945,0.0012762722,0.000802714,0.0019521352,0.0033130588,0.0015964061,0.00650203],"category_scores_gemma":[0.008860858,0.0004942648,0.0011185963,0.0007962271,0.0010248222,0.0057704197,0.0039272513,0.0032001825,0.0053302483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000866606,0.00062935613,0.015834462,0.00072823674,0.0003462253,0.00027698683,0.0013100356,0.058673944,0.035379652,0.011235955,0.047645293,0.8270732],"study_design_scores_gemma":[0.00004999098,0.00033776934,0.0026931092,0.00010480434,0.000113040616,0.00016332928,0.00046965227,0.907007,0.0253374,0.026904535,0.03675003,0.00006939891],"about_ca_topic_score_codex":0.0070044324,"about_ca_topic_score_gemma":0.014178438,"teacher_disagreement_score":0.0070044324,"about_ca_system_score_codex":0.0013416895,"about_ca_system_score_gemma":0.0013915409,"threshold_uncertainty_score":0.021751463},"labels":[],"label_agreement":null},{"id":"W7125580424","doi":"10.1109/cascon66301.2025.00121","title":"Evaluating Toxicity Understanding of LLM Agents","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Toxicity; Perception; Trustworthiness; Order (exchange); Action (physics); Risk assessment","score_opus":0.1342825005152352,"score_gpt":0.3670419346009326,"score_spread":0.2327594340856974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125580424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82341087,0.0010493482,0.12879947,0.0027900971,0.00007131546,0.0007106328,0.0005044391,0.0013745701,0.041289147],"genre_scores_gemma":[0.9615948,0.00033911783,0.034513824,0.0003797809,0.000030991894,0.00014383301,0.0005560569,0.00010233486,0.002339315],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98501855,0.0076844967,0.0010506503,0.0012189236,0.00458448,0.00044289106],"domain_scores_gemma":[0.9456544,0.029935125,0.011682311,0.0038677554,0.007656951,0.0012034787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013315102,0.0010723145,0.0006088388,0.0023394781,0.0012736807,0.0043624816,0.001084165,0.0023183587,0.0031173087],"category_scores_gemma":[0.089400336,0.0003768178,0.00066483277,0.0005760761,0.0024802715,0.0072743283,0.0050933072,0.0017095422,0.0009153582],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022251317,0.0011528182,0.32097107,0.0021479141,0.00051581097,0.0017862077,0.11345012,0.045879573,0.04876775,0.049857512,0.0074749654,0.40577117],"study_design_scores_gemma":[0.00014371041,0.005812382,0.30209306,0.0016666098,0.00087866845,0.0021898139,0.07713888,0.2675637,0.1178803,0.12344878,0.10047793,0.0007061714],"about_ca_topic_score_codex":0.0034794705,"about_ca_topic_score_gemma":0.0028050747,"teacher_disagreement_score":0.013315102,"about_ca_system_score_codex":0.003031352,"about_ca_system_score_gemma":0.0015265187,"threshold_uncertainty_score":0.07041782},"labels":[],"label_agreement":null},{"id":"W7125604742","doi":"10.1109/cascon66301.2025.00020","title":"Enhancing Machine Learning in Abusive Language Detection with Dataset Integration","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Vector Institute","funders":"","keywords":"Generalizability theory; Benchmark (surveying); Sampling (signal processing); Limiting; Language model; Co-occurrence; Core (optical fiber); Training set","score_opus":0.0049760488255016725,"score_gpt":0.23536545029513029,"score_spread":0.2303894014696286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125604742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6889797,0.0050813556,0.27547702,0.0042614536,0.00074482744,0.0011005201,0.00845201,0.008309032,0.007593998],"genre_scores_gemma":[0.84233224,0.00049466983,0.13186277,0.0015377065,0.00023453339,0.00069507945,0.021067383,0.00027723453,0.0014983398],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9898191,0.006318725,0.00062096043,0.0020557237,0.00084054883,0.00034485993],"domain_scores_gemma":[0.98210657,0.00922516,0.0011972232,0.005149763,0.0019134163,0.00040790805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014033496,0.0024933657,0.0014005043,0.0029379632,0.0011524842,0.0027891158,0.0022738327,0.0024120158,0.00091509306],"category_scores_gemma":[0.038194507,0.0005467565,0.001991355,0.0025271778,0.0011198294,0.004393481,0.004813356,0.0038254284,0.0010967728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001232152,0.00290593,0.25221166,0.0010992283,0.0022874454,0.0004980568,0.001398504,0.24486487,0.013648715,0.003759681,0.025737057,0.45035666],"study_design_scores_gemma":[0.00015668222,0.00084275374,0.03500247,0.00022771054,0.00049429527,0.0003960786,0.0007288541,0.9246467,0.010062897,0.014571264,0.01270033,0.000170015],"about_ca_topic_score_codex":0.00531877,"about_ca_topic_score_gemma":0.009545855,"teacher_disagreement_score":0.014033496,"about_ca_system_score_codex":0.0008540306,"about_ca_system_score_gemma":0.0013233804,"threshold_uncertainty_score":0.07421708},"labels":[],"label_agreement":null},{"id":"W7125606400","doi":"10.1109/icsai68704.2025.11345876","title":"Toxic Language and Threat Detection in Social Media","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Dalhousie University","funders":"","keywords":"Social media; The Internet; Action (physics); Misinformation","score_opus":0.009253955830846232,"score_gpt":0.24619386054912257,"score_spread":0.23693990471827633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125606400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878781,0.00027668857,0.0033802777,0.00049984996,0.000049089005,0.000032065982,0.00024777406,0.000061694845,0.0075745033],"genre_scores_gemma":[0.99746394,0.00010711281,0.0007417673,0.00009184335,0.000052186486,0.000011911051,0.0001256254,0.000009817836,0.0013957667],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982346,0.0008221709,0.00007144423,0.00015406027,0.0005187756,0.00019884152],"domain_scores_gemma":[0.9841287,0.010623998,0.0026844363,0.00046394044,0.001360552,0.00073836674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017559739,0.0004563209,0.0002745947,0.0026668934,0.0007472784,0.0022526819,0.0003445131,0.0012042744,0.0028475542],"category_scores_gemma":[0.016664712,0.00027390363,0.00028575567,0.0008247265,0.00077135203,0.0019637041,0.0011351617,0.0011154419,0.0010118315],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012156034,0.0017159425,0.8246247,0.00027714012,0.00022443832,0.0012688938,0.0047746827,0.0049236496,0.025197987,0.006262062,0.0039209076,0.12559395],"study_design_scores_gemma":[0.00003460867,0.00097266416,0.84450674,0.0001519435,0.00017816182,0.0022097859,0.009319076,0.11423309,0.012344281,0.010734061,0.005199124,0.0001164096],"about_ca_topic_score_codex":0.0035976376,"about_ca_topic_score_gemma":0.0052328464,"teacher_disagreement_score":0.0035976376,"about_ca_system_score_codex":0.00054951245,"about_ca_system_score_gemma":0.0004268914,"threshold_uncertainty_score":0.009526014},"labels":[],"label_agreement":null},{"id":"W7126432429","doi":"10.18653/v1/2024.dravidianlangtech-1.15","title":"Tewodros@DravidianLangTech 2024: Hate Speech Recognition in Telugu Codemixed Text","year":2024,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Atomic Energy of Canada Limited; Consejo Nacional de Ciencia y Tecnología; Instituto Politécnico Nacional","keywords":"Telugu; Feature (linguistics); Action (physics)","score_opus":0.0220542216814919,"score_gpt":0.25407164446554964,"score_spread":0.23201742278405774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126432429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92881626,0.00035858445,0.04788586,0.0008831476,0.0005213563,0.000361476,0.0014937273,0.002188306,0.017491248],"genre_scores_gemma":[0.9281478,0.00016284076,0.0409312,0.0003128817,0.00013156384,0.00032505923,0.0045938515,0.00053274434,0.024862124],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865377,0.0006565505,0.000059175374,0.00023745462,0.00022757327,0.00016540715],"domain_scores_gemma":[0.99813193,0.0006697945,0.00007219091,0.00024182459,0.0005622553,0.00032208473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015317745,0.00049297727,0.00037630377,0.0006094798,0.0008536193,0.0013294397,0.0004759491,0.0010590778,0.0060519464],"category_scores_gemma":[0.003944989,0.00014126916,0.00019167074,0.0002852396,0.00054257346,0.001137825,0.0017143984,0.00078043254,0.0055215484],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013800277,0.0011843058,0.033400994,0.0007009967,0.00007204977,0.0024121387,0.027066475,0.0032563643,0.29575625,0.003357551,0.05272094,0.5786918],"study_design_scores_gemma":[0.00014937851,0.0028979566,0.18659452,0.00033241572,0.00011705828,0.0062218593,0.050471164,0.12218622,0.41627234,0.005187875,0.20912784,0.00044137577],"about_ca_topic_score_codex":0.003986467,"about_ca_topic_score_gemma":0.006815654,"teacher_disagreement_score":0.0060519464,"about_ca_system_score_codex":0.00026467102,"about_ca_system_score_gemma":0.00040156598,"threshold_uncertainty_score":0.02024579},"labels":[],"label_agreement":null},{"id":"W7127405297","doi":"10.82187/jfrl.2025.1208270","title":"A Comparative Study of Mechanisms for Preventing Transphobic Hate Crimes: Case Study of Iran and Canada","year":2025,"lang":"en","type":"article","venue":"Open MIND","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Perspective (graphical); Blame; Agency (philosophy)","score_opus":0.05426540015169933,"score_gpt":0.327352375783161,"score_spread":0.27308697563146167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127405297","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9697707,0.001559079,0.00043512782,0.0007513629,0.00002484435,0.00023952479,0.00015651385,0.00001337452,0.027049359],"genre_scores_gemma":[0.98801893,0.002730036,0.00089476065,0.00012596366,0.0000054674238,0.00004136558,0.00008591565,0.000006147096,0.00809139],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9987784,0.00020105651,0.000036484293,0.00007913066,0.0003559452,0.0005489955],"domain_scores_gemma":[0.9987237,0.00020484757,0.00017766042,0.000056153076,0.0006085511,0.00022916014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083582866,0.00048208126,0.00036783528,0.0031969943,0.011372554,0.0017601781,0.002056749,0.0011783099,0.0033051448],"category_scores_gemma":[0.0029261683,0.000378865,0.0005203357,0.0030748888,0.0027667603,0.0006552584,0.001453813,0.0010073152,0.00021179528],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037922786,0.001112797,0.5445652,0.0011793135,0.00023077817,0.02330158,0.21034554,0.0021339615,0.0023354697,0.033262134,0.011704876,0.16944915],"study_design_scores_gemma":[0.000049996434,0.0003463553,0.372171,0.000515906,0.000247007,0.006734554,0.5576113,0.0015200328,0.0021961234,0.0011344192,0.057381324,0.000091982074],"about_ca_topic_score_codex":0.9563893,"about_ca_topic_score_gemma":0.9814674,"teacher_disagreement_score":0.043610692,"about_ca_system_score_codex":0.037360553,"about_ca_system_score_gemma":0.059028354,"threshold_uncertainty_score":0.27107102},"labels":[],"label_agreement":null},{"id":"W7132863002","doi":"","title":"Aseistakieltäytymisen puitteet Suomessa 1961–69 : Tilastollinen tutkielma asevelvollisten tutkijatoimikunnasta ja sen päätöksistä","year":2022,"lang":"fi","type":"dissertation","venue":"Trepo - Institutional Repository of Tampere University","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Life style","score_opus":0.010932882883666199,"score_gpt":0.21229272859517653,"score_spread":0.20135984571151033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132863002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8498245,0.0037237206,0.0007548931,0.0016443392,0.00028469556,0.00009079782,0.0019582848,0.00004917782,0.1416696],"genre_scores_gemma":[0.84741604,0.0025620242,0.0008034041,0.0003276704,0.000067611836,0.00008949249,0.002601595,0.000048063364,0.14608413],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995908,0.000048929403,0.000021795275,0.000092817696,0.00009201874,0.00015370581],"domain_scores_gemma":[0.9996661,0.00006654081,0.00007304372,0.000019708388,0.000090777714,0.00008377347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047924835,0.00027285557,0.00023009302,0.0009467621,0.0022776134,0.0019093668,0.0003720914,0.00048286904,0.016138315],"category_scores_gemma":[0.0010227272,0.00022496523,0.00020910529,0.0016072997,0.00077994075,0.0009984628,0.0016108265,0.0008884417,0.001853032],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011989437,0.0002922264,0.40329403,0.00082841393,0.000102870654,0.005441681,0.14894179,0.00069149304,0.013766016,0.035709392,0.03210472,0.35762843],"study_design_scores_gemma":[0.000010647805,0.00017848077,0.40215486,0.0003286261,0.00002549705,0.0006346289,0.042602256,0.00010667315,0.003104921,0.0015673038,0.54925203,0.000034074375],"about_ca_topic_score_codex":0.07766997,"about_ca_topic_score_gemma":0.21823075,"teacher_disagreement_score":0.07766997,"about_ca_system_score_codex":0.00297403,"about_ca_system_score_gemma":0.0034095417,"threshold_uncertainty_score":0.15443575},"labels":[],"label_agreement":null},{"id":"W7132983545","doi":"","title":"Pious: Why Meta’s Business Model Drives Intolerance","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Social media; Identity (music); Social identity theory; Politics; Blueprint; Identity formation; Blame; Cognitive reframing; Social identity approach","score_opus":0.0467270570996186,"score_gpt":0.3207627113661598,"score_spread":0.2740356542665412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132983545","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3172535,0.00068304304,0.115856774,0.054581,0.00042637158,0.00014655742,0.00017620566,0.000711065,0.5101655],"genre_scores_gemma":[0.97904646,0.00012873863,0.009180617,0.0015443236,0.00005934468,0.00008062175,0.000036353766,0.00027363247,0.0096501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9940912,0.003486604,0.000120639656,0.0007635991,0.0010414424,0.0004965267],"domain_scores_gemma":[0.9908914,0.00471055,0.0008252794,0.0018975119,0.000867782,0.00080748595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006184013,0.00050426216,0.00038648062,0.0016316295,0.0037830274,0.0146416165,0.0012502903,0.0026852232,0.009530486],"category_scores_gemma":[0.022094764,0.00042822582,0.00050100853,0.0012990902,0.018155329,0.015067065,0.0055588246,0.004489233,0.0018175868],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044246965,0.000040981347,0.0038448703,0.000039927472,0.000009754954,0.00013893195,0.020691851,0.00033310248,0.0005742539,0.95726806,0.0029537205,0.014060404],"study_design_scores_gemma":[0.00003411178,0.00009381783,0.0038530033,0.00020976103,0.000027755543,0.00045354752,0.035915945,0.008896358,0.0019646129,0.8217778,0.12671977,0.000053371277],"about_ca_topic_score_codex":0.0014625869,"about_ca_topic_score_gemma":0.0013204148,"teacher_disagreement_score":0.0146416165,"about_ca_system_score_codex":0.0035286138,"about_ca_system_score_gemma":0.0016327346,"threshold_uncertainty_score":0.03270459},"labels":[],"label_agreement":null},{"id":"W7132992361","doi":"","title":"How equality rights may expand the scope of expression for homosexual pornographic speech: a modified freedom of expression test","year":2003,"lang":"","type":"dissertation","venue":"TSpace","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Expression (computer science); Scope (computer science); Charter; Context (archaeology); Freedom of expression; Diversity (politics)","score_opus":0.03724699794321648,"score_gpt":0.3152789177928782,"score_spread":0.2780319198496617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132992361","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18231629,0.0009609834,0.15382361,0.034728207,0.00047813234,0.0002632316,0.00015674821,0.00014142516,0.6271314],"genre_scores_gemma":[0.9760275,0.00018507884,0.012908879,0.002197835,0.00013831246,0.00015075225,0.000030882027,0.000035743582,0.0083249705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9862619,0.0062625953,0.00063222804,0.0016047058,0.0035239616,0.0017146352],"domain_scores_gemma":[0.9702252,0.022384187,0.0010636897,0.0029846684,0.002554895,0.0007872182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014572289,0.00037936415,0.00079594034,0.0014940383,0.0039230306,0.0103823505,0.0020084383,0.0049611926,0.0067750015],"category_scores_gemma":[0.032117985,0.00032025858,0.0012844164,0.0009287467,0.039530937,0.014098699,0.006298563,0.005501437,0.00060982985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003560364,0.000024291756,0.0008222299,0.00001687584,0.0000055994096,0.00014535133,0.0026328359,0.00026668044,0.0006071329,0.9844536,0.0006697409,0.01032002],"study_design_scores_gemma":[0.00006235316,0.00012713361,0.0036640472,0.00010917782,0.00004760732,0.00031911652,0.004078599,0.004218113,0.0030418138,0.9514073,0.032822043,0.000102600236],"about_ca_topic_score_codex":0.008674075,"about_ca_topic_score_gemma":0.008542538,"teacher_disagreement_score":0.014572289,"about_ca_system_score_codex":0.0044225333,"about_ca_system_score_gemma":0.0045521213,"threshold_uncertainty_score":0.07706654},"labels":[],"label_agreement":null},{"id":"W7152417213","doi":"10.1109/icecet63943.2025.11472543","title":"Multi-Modal CSNNs for Integrated Toxicity Detection Across Text, Audio, and Visual Modalities","year":2025,"lang":"","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Modalities; Visualization; Toxicity; Modality (human–computer interaction); Identification (biology)","score_opus":0.015129651912685817,"score_gpt":0.30366751259073804,"score_spread":0.2885378606780522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7152417213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1626952,0.0009515995,0.82758176,0.000578108,0.0002284902,0.0001002792,0.000740197,0.002475522,0.0046487516],"genre_scores_gemma":[0.892938,0.00047835443,0.10051863,0.00033095703,0.00008099885,0.00009029148,0.00081232464,0.000079275,0.0046711676],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977463,0.00003244353,0.0000128639695,0.000078496545,0.000073783805,0.000027824384],"domain_scores_gemma":[0.9995666,0.00012218468,0.00007692205,0.00004683721,0.00016053567,0.000026848651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004726287,0.00076049735,0.0004242688,0.00077869726,0.00021863758,0.0004403604,0.0007866364,0.000751097,0.0011735695],"category_scores_gemma":[0.0014351673,0.00020158419,0.00050354406,0.00061922456,0.0003915236,0.0010101666,0.0007834275,0.0008282981,0.00048309282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005111468,0.00045300624,0.012604315,0.00029587973,0.00020728787,0.000372038,0.0001534945,0.23553437,0.13978422,0.0035987052,0.0047940398,0.6016915],"study_design_scores_gemma":[0.000004695501,0.000087492284,0.003409362,0.000014953029,0.000034369943,0.00010656379,0.000035757635,0.9737378,0.0177601,0.0037164583,0.001071142,0.000021299307],"about_ca_topic_score_codex":0.002701879,"about_ca_topic_score_gemma":0.0068329354,"teacher_disagreement_score":0.002701879,"about_ca_system_score_codex":0.00059022446,"about_ca_system_score_gemma":0.0004609331,"threshold_uncertainty_score":0.005372286},"labels":[],"label_agreement":null},{"id":"W7160242492","doi":"10.69513/ilcr.v2.i1.a5","title":"Detecting Censorship and Self-Censorship: NLP Analysis of Political Discourse in Iraqi Social Media and Blogs","year":2024,"lang":"","type":"article","venue":"Iraqi Literary and Cultural Review (ILCR)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Niagara","funders":"","keywords":"Politics; Censorship; Taboo; Social media; Harm; Reading (process); Criticism; Critical discourse analysis; Key (lock)","score_opus":0.024667357913764957,"score_gpt":0.3040283281900425,"score_spread":0.27936097027627754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160242492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91991436,0.001602919,0.049847685,0.0044449586,0.00009751478,0.00045667766,0.006679352,0.0004487622,0.016507804],"genre_scores_gemma":[0.95275915,0.00062119926,0.038854737,0.00028326616,0.000104958315,0.00052227575,0.0042502386,0.00009607903,0.0025080105],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99694616,0.0016263269,0.00020194713,0.0004795325,0.00057652185,0.00016951072],"domain_scores_gemma":[0.9774125,0.01853801,0.0018106401,0.0008625428,0.0012208506,0.00015542851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035939815,0.00038213521,0.00028411974,0.0053218137,0.0015752375,0.0029229634,0.00055201835,0.0007133812,0.0012321626],"category_scores_gemma":[0.016422145,0.00023147489,0.0004539488,0.0052512614,0.0019847504,0.003767874,0.0025412512,0.0011054846,0.0006555361],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031254973,0.0003182107,0.18069182,0.002555655,0.00014742643,0.0028420347,0.3400237,0.00458384,0.025461785,0.02419764,0.013504043,0.40536132],"study_design_scores_gemma":[0.000051270963,0.00012409591,0.38696897,0.0012780866,0.0001238611,0.0017128226,0.31237435,0.077642895,0.015667072,0.03245452,0.17143409,0.00016796407],"about_ca_topic_score_codex":0.0077519785,"about_ca_topic_score_gemma":0.00859175,"teacher_disagreement_score":0.0077519785,"about_ca_system_score_codex":0.0012936969,"about_ca_system_score_gemma":0.0014233894,"threshold_uncertainty_score":0.019007027},"labels":[],"label_agreement":null},{"id":"W7161484934","doi":"10.14288/ce.v17i2.187246","title":"Anti-Wokeness, Hate Speech, and Rights Discourse Uphold the Status Quo","year":2025,"lang":"en","type":"article","venue":"Érudit (Université de Montréal)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Supreme court; Oppression; Rhetoric; Human rights; Charter; Racism; Multiculturalism; Jurisdiction; Civil rights","score_opus":0.0033373685387437842,"score_gpt":0.17275833885983516,"score_spread":0.16942097032109138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161484934","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30773377,0.015950259,0.010638431,0.14365241,0.0009644111,0.00014058789,0.00021953076,0.00007377142,0.5206269],"genre_scores_gemma":[0.9809838,0.0032463816,0.0015188485,0.004156984,0.00010292568,0.00003242069,0.000032629658,0.000018653642,0.009907315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938305,0.0019117224,0.00014307935,0.0004598092,0.0018500744,0.0018047872],"domain_scores_gemma":[0.98401326,0.0072087264,0.003100108,0.0005689561,0.0032647345,0.0018441511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059726895,0.00059288903,0.0006049126,0.0038080362,0.018821757,0.020601626,0.0015922337,0.0033191782,0.0059712604],"category_scores_gemma":[0.012305577,0.00029419854,0.00038372647,0.0035272227,0.057269536,0.010572235,0.0064357207,0.0055699754,0.00028477467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017961962,0.00004368557,0.019233715,0.00012779579,0.000015830574,0.0003044425,0.15670323,0.00015387502,0.00030897354,0.78670126,0.005764596,0.030624595],"study_design_scores_gemma":[0.000013074952,0.00003953086,0.07318248,0.0019867814,0.00006193715,0.00042402884,0.53305256,0.0009731865,0.0015431278,0.18325974,0.20530735,0.00015618114],"about_ca_topic_score_codex":0.8132018,"about_ca_topic_score_gemma":0.9058971,"teacher_disagreement_score":0.8132018,"about_ca_system_score_codex":0.041722625,"about_ca_system_score_gemma":0.06375429,"threshold_uncertainty_score":0.37579656},"labels":[],"label_agreement":null},{"id":"W7162744427","doi":"","title":"Eliminating Subjectivity from Hate Speech Regulation : Exploringa Modified Definition of Hatred in Canada","year":2019,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Subjectivity; Perspective (graphical); Derogation; Focus (optics)","score_opus":0.02501523676650405,"score_gpt":0.21569059107992325,"score_spread":0.1906753543134192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162744427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85852855,0.0018868588,0.020263214,0.014519841,0.00012961648,0.0003827733,0.0062809484,0.00023794238,0.09777027],"genre_scores_gemma":[0.98762983,0.00056417723,0.0049294415,0.0006063481,0.000017354438,0.000050212704,0.0011053993,0.00004774189,0.0050494736],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9914766,0.0012526099,0.0005595416,0.0010209022,0.0039721266,0.001718189],"domain_scores_gemma":[0.97238344,0.00501064,0.003086734,0.0019392011,0.015610961,0.001969063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052469145,0.0002916046,0.00048265324,0.0058888965,0.008778341,0.009954829,0.0027692756,0.0011459853,0.0027222368],"category_scores_gemma":[0.024311395,0.00033643842,0.00047676303,0.013235179,0.006858384,0.0035581598,0.003756297,0.0017200295,0.00022228662],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024283913,0.00017466358,0.6317722,0.0004128592,0.000111363115,0.0006511187,0.06605198,0.0032373932,0.0031464568,0.13283549,0.024036605,0.13732699],"study_design_scores_gemma":[0.000029392035,0.000058982216,0.74671817,0.00056769076,0.00015674175,0.00037969887,0.09202252,0.013799031,0.0042717718,0.010924363,0.13085915,0.00021248862],"about_ca_topic_score_codex":0.9926514,"about_ca_topic_score_gemma":0.99502265,"teacher_disagreement_score":0.0730095,"about_ca_system_score_codex":0.0730095,"about_ca_system_score_gemma":0.10838107,"threshold_uncertainty_score":0.52972347},"labels":[],"label_agreement":null},{"id":"W7165709219","doi":"","title":"Free Speech and the Protection of Human Dignity in Canada, Germany, and the United States: The Moral Legitimation of Competing Discourses","year":2020,"lang":"en","type":"article","venue":"PhilPapers (PhilPapers Foundation)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Argument (complex analysis); Legitimation; Dignity; Free speech; Politics; Human rights; Supreme court; Racism","score_opus":0.019309006153824002,"score_gpt":0.2223625776021607,"score_spread":0.2030535714483367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7165709219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5931222,0.014978112,0.00291339,0.06887128,0.0005480047,0.00006286204,0.00012511903,0.00003978063,0.3193392],"genre_scores_gemma":[0.99346787,0.0013273404,0.00027400034,0.0013230593,0.00002387435,0.000008567948,0.000016335296,0.000008299304,0.0035505774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9904925,0.002645398,0.00019777444,0.000540776,0.0033441666,0.00277935],"domain_scores_gemma":[0.9907437,0.004535202,0.0007513152,0.0003191697,0.0022604358,0.0013902594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008465524,0.0004989835,0.00075504184,0.0034639107,0.031824376,0.019586245,0.0016121692,0.0041761976,0.0019512548],"category_scores_gemma":[0.016447559,0.0003620716,0.00029422072,0.0035483676,0.0697171,0.004930203,0.0059858914,0.005894862,0.00013421944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075861775,0.000051649677,0.0073629958,0.00006323346,0.000027980499,0.00041992852,0.21659465,0.0004200397,0.0002458531,0.7367566,0.008812037,0.029169103],"study_design_scores_gemma":[0.000064308595,0.00005257472,0.052880235,0.0013626562,0.00011778363,0.00032329475,0.51677465,0.002518623,0.0014080658,0.19016075,0.23401675,0.00032032578],"about_ca_topic_score_codex":0.9586528,"about_ca_topic_score_gemma":0.9711962,"teacher_disagreement_score":0.11604076,"about_ca_system_score_codex":0.11604076,"about_ca_system_score_gemma":0.12275319,"threshold_uncertainty_score":0.84193856},"labels":[],"label_agreement":null},{"id":"W75226891","doi":"","title":"Hate Speech, Sedition and the War on Terror","year":2007,"lang":"en","type":"book-chapter","venue":"ANU Open Research (Australian National University)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hatred; Sedition; Context (archaeology); Law; Politics; Political science; Variety (cybernetics); Incitement; Racism; Sociology; History; Computer science","score_opus":0.15188335585630366,"score_gpt":0.34298382890117185,"score_spread":0.19110047304486819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75226891","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076444276,0.04789209,0.0025146133,0.06011557,0.002653652,0.000036493107,0.000028148504,0.000041091665,0.810274],"genre_scores_gemma":[0.7228332,0.018753514,0.0006986886,0.009136617,0.00087810616,0.000041303472,0.000022581124,0.000030856143,0.24760519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9987363,0.0006172015,0.000033654425,0.00008204355,0.00030352024,0.00022728364],"domain_scores_gemma":[0.99921954,0.0003867557,0.00012425243,0.0000619377,0.00009787747,0.00010966327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011702396,0.00037156977,0.00025745892,0.0005388434,0.005593215,0.004856569,0.0005988354,0.003333412,0.0027254634],"category_scores_gemma":[0.0023303363,0.00023717033,0.00026798798,0.0003876653,0.019876825,0.0037602014,0.0027215818,0.0036493924,0.000687157],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002246219,0.000028615721,0.00084938575,0.00014503062,0.000008267604,0.00043138393,0.06674905,0.00034456633,0.00045502128,0.85360575,0.04218148,0.035179038],"study_design_scores_gemma":[0.0000064658157,0.00006177805,0.007330014,0.0005888444,0.000010969653,0.000529652,0.036295503,0.0004172045,0.00063638727,0.13139586,0.8226975,0.000029831097],"about_ca_topic_score_codex":0.01774056,"about_ca_topic_score_gemma":0.023639437,"teacher_disagreement_score":0.01774056,"about_ca_system_score_codex":0.0049470514,"about_ca_system_score_gemma":0.0029015888,"threshold_uncertainty_score":0.03589356},"labels":[],"label_agreement":null},{"id":"W78450645","doi":"","title":"Technical Evaluation Report. 54. Best Practices in Synchronous Conferencing Moderation.","year":2006,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Moderation; Distance education; Computer science; Computer-mediated communication; Teleconference; Multimedia; Psychology; World Wide Web; The Internet; Mathematics education","score_opus":0.13678623470394438,"score_gpt":0.4621895447043518,"score_spread":0.32540331000040745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W78450645","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16886799,0.025408339,0.069172375,0.026975654,0.015561051,0.16419125,0.1565206,0.017986447,0.3553163],"genre_scores_gemma":[0.49071178,0.0063104844,0.13477686,0.00789665,0.0020986209,0.20686899,0.04252584,0.0035035221,0.105307244],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.8684816,0.10019443,0.008006581,0.003047055,0.018269572,0.002000693],"domain_scores_gemma":[0.6705048,0.18481456,0.013527285,0.03817995,0.08362712,0.0093462765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09879985,0.0012834112,0.0013002204,0.0026379041,0.002005342,0.003483469,0.0026831157,0.0028763781,0.059364256],"category_scores_gemma":[0.2827532,0.0008514556,0.0015779323,0.0028121541,0.0011918118,0.002815396,0.002946566,0.0020892522,0.0188517],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02840042,0.007158006,0.006843623,0.010778138,0.0006024166,0.000118131764,0.0021412056,0.00078566826,0.0019805257,0.0021213235,0.3106893,0.62838125],"study_design_scores_gemma":[0.06678466,0.03419076,0.1413293,0.011070762,0.005261774,0.0004332444,0.0047169947,0.0047291727,0.014449561,0.0064542703,0.7101456,0.00043396972],"about_ca_topic_score_codex":0.0067847404,"about_ca_topic_score_gemma":0.0082011875,"teacher_disagreement_score":0.09879985,"about_ca_system_score_codex":0.0028738559,"about_ca_system_score_gemma":0.0070507405,"threshold_uncertainty_score":0.5225098},"labels":[],"label_agreement":null},{"id":"W78689030","doi":"","title":"They Promise They Won't Be Evil . . . But Should Google Still Be Your Friend after R v Ward?","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Internet privacy; Appeal; Warrant; The Internet; Charter; Jurisprudence; Meaning (existential); Order (exchange); Service provider; Service (business); Right to privacy; Personally identifiable information; Business; Public relations; Political science; Law; Computer science; World Wide Web; Psychology","score_opus":0.01743472263450214,"score_gpt":0.24050821013925572,"score_spread":0.2230734875047536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W78689030","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037212495,0.0014133925,0.004804116,0.22069627,0.0025024433,0.00010053905,0.0003583164,0.0002945469,0.73261786],"genre_scores_gemma":[0.43487832,0.0014930773,0.0026804758,0.07594396,0.0003786831,0.00005720052,0.00019055315,0.0002140477,0.48416373],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99780804,0.00044356004,0.00005987061,0.00018279169,0.00087876245,0.0006270325],"domain_scores_gemma":[0.9975764,0.0005608194,0.00028701423,0.0002113501,0.000937221,0.00042731527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001877781,0.0003754606,0.00026033432,0.00052789587,0.01014671,0.006106293,0.00080007524,0.0044773975,0.02800451],"category_scores_gemma":[0.01040421,0.00024483245,0.0003245568,0.00046375993,0.0075902645,0.005240621,0.0024856215,0.0052484553,0.009824006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004685037,0.000045785597,0.0074177873,0.00007016647,0.000021283591,0.0011037824,0.03384158,0.00009452561,0.0009874286,0.26837075,0.63175887,0.05624115],"study_design_scores_gemma":[0.0000073781666,0.000021038779,0.003557408,0.00014869517,0.000013146083,0.00063649716,0.040167596,0.0001759007,0.0005365546,0.0149494475,0.93974274,0.00004352266],"about_ca_topic_score_codex":0.10938907,"about_ca_topic_score_gemma":0.20012303,"teacher_disagreement_score":0.10938907,"about_ca_system_score_codex":0.0042547486,"about_ca_system_score_gemma":0.004519235,"threshold_uncertainty_score":0.21750468},"labels":[],"label_agreement":null},{"id":"W79259524","doi":"10.1007/978-94-017-2702-0_3","title":"Hate Speech and the Law: A Canadian Perspective","year":2001,"lang":"en","type":"book-chapter","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Charter; Adjudication; Legislature; Deference; Political science; Perspective (graphical); Law; Judicial deference; Constitutional law; Law and economics; Sociology; Computer science","score_opus":0.009903906410982854,"score_gpt":0.20167531546686293,"score_spread":0.19177140905588008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W79259524","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072188126,0.104143575,0.0035236466,0.1773683,0.0025537664,0.000069386566,0.0009353828,0.00008785055,0.7040993],"genre_scores_gemma":[0.3649467,0.25760603,0.0062493808,0.038104177,0.0025042582,0.00010892634,0.00063574355,0.00025859103,0.3295862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948807,0.000778796,0.0001347319,0.0005871343,0.0021931878,0.0014255579],"domain_scores_gemma":[0.99315953,0.0019926652,0.00026810545,0.00016158908,0.0033434173,0.0010746061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003749431,0.0014712008,0.0012215031,0.010326895,0.020947766,0.017069038,0.0033145598,0.008333662,0.021486355],"category_scores_gemma":[0.010393021,0.0010528418,0.00077620306,0.014157058,0.027542057,0.0073513533,0.00251012,0.008078233,0.0013756048],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018261211,0.000031746862,0.0009349752,0.000150171,0.000009972688,0.00014355023,0.004488949,0.00047560228,0.00008215386,0.8599297,0.09725265,0.036482263],"study_design_scores_gemma":[0.000019003346,0.0000234195,0.0076962677,0.0010338111,0.0000450669,0.00023262133,0.011395937,0.0012250378,0.00020818204,0.13425416,0.8437017,0.0001648702],"about_ca_topic_score_codex":0.9958473,"about_ca_topic_score_gemma":0.9970521,"teacher_disagreement_score":0.19770953,"about_ca_system_score_codex":0.19770953,"about_ca_system_score_gemma":0.23434186,"threshold_uncertainty_score":0.93054324},"labels":[],"label_agreement":null},{"id":"W99876286","doi":"","title":"The Hidden Role of Pathos in Toulmin’s Layout of Argument","year":2005,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Pathos; Argumentation theory; Argument (complex analysis); Epistemology; Rhetorical question; Rationality; Appeal; Appeal to emotion; Sociology; Philosophy; Psychology; Law; Linguistics; Political science","score_opus":0.0075861609591408455,"score_gpt":0.19214244182165596,"score_spread":0.1845562808625151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W99876286","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066604815,0.0063732117,0.32507396,0.1068313,0.0013955194,0.00027145992,0.00016955455,0.00032883772,0.49295127],"genre_scores_gemma":[0.9367768,0.001424498,0.036385994,0.0053394404,0.000348912,0.00022907459,0.000052310203,0.00022986988,0.019213177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9901211,0.006591053,0.00033923466,0.0011364914,0.0013265611,0.00048554118],"domain_scores_gemma":[0.9867857,0.009734391,0.0009346721,0.000936788,0.0012639578,0.00034447422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074044364,0.00056490593,0.00056053576,0.003468529,0.005746254,0.009448853,0.0015861924,0.0043705245,0.0033390662],"category_scores_gemma":[0.02244987,0.0005899832,0.00079788367,0.001937707,0.042524427,0.01397087,0.004480638,0.005006734,0.0006870679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013649437,0.0000050762897,0.00021738092,0.000026972122,0.000003934113,0.00006308024,0.0117583005,0.00010452465,0.00009201362,0.9811864,0.002345134,0.0041835927],"study_design_scores_gemma":[0.00001202039,0.000016530117,0.00049536995,0.00014455861,0.000015272632,0.00017785981,0.0038164346,0.001176715,0.0005513378,0.9014719,0.09209881,0.000023228538],"about_ca_topic_score_codex":0.00399922,"about_ca_topic_score_gemma":0.0044939127,"teacher_disagreement_score":0.009448853,"about_ca_system_score_codex":0.006679835,"about_ca_system_score_gemma":0.0028688742,"threshold_uncertainty_score":0.04846579},"labels":[],"label_agreement":null}]}