{"meta":{"query_hash":"53a51b8911cc","filters":{"venue":"Journal of Privacy and Confidentiality"},"cohort_total":14,"direct_labels_cover":0,"predictions_cover":14,"exported":14,"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/53a51b8911cc","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Privacy+and+Confidentiality"},"results":[{"id":"W1563314065","doi":"10.29012/jpc.v1i2.571","title":"Maintaining Analytic Utility while Protecting Confidentiality of Survey and Nonsurvey Data","year":2010,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"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; Nutrition Obesity Research Center, University of North Carolina; RTI International; University of Chicago","keywords":"Confidentiality; Computer science; Macro; Actuarial science; Econometrics; Statistics; Data mining; Mathematics; Business; Computer security","score_opus":0.2908189185928526,"score_gpt":0.43491223749864016,"score_spread":0.14409331890578758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563314065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9071172,0.00007050261,0.09194873,0.000075711076,0.00020329913,0.0002838638,0.00010757693,0.00003036956,0.00016273255],"genre_scores_gemma":[0.98579127,0.000048950304,0.0140000405,0.000017361146,0.000098926816,0.0000020064736,0.000009891986,0.000015920969,0.00001563459],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996519,0.0010161523,0.0013909915,0.00034958214,0.00046838002,0.00025587564],"domain_scores_gemma":[0.9943809,0.0019433914,0.0016687916,0.0010144276,0.0008125748,0.00017990828],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.018271081,0.00021524879,0.00075493555,0.00012942897,0.00016494811,0.00013096735,0.00072480127,0.00017000413,0.00016174164],"category_scores_gemma":[0.0104329195,0.00019199634,0.00007980667,0.00018267203,0.00043123605,0.0007842706,0.0007126154,0.0010696445,3.9715286e-7],"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":[0.0013516608,0.0010889667,0.8185749,0.001664521,0.0011911078,0.000055714194,0.0050661243,0.0000012881468,0.09944436,0.027616907,0.0011012794,0.042843137],"study_design_scores_gemma":[0.0014998614,0.00028891375,0.6708709,0.00029519424,0.0003507336,0.00035063675,0.0011308225,0.000858932,0.026702685,0.2966131,0.00049549434,0.00054271653],"about_ca_topic_score_codex":0.0023260515,"about_ca_topic_score_gemma":0.0022302233,"teacher_disagreement_score":0.26899618,"about_ca_system_score_codex":0.00001628392,"about_ca_system_score_gemma":0.00016349556,"threshold_uncertainty_score":0.99790263},"labels":[],"label_agreement":null},{"id":"W2119589135","doi":"10.29012/jpc.v2i2.591","title":"Polynomial-time Attack on Output Perturbation Sanitizers for Real-valued Databases","year":2011,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Cryptography and Data Security","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":"York University","funders":"","keywords":"Impossibility; Binary number; Perturbation (astronomy); Adversary; Computer science; Upper and lower bounds; Time complexity; Mathematics; Theoretical computer science; Database; Algorithm; Computer security; Arithmetic; Law","score_opus":0.09398168476257879,"score_gpt":0.30958735054845077,"score_spread":0.21560566578587198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119589135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5123001,0.00016494558,0.48444605,0.00037820614,0.0008349005,0.00026279982,0.000102699196,0.00004181944,0.0014685132],"genre_scores_gemma":[0.9391471,0.00019363771,0.059802365,0.0004364355,0.00034252714,0.0000041300136,0.000017119813,0.000009316629,0.00004735452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9987422,0.000111657326,0.0004705244,0.00023189897,0.00026416895,0.0001795274],"domain_scores_gemma":[0.9986333,0.00016287155,0.00044033452,0.00042816423,0.00019496695,0.00014036268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009274943,0.00013152685,0.00025132947,0.0001386384,0.00016114519,0.00012953687,0.00055657496,0.00004899536,0.000052446867],"category_scores_gemma":[0.0001671522,0.000112197384,0.00017057805,0.00011428169,0.00008052305,0.001065435,0.00015236053,0.00014034429,0.0000058050755],"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":[0.004777346,0.0022693183,0.004465257,0.0004243393,0.0010585878,0.00011550897,0.029727966,0.000007984994,0.014501715,0.6558852,0.18496019,0.10180658],"study_design_scores_gemma":[0.026839064,0.00927166,0.37172598,0.0012336235,0.0015584237,0.0010741968,0.0026293413,0.022170072,0.0892274,0.10094878,0.36914742,0.0041740444],"about_ca_topic_score_codex":0.00017360061,"about_ca_topic_score_gemma":0.000007953756,"teacher_disagreement_score":0.5549364,"about_ca_system_score_codex":0.000017752749,"about_ca_system_score_gemma":0.000079535734,"threshold_uncertainty_score":0.45752767},"labels":[],"label_agreement":null},{"id":"W2595008630","doi":"10.29012/jpc.v7i3.406","title":"On the Meaning and Limits of Empirical Differential Privacy","year":2017,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","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 British Columbia; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Differential privacy; Computer science; Meaning (existential); Conjugate prior; Measure (data warehouse); Bayesian probability; Discretization; Empirical measure; Differential (mechanical device); Empirical research; Data mining; Posterior probability; Mathematics; Artificial intelligence; Statistics","score_opus":0.0786503603741271,"score_gpt":0.3423210247903177,"score_spread":0.2636706644161906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595008630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.915297,0.0001358303,0.05409889,0.029611245,0.00044759436,0.000093628376,0.000004227256,0.000025193744,0.00028643198],"genre_scores_gemma":[0.99192244,0.00018746668,0.007658626,0.00013342345,0.000079914535,0.0000010574745,2.1890838e-7,0.0000054454845,0.0000113827655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99832857,0.00017678265,0.0005414727,0.0002590762,0.0004895059,0.00020461711],"domain_scores_gemma":[0.99388725,0.000487424,0.0010543783,0.004315431,0.0001664293,0.000089088855],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0011615079,0.0001514316,0.00034249653,0.0000918512,0.0004376281,0.0006032976,0.015178324,0.000104612685,0.000019056917],"category_scores_gemma":[0.019719593,0.00009855187,0.00009752277,0.00006354021,0.0004205023,0.00084713334,0.025138084,0.00047084378,0.000001225821],"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":[0.00077706535,0.0012184883,0.10345778,0.00045160885,0.0013644205,0.0002577036,0.0076266164,0.0000032563103,0.048842546,0.46887848,0.12535183,0.2417702],"study_design_scores_gemma":[0.0012111001,0.00035872386,0.21476428,0.0002610339,0.000073747644,0.00016661754,0.000095425734,0.0040383656,0.026028486,0.7516736,0.0010820888,0.0002465686],"about_ca_topic_score_codex":0.000042448388,"about_ca_topic_score_gemma":0.000003896008,"teacher_disagreement_score":0.28279507,"about_ca_system_score_codex":0.000017508142,"about_ca_system_score_gemma":0.000065118744,"threshold_uncertainty_score":0.99015003},"labels":[],"label_agreement":null},{"id":"W2740605573","doi":"10.29012/jpc.654","title":"Differentially Private Ordinary Least Squares","year":2019,"lang":"en","type":"preprint","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":25,"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":"Harvard University; National Science Foundation","keywords":"Mathematics; Ordinary least squares; Estimator; Statistics; Confidence interval; Linear regression; Regression; Gaussian; Econometrics","score_opus":0.03203930416041271,"score_gpt":0.2885691054556599,"score_spread":0.25652980129524716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740605573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4363647,0.001987949,0.5440172,0.01247261,0.0043184347,0.00036137374,0.00004482587,0.00020174947,0.0002311549],"genre_scores_gemma":[0.92631537,0.0012410247,0.07181438,0.00015897553,0.00034903706,0.0000059974586,0.000012510245,0.000027635906,0.00007506363],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99576265,0.00042089724,0.0014549192,0.00084552675,0.001019277,0.000496712],"domain_scores_gemma":[0.9878405,0.00024204531,0.002187599,0.009069563,0.00045738212,0.00020294229],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0017007324,0.00051732484,0.0010906002,0.0004385581,0.00015213597,0.0011895931,0.03868115,0.0005985994,0.00006186733],"category_scores_gemma":[0.0057306383,0.00045643843,0.00042065213,0.00022737606,0.0002591477,0.0011862904,0.20280215,0.0021665706,0.0000197895],"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":[0.0011360446,0.0025967506,0.0456804,0.0073525542,0.0056177475,0.0016725645,0.0031681834,0.00032912457,0.016896188,0.057516202,0.4792868,0.37874746],"study_design_scores_gemma":[0.0016995656,0.00040421978,0.07211073,0.0013207193,0.00027361844,0.0005493147,0.00007456586,0.011846127,0.0044335555,0.8965381,0.009627566,0.0011219404],"about_ca_topic_score_codex":0.00017698681,"about_ca_topic_score_gemma":0.000007196151,"teacher_disagreement_score":0.83902186,"about_ca_system_score_codex":0.00012070704,"about_ca_system_score_gemma":0.00044477973,"threshold_uncertainty_score":0.9998473},"labels":[],"label_agreement":null},{"id":"W2963817128","doi":"10.29012/jpc.724","title":"INSPECTRE: Privately Estimating the Unseen","year":2020,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":13,"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 Waterloo; Office of Naval Research; Simons Institute for the Theory of Computing, University of California Berkeley; Microsoft Research; National Science Foundation","keywords":"Sublinear function; Differential privacy; Sample (material); Entropy (arrow of time); Computer science; Mathematics; Sample size determination; Sensitivity (control systems); Statistics; Algorithm; Discrete mathematics; Engineering; Physics","score_opus":0.04265814773859074,"score_gpt":0.28613413175128727,"score_spread":0.24347598401269654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963817128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16155745,0.0004538022,0.6799197,0.15723532,0.00042556482,0.00009956722,0.0000028126908,0.00012076907,0.00018499792],"genre_scores_gemma":[0.82992995,0.00006343252,0.16870792,0.0010723454,0.00021516568,0.0000010227493,3.7390635e-7,0.0000054318853,0.0000043691857],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998405,0.00016057855,0.0005642029,0.00023816268,0.00042400026,0.00020805238],"domain_scores_gemma":[0.9967768,0.00022538593,0.0005885416,0.0021519612,0.00013666622,0.000120643745],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0010701626,0.00013059466,0.00024859537,0.00006508475,0.00019071305,0.00042546054,0.016941087,0.00006767749,0.000016368625],"category_scores_gemma":[0.013292194,0.00008854944,0.00008692695,0.00043438058,0.00015687598,0.0009920348,0.025337089,0.0005290017,0.000006997378],"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":[0.00026796298,0.00035156365,0.01616386,0.00045417898,0.0009465663,0.00059392635,0.013175691,0.00014782113,0.022165095,0.11284021,0.40113798,0.43175516],"study_design_scores_gemma":[0.0015686975,0.00046311872,0.021109505,0.00015216212,0.000101187914,0.0006245576,0.00033763974,0.17745443,0.017374985,0.7540892,0.02620546,0.0005191127],"about_ca_topic_score_codex":0.000030299238,"about_ca_topic_score_gemma":0.0000013160984,"teacher_disagreement_score":0.6683725,"about_ca_system_score_codex":0.000022264807,"about_ca_system_score_gemma":0.0000991726,"threshold_uncertainty_score":0.99501926},"labels":[],"label_agreement":null},{"id":"W2999073677","doi":"10.29012/jpc.736","title":"Editorial for Special Issue on the Theory and Practice of Differential Privacy 2018","year":2020,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","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":"Differential privacy; Computer science; Differential (mechanical device); Internet privacy; Computer security; Engineering; Data mining","score_opus":0.03617561366068503,"score_gpt":0.3077065375409062,"score_spread":0.27153092388022115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999073677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08206802,0.00043051218,0.62008137,0.23732424,0.058201145,0.0009145054,0.0000818835,0.00010310659,0.00079523405],"genre_scores_gemma":[0.7859796,0.000660029,0.044776917,0.0018199348,0.16669305,0.000010700703,0.0000030081355,0.000026431215,0.000030286374],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99780875,0.0005219492,0.00061572675,0.00029025492,0.0005653643,0.00019792504],"domain_scores_gemma":[0.9938829,0.0030913546,0.00091515365,0.0016639256,0.0003313132,0.000115347946],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002623505,0.00016222052,0.00034995924,0.00006055409,0.0001614017,0.00027263674,0.007834031,0.00013121244,0.00005257889],"category_scores_gemma":[0.069913656,0.00011158767,0.00011237207,0.00014568174,0.00027649224,0.00089616334,0.012328503,0.00043840765,0.0000023042553],"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":[0.002055616,0.00012642545,0.00003920945,0.00007050233,0.00017635559,0.000005061808,0.0011964594,3.2132183e-7,0.0031289868,0.04451594,0.9281374,0.020547697],"study_design_scores_gemma":[0.0020968635,0.0014387942,0.0007073387,0.00008743637,0.00017424075,0.000065081396,0.0005524419,0.0007191668,0.011988357,0.37523738,0.6066717,0.00026123755],"about_ca_topic_score_codex":0.0000127350995,"about_ca_topic_score_gemma":4.9008247e-7,"teacher_disagreement_score":0.7039116,"about_ca_system_score_codex":0.000018866274,"about_ca_system_score_gemma":0.00009499081,"threshold_uncertainty_score":0.99753404},"labels":[],"label_agreement":null},{"id":"W2999940786","doi":"10.29012/jpc.697","title":"Program for TPDP 2018","year":2018,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Legal and Policy Issues","field":"Social Sciences","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":"Differential privacy; Differential (mechanical device); Computer science; Library science; Data science; Engineering; Data mining","score_opus":0.05281254402730334,"score_gpt":0.4200759570956735,"score_spread":0.3672634130683702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999940786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9723813,0.00038476364,0.0011324331,0.008650334,0.0015424832,0.00034653812,0.000007364219,0.000029294313,0.015525478],"genre_scores_gemma":[0.9936867,0.000102295584,0.0011214693,0.00025032504,0.0032764634,0.0000030057745,2.964845e-7,0.0000032897415,0.0015561548],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991921,0.00010482562,0.00025421183,0.00007005965,0.00020612762,0.00017265158],"domain_scores_gemma":[0.9992173,0.000075445074,0.00021329046,0.000074176234,0.00031580476,0.00010399408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011460707,0.000053294094,0.0001429034,0.0000345218,0.00033629584,0.00016542106,0.00020587315,0.000058288933,0.00016413703],"category_scores_gemma":[0.00027895474,0.00004208392,0.000088073255,0.00006976667,0.00039687124,0.00028739215,0.000029251645,0.000073169256,0.00000783643],"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":[0.001004125,0.00060176686,0.008053843,0.0001376941,0.00035565216,0.000009682007,0.11101646,8.3272184e-8,0.001841568,0.2774196,0.15230688,0.44725266],"study_design_scores_gemma":[0.00034909823,0.0004061045,0.003196688,0.000017578439,0.000037594717,0.000005016872,0.000985969,0.0000025086053,0.00086504524,0.028727058,0.9653445,0.00006285703],"about_ca_topic_score_codex":0.0020609666,"about_ca_topic_score_gemma":0.0005561039,"teacher_disagreement_score":0.8130376,"about_ca_system_score_codex":0.000016459528,"about_ca_system_score_gemma":0.00013509051,"threshold_uncertainty_score":0.3115579},"labels":[],"label_agreement":null},{"id":"W3015161938","doi":"10.29012/jpc.784","title":"Discrete Gaussian for Differential Privacy","year":2022,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":37,"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":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Compute Canada","keywords":"Differential privacy; Gaussian; Computer science; Mathematics; Physics; Algorithm; Quantum mechanics","score_opus":0.029094971369360165,"score_gpt":0.29464366984682266,"score_spread":0.2655486984774625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015161938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20364517,0.0003412435,0.7692876,0.024691233,0.0015294469,0.00026830586,0.000053679432,0.00010018076,0.000083145824],"genre_scores_gemma":[0.9499673,0.00006225919,0.049529653,0.00017810332,0.0001656029,0.000020934776,0.0000060258994,0.000011607654,0.00005851458],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977297,0.0002237098,0.00071499584,0.00036895953,0.00061731326,0.00034530382],"domain_scores_gemma":[0.9957646,0.00020827643,0.00073228223,0.0030300308,0.00013307264,0.00013174494],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0012617046,0.00018112415,0.00037823367,0.00020315556,0.00050278695,0.00035053442,0.017770447,0.000065264045,0.00010988963],"category_scores_gemma":[0.0037661784,0.00016288817,0.00021069661,0.0002513267,0.0001201767,0.0009423417,0.05157695,0.00052522944,0.0000011586502],"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":[0.001382357,0.0015457117,0.009350321,0.00059413933,0.0014838671,0.00032955362,0.005482913,0.000041762094,0.03678609,0.17964809,0.5343553,0.22899991],"study_design_scores_gemma":[0.003481961,0.0009431739,0.014205026,0.000054376906,0.00013769622,0.0006760103,0.0004110579,0.01723199,0.006912836,0.8650824,0.09024696,0.00061651686],"about_ca_topic_score_codex":0.00003915596,"about_ca_topic_score_gemma":0.0000020357222,"teacher_disagreement_score":0.7463221,"about_ca_system_score_codex":0.00008427099,"about_ca_system_score_gemma":0.00014255174,"threshold_uncertainty_score":0.9875439},"labels":[],"label_agreement":null},{"id":"W3126588461","doi":"10.29012/jpc.765","title":"Reflections on the Successes and Challenges of Research Data Centers in Canada and the U.S.","year":2021,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Data Quality and Management","field":"Decision Sciences","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; Regional science; Library science; Data science; Engineering ethics; Geography; Engineering; Computer science","score_opus":0.513031489092612,"score_gpt":0.5069444633669749,"score_spread":0.00608702572563713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126588461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85827506,0.0060236054,0.00013032656,0.13358508,0.00029531782,0.00012406381,0.00005253232,8.3985236e-7,0.0015131551],"genre_scores_gemma":[0.98927575,0.010305425,0.000020927147,0.00031033866,0.00003305337,7.325576e-7,8.2386805e-7,0.0000013080529,0.000051639166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9966484,0.0015759768,0.0005282039,0.00018966402,0.0009533332,0.00010442829],"domain_scores_gemma":[0.9952601,0.003497747,0.00025103547,0.00064368325,0.00029834223,0.000049088558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015500897,0.00004867007,0.00020023095,0.000082311824,0.00016955564,0.0002407879,0.00079294614,0.000016121017,0.00004901009],"category_scores_gemma":[0.00476271,0.000024155408,0.00001835987,0.00024700307,0.0003518943,0.00031105118,0.00095658016,0.00024402185,2.6285736e-7],"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":[0.0014131021,0.0003901474,0.017648106,0.00036993675,0.00042772797,0.0001717144,0.015205275,0.000014511449,0.00034346743,0.79537725,0.03290143,0.13573731],"study_design_scores_gemma":[0.0027301414,0.000119367694,0.4115006,0.00027028032,0.000082036095,0.00012061546,0.15429658,0.00015256704,0.00080159155,0.21461172,0.21515884,0.00015567461],"about_ca_topic_score_codex":0.38447738,"about_ca_topic_score_gemma":0.8562723,"teacher_disagreement_score":0.58076555,"about_ca_system_score_codex":0.000020657844,"about_ca_system_score_gemma":0.00035980696,"threshold_uncertainty_score":0.61962146},"labels":[],"label_agreement":null},{"id":"W3198431637","doi":"10.29012/jpc.769","title":"Research Data Centres - a Regulator's Perspective","year":2021,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Research Data Management Practices","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":"Regulator; Perspective (graphical); Computer science; Biology; Artificial intelligence","score_opus":0.23203570928983966,"score_gpt":0.46273747352166017,"score_spread":0.2307017642318205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198431637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24551865,0.014741685,0.6040844,0.11687276,0.0018537827,0.00053397886,0.00006133855,0.00006658068,0.016266864],"genre_scores_gemma":[0.9846467,0.003298442,0.010466319,0.00014451232,0.0003162435,8.837805e-7,0.0000052551695,0.0000058313994,0.0011158215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99635607,0.0012317289,0.00038618452,0.0004473852,0.0012848491,0.00029377072],"domain_scores_gemma":[0.9955368,0.0005177377,0.0002788734,0.0020064523,0.0014701673,0.00018998334],"candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.007585796,0.00008001803,0.00019180677,0.00019246049,0.00022078524,0.003707573,0.0040265457,0.00003800734,0.00005948687],"category_scores_gemma":[0.00330346,0.000069821304,0.000046387457,0.0005525329,0.00015519197,0.022587733,0.0062038153,0.00054069795,0.000007164825],"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":[0.000080152684,0.00033290221,0.0012439815,0.00007232756,0.0003609345,0.00092499395,0.001602727,0.0000023375185,0.002129703,0.9595846,0.022810183,0.010855155],"study_design_scores_gemma":[0.0025657904,0.00034029677,0.09413184,0.00023398882,0.00012448843,0.0012030996,0.017623898,0.0028408582,0.009098488,0.14982961,0.7215313,0.00047637086],"about_ca_topic_score_codex":0.00041562764,"about_ca_topic_score_gemma":0.00006594797,"teacher_disagreement_score":0.80975497,"about_ca_system_score_codex":0.00006448692,"about_ca_system_score_gemma":0.0005072274,"threshold_uncertainty_score":0.9973267},"labels":[],"label_agreement":null},{"id":"W4386399481","doi":"10.29012/jpc.808","title":"Private Boosted Decision Trees via Smooth Re-Weighting","year":2023,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","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; Simon Fraser University","funders":"","keywords":"Differential privacy; Boosting (machine learning); Decision tree; Computer science; Weighting; Machine learning; Artificial intelligence; Gradient boosting; Ensemble learning; Privacy protection; ID3 algorithm; Alternating decision tree; Decision tree learning; Data mining; Incremental decision tree; Computer security; Random forest","score_opus":0.03350593916545908,"score_gpt":0.29915564061178873,"score_spread":0.26564970144632966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386399481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57972836,0.00026724482,0.41075283,0.007992244,0.0007766036,0.00009503672,0.000004001843,0.00027329606,0.00011036402],"genre_scores_gemma":[0.9194949,0.00041337378,0.07979034,0.00013281072,0.00012581036,0.0000019622614,0.0000022680385,0.000011826982,0.000026695121],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99749416,0.00017148757,0.0008828396,0.00038005278,0.00069812615,0.0003733357],"domain_scores_gemma":[0.99504125,0.0005014385,0.0007285956,0.003380477,0.00020687818,0.000141346],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0024628162,0.00018952713,0.00038168186,0.000387922,0.00023500777,0.00043005022,0.013561017,0.0001470616,0.000029229575],"category_scores_gemma":[0.011056602,0.00015897868,0.00013607365,0.00085162674,0.00012701695,0.0014777799,0.032456137,0.000456498,0.000026580708],"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":[0.00021416554,0.00022230264,0.013209008,0.00013225563,0.00030925815,0.00068358,0.001332203,0.000024974885,0.049159687,0.007820975,0.16097094,0.76592064],"study_design_scores_gemma":[0.0014810622,0.00023811507,0.08765549,0.00032062372,0.00005135762,0.00023506537,0.0001395252,0.033022486,0.021638557,0.8371911,0.017592564,0.0004340599],"about_ca_topic_score_codex":0.000050506773,"about_ca_topic_score_gemma":0.000014099875,"teacher_disagreement_score":0.82937014,"about_ca_system_score_codex":0.000049560167,"about_ca_system_score_gemma":0.00007233325,"threshold_uncertainty_score":0.9972737},"labels":[],"label_agreement":null},{"id":"W4399971959","doi":"10.29012/jpc.873","title":"Private Query Release via the Johnson-Lindenstrauss Transform","year":2024,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Cryptography and Data Security","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 Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","score_opus":0.01374124547713643,"score_gpt":0.26278040313545475,"score_spread":0.24903915765831833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399971959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16856436,0.004023098,0.8209162,0.0048803,0.0012037769,0.00011650218,0.0000102806625,0.000046762798,0.00023871796],"genre_scores_gemma":[0.99650687,0.0010679462,0.001866459,0.0002637587,0.00027811012,0.0000014434069,0.0000010586903,0.000005278463,0.000009078373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99847263,0.00014230215,0.0005441535,0.00022104802,0.00041463887,0.0002052505],"domain_scores_gemma":[0.99900615,0.00016804242,0.000159815,0.0004230659,0.00009534703,0.00014757836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468345,0.00013856818,0.00020902048,0.00012138167,0.00018526061,0.00074427854,0.0009537455,0.000071212824,0.000037250215],"category_scores_gemma":[0.000037495225,0.00008848362,0.00024922434,0.00034021214,0.00014490065,0.0012800168,0.0001590631,0.0005122847,0.0000068871263],"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":[0.0001666005,0.0002534501,0.0005255252,0.0003607649,0.0004923796,0.0004962524,0.00771776,0.000007331051,0.0035175858,0.5849704,0.005869943,0.39562201],"study_design_scores_gemma":[0.0013667623,0.00037319376,0.021567773,0.00041623256,0.00029437087,0.0027137988,0.00028480755,0.010124722,0.008296603,0.59115094,0.36281356,0.0005972212],"about_ca_topic_score_codex":0.00009719947,"about_ca_topic_score_gemma":0.00002033935,"teacher_disagreement_score":0.8279425,"about_ca_system_score_codex":0.00001576277,"about_ca_system_score_gemma":0.000116053016,"threshold_uncertainty_score":0.7177095},"labels":[],"label_agreement":null},{"id":"W4399971973","doi":"10.29012/jpc.880","title":"Differentially Private Fine-tuning of Language Models","year":2024,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Private speech; Base (topology); Artificial intelligence; Private information retrieval; Language model; Machine learning; Scale (ratio); Natural language processing; Data mining; Computer security; Mathematics","score_opus":0.03058623113808286,"score_gpt":0.29141095495886227,"score_spread":0.2608247238207794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399971973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40868065,0.0020385124,0.58566105,0.002819119,0.0005122567,0.000053804535,0.000008476463,0.00009061039,0.00013550132],"genre_scores_gemma":[0.9479591,0.00024296343,0.051646944,0.000030640167,0.000080570935,9.678644e-7,0.0000013344484,0.000008387378,0.000029063145],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982886,0.000113756694,0.00068132376,0.0002607289,0.00044725602,0.00020835939],"domain_scores_gemma":[0.9971359,0.00017698447,0.00035368255,0.0021305967,0.00012416094,0.000078679244],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0010192894,0.00014690559,0.00033771692,0.00024309401,0.000056551744,0.00033816384,0.009032965,0.00009929719,0.000025945355],"category_scores_gemma":[0.0019096847,0.000120537436,0.00013982282,0.0002940085,0.00012216724,0.0014511985,0.017417505,0.00040858626,0.0000021230715],"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":[0.00015085831,0.00041763377,0.001460527,0.0017210186,0.0012489815,0.0008844934,0.009039147,0.00009509202,0.23444979,0.31069925,0.037486225,0.40234697],"study_design_scores_gemma":[0.0006294205,0.00017512459,0.0021659145,0.000665157,0.00010050548,0.0002870079,0.00015383845,0.14234033,0.045965336,0.805797,0.0014182477,0.00030213466],"about_ca_topic_score_codex":0.00006834903,"about_ca_topic_score_gemma":0.00000582421,"teacher_disagreement_score":0.53927845,"about_ca_system_score_codex":0.000027261853,"about_ca_system_score_gemma":0.00010585516,"threshold_uncertainty_score":0.99632865},"labels":[],"label_agreement":null},{"id":"W768808963","doi":"10.29012/jpc.v7i2.652","title":"Heterogeneous Differential Privacy","year":2017,"lang":"en","type":"preprint","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":35,"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":"Differential privacy; Computer science; Cluster analysis; Personalization; Mechanism (biology); Task (project management); Personally identifiable information; Domain (mathematical analysis); Information sensitivity; Privacy software; Function (biology); Information privacy; Information retrieval; Data mining; Internet privacy; Computer security; World Wide Web; Artificial intelligence; Mathematics","score_opus":0.04673858409058168,"score_gpt":0.31348688253411744,"score_spread":0.26674829844353576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W768808963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4704563,0.0025589576,0.50375426,0.016089221,0.0062646163,0.0003701354,0.000060587816,0.00023864556,0.00020724168],"genre_scores_gemma":[0.95385236,0.0016351886,0.04373962,0.00009583427,0.00058335863,0.0000074797117,0.000008968522,0.000025918354,0.000051259405],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99583447,0.00033314,0.0014071416,0.0008750526,0.0010218885,0.00052830577],"domain_scores_gemma":[0.9815286,0.00017200968,0.0031936453,0.014335823,0.00049548026,0.00027444263],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0014085819,0.0005392276,0.0011197546,0.00035059112,0.00038719512,0.0023642955,0.061736736,0.00066579954,0.000057615573],"category_scores_gemma":[0.011852418,0.0004843009,0.0005069481,0.00008439462,0.00040616823,0.0011523858,0.25391394,0.0020709091,0.000009530037],"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":[0.00079979474,0.0025025755,0.01132051,0.003958245,0.0067410814,0.0041392604,0.0048835147,0.000097523916,0.012305058,0.015055234,0.321409,0.6167882],"study_design_scores_gemma":[0.0015489487,0.00023791293,0.010657355,0.00079821714,0.00029938604,0.0011210021,0.000021650736,0.009605651,0.010499423,0.9533549,0.010833848,0.0010216912],"about_ca_topic_score_codex":0.00019686733,"about_ca_topic_score_gemma":0.000009004599,"teacher_disagreement_score":0.93829966,"about_ca_system_score_codex":0.00012113371,"about_ca_system_score_gemma":0.00046102746,"threshold_uncertainty_score":0.99976087},"labels":[],"label_agreement":null}]}