{"meta":{"query_hash":"206c29829450","filters":{"venue":"Political Analysis"},"cohort_total":31,"direct_labels_cover":0,"predictions_cover":31,"exported":31,"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/206c29829450","api":"https://metacan.xera.ac/api/v1/cohort?venue=Political+Analysis"},"results":[{"id":"W1984207056","doi":"10.1093/oxfordjournals.pan.a004862","title":"Methodological Artifacts in Measures of Political Efficacy and Trust: A Multiple Correspondence Analysis","year":2001,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":53,"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":"Politics; Political efficacy; Set (abstract data type); Test (biology); Social psychology; Psychology; National election; Political science; Computer science; Law","score_opus":0.3002946878885215,"score_gpt":0.45182794700522577,"score_spread":0.1515332591167043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984207056","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":[0.29007718,0.0005515996,0.68718237,0.00061598123,0.00025814163,0.0050373427,0.00080465834,0.00034432867,0.015128452],"genre_scores_gemma":[0.7609978,0.00013821108,0.22743116,0.000113674156,0.000084131665,0.009139172,0.0004920423,0.00019418063,0.0014095762],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.59897876,0.3101942,0.027241647,0.014772762,0.04617742,0.0026351302],"domain_scores_gemma":[0.4498661,0.44133735,0.031120757,0.049757145,0.027011055,0.0009075717],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15782362,0.0012412127,0.0015653,0.010812383,0.003930276,0.005090126,0.002549698,0.0016145231,0.003977318],"category_scores_gemma":[0.49224234,0.001116161,0.0025122066,0.021932159,0.0044754874,0.0029776192,0.0054379622,0.002054289,0.0005328346],"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":[0.002036361,0.0009159732,0.39053756,0.003474965,0.004971787,0.0010969536,0.042664602,0.011075565,0.005936962,0.23049118,0.0063003385,0.30049777],"study_design_scores_gemma":[0.00069273147,0.0026890973,0.5183975,0.0012642791,0.0032591436,0.001660319,0.020394932,0.14845051,0.02053313,0.2254411,0.056477595,0.0007395872],"about_ca_topic_score_codex":0.0042698416,"about_ca_topic_score_gemma":0.0043806476,"teacher_disagreement_score":0.8421764,"about_ca_system_score_codex":0.0034821401,"about_ca_system_score_gemma":0.004453643,"threshold_uncertainty_score":0.83466107},"labels":[],"label_agreement":null},{"id":"W2008484920","doi":"10.1093/pan/mpr039","title":"Model-based Clustering and Typologies in the Social Sciences","year":2011,"lang":"en","type":"article","venue":"Political Analysis","topic":"Qualitative Comparative Analysis Research","field":"Social Sciences","cited_by":147,"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":"Typology; Cluster analysis; Computer science; Curse of dimensionality; Cluster (spacecraft); Sociology; Artificial intelligence; Data science; Machine learning; Epistemology; Management science; Economics","score_opus":0.5103260896461577,"score_gpt":0.5439912731955652,"score_spread":0.03366518354940751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008484920","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":[0.031106805,0.019649645,0.87882394,0.020612359,0.0007905455,0.0005846309,0.0005856811,0.00032102136,0.047525443],"genre_scores_gemma":[0.5285587,0.011492975,0.45182002,0.0015689773,0.00076941244,0.0021894933,0.00074009475,0.00017036308,0.002690058],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96120006,0.028893573,0.0017056891,0.002729247,0.0048794234,0.0005920544],"domain_scores_gemma":[0.94014245,0.04540052,0.0042348728,0.0065780203,0.0028281922,0.0008160024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027829068,0.0011383698,0.0019299127,0.0164604,0.0042160843,0.009379665,0.0028774834,0.003960554,0.0031942725],"category_scores_gemma":[0.05093374,0.00074241945,0.0018289152,0.016383419,0.021354971,0.0125849135,0.005962326,0.0039594634,0.0005509201],"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":[0.000007929324,0.000015583233,0.0017436723,0.0002627437,0.000048524893,0.000042226195,0.0016377091,0.0067983125,0.000047768484,0.9715542,0.0014200169,0.016421242],"study_design_scores_gemma":[0.0000037144728,0.0000072482817,0.0006338463,0.00015781769,0.000008182216,0.00005177034,0.0008352724,0.009625743,0.000055286797,0.9820877,0.0065153027,0.000018090928],"about_ca_topic_score_codex":0.0028354647,"about_ca_topic_score_gemma":0.0031357924,"teacher_disagreement_score":0.027829068,"about_ca_system_score_codex":0.007853756,"about_ca_system_score_gemma":0.0044872,"threshold_uncertainty_score":0.14717591},"labels":[],"label_agreement":null},{"id":"W2114827468","doi":"10.1093/pan/mpn014","title":"Measurement Equivalence of Nationalism and Constructive Patriotism in the ISSP: 34 Countries in a Comparative Perspective","year":2009,"lang":"en","type":"article","venue":"Political Analysis","topic":"Cultural Differences and Values","field":"Psychology","cited_by":265,"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":"Institute of Population and Public Health","keywords":"Nationalism; Patriotism; Measurement invariance; Constructive; Equivalence (formal languages); Confirmatory factor analysis; Social psychology; Psychology; Sociology; Structural equation modeling; Mathematics; Statistics; Political science; Pure mathematics; Computer science; Law; Politics","score_opus":0.12585407366482462,"score_gpt":0.4183749259464855,"score_spread":0.2925208522816609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114827468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9815793,0.0005151152,0.002168651,0.00012018326,0.000029128485,0.000071726245,0.00040800255,0.0000063819075,0.015101453],"genre_scores_gemma":[0.9968291,0.00020330519,0.002380448,0.000023001328,0.0000065532645,0.00007605332,0.00032194422,0.0000065388317,0.00015311589],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9895175,0.0077611357,0.00073121354,0.00056740455,0.00097110274,0.00045164433],"domain_scores_gemma":[0.98800117,0.0052550714,0.0020216552,0.0016730782,0.0027247795,0.0003242114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010243133,0.00033894682,0.0006071725,0.0067641134,0.0010655817,0.0023036376,0.00052233285,0.00038575364,0.0014150295],"category_scores_gemma":[0.024994766,0.00029271905,0.0007407403,0.00918049,0.002248735,0.002466198,0.0036805472,0.0009130586,0.00012329825],"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":[0.00034469267,0.00010911114,0.80786747,0.0005538886,0.0008856087,0.0006925899,0.05554825,0.0016945581,0.002096461,0.041138005,0.0009980306,0.08807145],"study_design_scores_gemma":[0.000017476237,0.00024658412,0.93833834,0.00047926264,0.00024398921,0.00049913843,0.046286833,0.0012002748,0.0010745667,0.0038200002,0.0077446653,0.00004878561],"about_ca_topic_score_codex":0.0066262935,"about_ca_topic_score_gemma":0.0066592093,"teacher_disagreement_score":0.010243133,"about_ca_system_score_codex":0.0013854303,"about_ca_system_score_gemma":0.0009847657,"threshold_uncertainty_score":0.054171503},"labels":[],"label_agreement":null},{"id":"W2154766867","doi":"10.1093/pan/mpq038","title":"Stochastic Process Methods with an Application to Budgetary Data","year":2010,"lang":"en","type":"article","venue":"Political Analysis","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":86,"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":"Quantile; Computer science; Econometrics; Variety (cybernetics); Process (computing); Stochastic process; Quantile regression; Distribution (mathematics); Variable (mathematics); Statistics; Mathematics; Artificial intelligence","score_opus":0.056947273401056035,"score_gpt":0.4483611952715523,"score_spread":0.3914139218704963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154766867","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":[0.001742886,0.00030122598,0.99661934,0.0003948786,0.000028629036,0.000064511856,0.000089857414,0.0001160631,0.0006426558],"genre_scores_gemma":[0.09937946,0.0017426531,0.8948768,0.00027535448,0.00035285208,0.0012589707,0.00039846078,0.00015805675,0.0015575105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9884792,0.0088090785,0.0005001174,0.0006399455,0.0013950714,0.00017658992],"domain_scores_gemma":[0.92250335,0.070447035,0.0024435278,0.001999634,0.0022480753,0.00035838148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017792387,0.0012004331,0.0014163778,0.0049154386,0.0010980499,0.00214161,0.0016950857,0.0016991679,0.003982359],"category_scores_gemma":[0.08564616,0.00084318005,0.001713271,0.0059296414,0.0024057925,0.0022738285,0.0034626778,0.0041468935,0.0007527063],"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":[0.0000318218,0.00010404853,0.004374591,0.00021550145,0.00021440475,0.0003019697,0.0004823619,0.17058572,0.00044801677,0.71585417,0.0026518044,0.104735665],"study_design_scores_gemma":[0.00003342532,0.000031369174,0.0008161153,0.00008433818,0.000019834315,0.000084669235,0.000107863074,0.5078627,0.0002038736,0.48427257,0.0064490037,0.000034267196],"about_ca_topic_score_codex":0.007117862,"about_ca_topic_score_gemma":0.006131146,"teacher_disagreement_score":0.017792387,"about_ca_system_score_codex":0.0012976441,"about_ca_system_score_gemma":0.0024185334,"threshold_uncertainty_score":0.09409624},"labels":[],"label_agreement":null},{"id":"W2155643382","doi":"10.1093/pan/mpl008","title":"The Logic of the Survey Experiment Reexamined","year":2006,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":556,"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":"Panacea (medicine); Inference; Range (aeronautics); Survey research; Computer science; Simple (philosophy); Survey data collection; Survey methodology; Data science; Experimental data; Politics; Psychology; Management science; Econometrics; Epistemology; Applied psychology; Artificial intelligence; Political science; Statistics; Mathematics; Engineering; Medicine; Alternative medicine; Law","score_opus":0.06365078579159351,"score_gpt":0.3727602341563828,"score_spread":0.3091094483647893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155643382","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":[0.016897712,0.0016505859,0.7608389,0.13905685,0.008364324,0.005524546,0.0013419981,0.00053527736,0.065789826],"genre_scores_gemma":[0.37193274,0.001347361,0.49383417,0.081435755,0.004145532,0.030082218,0.0003330809,0.0002149717,0.016674116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.75816226,0.19680633,0.0067622573,0.019618405,0.017072598,0.001578214],"domain_scores_gemma":[0.71991277,0.21532276,0.008931552,0.036651786,0.017230785,0.0019502379],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.21154882,0.0009886266,0.0027621225,0.0017414959,0.0033819282,0.009240108,0.0031738682,0.007922542,0.007175543],"category_scores_gemma":[0.34439138,0.0011506606,0.0015759524,0.0023712453,0.034475207,0.013674498,0.0044358876,0.015290205,0.002383373],"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":[0.00024584538,0.00004287949,0.000987528,0.00027532564,0.000059749887,0.000052132225,0.0011884016,0.00037573508,0.00018568218,0.9773481,0.0043943534,0.014844244],"study_design_scores_gemma":[0.0002946936,0.00023656162,0.0004909642,0.00039822375,0.00006486586,0.00010632965,0.00036843767,0.0033246388,0.00076034525,0.92961997,0.06426281,0.00007218885],"about_ca_topic_score_codex":0.0019630857,"about_ca_topic_score_gemma":0.0012254361,"teacher_disagreement_score":0.21154882,"about_ca_system_score_codex":0.0054886,"about_ca_system_score_gemma":0.009367539,"threshold_uncertainty_score":0.97230065},"labels":[],"label_agreement":null},{"id":"W2160128373","doi":"10.1093/pan/mpu038","title":"What's in a Name? A Method for Extracting Information about Ethnicity from Names","year":2015,"lang":"en","type":"article","venue":"Political Analysis","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":35,"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; Harvard University; National Science Foundation","keywords":"Ethnic group; Geocoding; Context (archaeology); Identity (music); Linkage (software); Ethnic composition; Group (periodic table); Computer science; Linguistics; Genealogy; Geography; Sociology; History; Anthropology; Cartography","score_opus":0.10946708747816192,"score_gpt":0.4752007986900707,"score_spread":0.3657337112119088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160128373","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":[0.009025994,0.0002961565,0.97942436,0.0009872072,0.00016294334,0.0003246649,0.0015637852,0.0012169374,0.006997911],"genre_scores_gemma":[0.03900772,0.00017830034,0.9534016,0.00015462414,0.00008612517,0.00047876255,0.0009909576,0.00012460983,0.0055772853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599504,0.0017023059,0.0004133555,0.0008608227,0.0008954427,0.00013298148],"domain_scores_gemma":[0.9926662,0.0039913496,0.00078988145,0.0010095091,0.0013724689,0.0001706262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043524313,0.0006577693,0.00052074256,0.007760543,0.0018169632,0.0029023958,0.0011843239,0.0012107297,0.0072957156],"category_scores_gemma":[0.016304925,0.0006376219,0.00077044405,0.0062246923,0.0016201609,0.0041762963,0.0019150977,0.001276989,0.0038290627],"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":[0.00021878895,0.00014939088,0.015648449,0.00034831115,0.0001097114,0.00026075807,0.0035794813,0.0013089658,0.008837441,0.068788394,0.022205599,0.87854475],"study_design_scores_gemma":[0.00026545822,0.0003008242,0.04299284,0.000605257,0.000346235,0.00492447,0.010287059,0.16525665,0.03793709,0.23172124,0.5045703,0.00079262897],"about_ca_topic_score_codex":0.0032810208,"about_ca_topic_score_gemma":0.0067627463,"teacher_disagreement_score":0.007760543,"about_ca_system_score_codex":0.0007075432,"about_ca_system_score_gemma":0.0018138998,"threshold_uncertainty_score":0.024406612},"labels":[],"label_agreement":null},{"id":"W2167579695","doi":"10.1093/pan/mph003","title":"Party System Compactness: Measurement and Consequences","year":2004,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":172,"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":"Compact space; Space (punctuation); Voting; Distribution (mathematics); Metric (unit); Metric space; Set (abstract data type); Computer science; Political science; Mathematics; Business; Law; Politics; Marketing; Discrete mathematics","score_opus":0.09507238852847202,"score_gpt":0.3578511457791606,"score_spread":0.26277875725068855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167579695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85121614,0.0010977605,0.13260786,0.00092488807,0.00004424055,0.00011633722,0.000426438,0.00014851177,0.013417855],"genre_scores_gemma":[0.9894108,0.00010608916,0.009792854,0.00002741226,0.0000397177,0.000059518683,0.00018128759,0.000025459018,0.00035684742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.98307186,0.0095380945,0.0011637027,0.0023128476,0.0032609238,0.0006525537],"domain_scores_gemma":[0.8635387,0.09581562,0.016969116,0.017149784,0.004659774,0.0018669855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011838129,0.0004908162,0.0012692072,0.004439656,0.0023917877,0.0034763985,0.0009379703,0.00094173173,0.004186011],"category_scores_gemma":[0.092721224,0.00072353525,0.0007374697,0.005174833,0.008127284,0.006239384,0.0066439444,0.0016404446,0.00035261773],"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":[0.0010319198,0.00024672074,0.36310497,0.00058734644,0.00057848095,0.0005031582,0.008462783,0.024921764,0.0069958866,0.4569069,0.0016844834,0.13497551],"study_design_scores_gemma":[0.00011087626,0.0006574221,0.38935786,0.00023508775,0.00018035327,0.0015822914,0.0069678915,0.064958505,0.008807098,0.517094,0.009886972,0.0001617006],"about_ca_topic_score_codex":0.0011118392,"about_ca_topic_score_gemma":0.000814085,"teacher_disagreement_score":0.011838129,"about_ca_system_score_codex":0.0014552043,"about_ca_system_score_gemma":0.0004725737,"threshold_uncertainty_score":0.06260675},"labels":[],"label_agreement":null},{"id":"W2323450102","doi":"10.1093/pan/mpt028","title":"A Common Left-Right Scale for Voters and Parties in Europe","year":2013,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":82,"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":"Deutsche Forschungsgemeinschaft; Universität Mannheim; University of Cambridge","keywords":"Dimension (graph theory); Scaling; Survey data collection; Politics; Scale (ratio); Bridging (networking); Left and right; Perception; Multidimensional scaling; Political science; National election; Comparative politics; Econometrics; Computer science; Statistics; Economics; Psychology; Geography; Mathematics; Law","score_opus":0.030185600901857675,"score_gpt":0.33696788002445566,"score_spread":0.306782279122598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323450102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5610531,0.0005640095,0.40175062,0.0007734159,0.00012804498,0.0002928621,0.001131854,0.00030029527,0.034005906],"genre_scores_gemma":[0.9385205,0.000102598475,0.058615874,0.00011862719,0.000058362926,0.00032464985,0.0007678,0.000063855085,0.0014277512],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989296,0.0068196505,0.0004926777,0.0018368833,0.0011810712,0.0003736753],"domain_scores_gemma":[0.9910358,0.0036027366,0.0014905141,0.002541565,0.0010432795,0.00028604484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006957922,0.00044360012,0.00084768,0.0031476393,0.0012139919,0.002482899,0.0007628742,0.00094666425,0.004716692],"category_scores_gemma":[0.03156934,0.00039820207,0.0011363643,0.0029885792,0.0017356129,0.0025354838,0.0038379452,0.0011588443,0.0008633359],"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":[0.00042911727,0.00018454029,0.39020324,0.00028191623,0.00035301287,0.00036200383,0.0108245,0.020965442,0.0056593027,0.2950679,0.005782481,0.26988646],"study_design_scores_gemma":[0.00013211575,0.00043214325,0.5022049,0.0004881894,0.00017275436,0.0005951719,0.01262086,0.13727266,0.004126484,0.27904016,0.062696435,0.00021820441],"about_ca_topic_score_codex":0.0028762275,"about_ca_topic_score_gemma":0.0026658538,"teacher_disagreement_score":0.006957922,"about_ca_system_score_codex":0.0006080699,"about_ca_system_score_gemma":0.00044057876,"threshold_uncertainty_score":0.036797404},"labels":[],"label_agreement":null},{"id":"W2329543604","doi":"10.1093/pan/mps017","title":"The Relationship between Seats and Votes in Multiparty Systems","year":2012,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":42,"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":"Legislature; Swing; Democracy; Representation (politics); Distribution (mathematics); Single non-transferable vote; Political science; Political economy; Politics; Economics; Law; Mathematics","score_opus":0.13637461665846626,"score_gpt":0.41221435510638327,"score_spread":0.275839738447917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329543604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97887254,0.00008300209,0.016795594,0.00017519336,0.000005985607,0.000021043132,0.00018505713,0.000046997455,0.0038146765],"genre_scores_gemma":[0.998459,0.000018551185,0.000832426,0.00000647417,0.0000060761568,0.000010458744,0.00011095653,0.0000087730605,0.00054722925],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99725336,0.0015231606,0.00014720831,0.00038107156,0.0003597993,0.00033543602],"domain_scores_gemma":[0.98041683,0.014080378,0.0026673053,0.001384226,0.000922944,0.0005283196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033256745,0.00017006835,0.0004269679,0.0015467714,0.00088605937,0.0020113296,0.0006644593,0.00049470604,0.006206581],"category_scores_gemma":[0.04065321,0.00034027765,0.00039017847,0.0017426029,0.0014374562,0.0022169624,0.0020606206,0.0008590176,0.00069916225],"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":[0.0006622041,0.0001791164,0.74803114,0.00012652593,0.0002819988,0.00027937765,0.0047189877,0.050278716,0.0039738426,0.10878362,0.0008964468,0.08178803],"study_design_scores_gemma":[0.00005739084,0.0002234041,0.6795557,0.00005142054,0.00009807406,0.00037781955,0.003084697,0.1926741,0.0029886847,0.11696597,0.0038443184,0.000078547215],"about_ca_topic_score_codex":0.0022668939,"about_ca_topic_score_gemma":0.003487936,"teacher_disagreement_score":0.006206581,"about_ca_system_score_codex":0.00069039874,"about_ca_system_score_gemma":0.00025369978,"threshold_uncertainty_score":0.02076304},"labels":[],"label_agreement":null},{"id":"W2590719758","doi":"10.1017/pan.2016.9","title":"Detecting Election Fraud from Irregularities in Vote-Share Distributions","year":2017,"lang":"en","type":"article","venue":"Political Analysis","topic":"Benford’s Law and Fraud Detection","field":"Mathematics","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":"Polling; Measure (data warehouse); Contingent vote; Simple (philosophy); Computer science; Distribution (mathematics); Voting; Econometrics; Group voting ticket; Political science; Economics; Law; Data mining; Mathematics","score_opus":0.04992491476445786,"score_gpt":0.33052635242525696,"score_spread":0.2806014376607991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590719758","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":[0.30777454,0.0003127654,0.68489665,0.0008212463,0.00008437944,0.0002510911,0.0009976078,0.0007966513,0.004065049],"genre_scores_gemma":[0.9052099,0.00007210769,0.09254146,0.0000648568,0.000059862043,0.00010516932,0.0007872128,0.00003424535,0.0011251683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969478,0.001168814,0.00026044325,0.00054284785,0.00083713204,0.0002429416],"domain_scores_gemma":[0.980288,0.009928018,0.0039824885,0.0037198414,0.0017746682,0.00030705787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004069525,0.0003591231,0.00079209934,0.004459598,0.00063779275,0.0013043918,0.0014821346,0.0010136691,0.0016286419],"category_scores_gemma":[0.03787927,0.00030483585,0.00052110106,0.002925231,0.0010148082,0.0014299136,0.0016349272,0.0013077684,0.00063530204],"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":[0.00030585317,0.0003123425,0.39131576,0.00024296035,0.00030347897,0.0004913518,0.0010763861,0.07606196,0.005379157,0.05613529,0.00793116,0.4604444],"study_design_scores_gemma":[0.000033359687,0.000059524264,0.06356323,0.00004573456,0.000027981518,0.000599458,0.00024936895,0.89101833,0.0046488037,0.033840984,0.0058629713,0.000050254454],"about_ca_topic_score_codex":0.0038520982,"about_ca_topic_score_gemma":0.003382894,"teacher_disagreement_score":0.004459598,"about_ca_system_score_codex":0.0010261309,"about_ca_system_score_gemma":0.0010716721,"threshold_uncertainty_score":0.021521926},"labels":[],"label_agreement":null},{"id":"W2607179242","doi":"10.1017/pan.2017.8","title":"The Statistical Analysis of Misreporting on Sensitive Survey Questions","year":2017,"lang":"en","type":"article","venue":"Political Analysis","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":31,"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); Variety (cybernetics); Prejudice (legal term); Computer science; Survey data collection; Ideology; Multivariate statistics; Scale (ratio); Social psychology; Psychology; Econometrics; Statistics; Political science; Artificial intelligence; Mathematics; Law; Machine learning","score_opus":0.17872944379696518,"score_gpt":0.4678313315530983,"score_spread":0.2891018877561331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607179242","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":[0.20937382,0.00081229844,0.7755573,0.0017928133,0.0004993246,0.0022087975,0.0018753025,0.0005493357,0.0073309885],"genre_scores_gemma":[0.87017006,0.00033358275,0.121967375,0.0009327577,0.0004195623,0.0038358397,0.001137644,0.000119213495,0.0010839695],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.5225875,0.4142076,0.015143766,0.019016221,0.02737989,0.001664984],"domain_scores_gemma":[0.19567905,0.66093045,0.056218177,0.07481265,0.011832212,0.0005274954],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.27638167,0.0010433125,0.0018726136,0.0062360205,0.0015613113,0.0025955315,0.001963381,0.0015263974,0.0037244537],"category_scores_gemma":[0.6286774,0.0008743998,0.0022312626,0.0071655116,0.0077264295,0.0042863493,0.0044953465,0.0028934428,0.0007949015],"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":[0.0015639793,0.00046896902,0.3387244,0.0030498067,0.0057106493,0.000495424,0.017574862,0.013594179,0.0033055984,0.24545808,0.015339516,0.35471457],"study_design_scores_gemma":[0.00035131056,0.0020226699,0.30310443,0.0017904078,0.0021621562,0.001419,0.007992398,0.20071599,0.01716176,0.4177391,0.04512724,0.00041355455],"about_ca_topic_score_codex":0.0011759738,"about_ca_topic_score_gemma":0.00066198665,"teacher_disagreement_score":0.7236183,"about_ca_system_score_codex":0.0018422692,"about_ca_system_score_gemma":0.0018654678,"threshold_uncertainty_score":0.89235026},"labels":[],"label_agreement":null},{"id":"W2789931592","doi":"10.1017/pan.2017.43","title":"When Can Multiple Imputation Improve Regression Estimates?","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","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":"McGill University; Université de Montréal","funders":"","keywords":"Missing data; Imputation (statistics); Estimator; Regression; Computer science; Regression analysis; Statistics; Panacea (medicine); Meta-regression; Econometrics; Data mining; Meta-analysis; Mathematics; Machine learning; Medicine","score_opus":0.05043689094324021,"score_gpt":0.39517078885719203,"score_spread":0.3447338979139518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789931592","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":[0.0042425506,0.01949581,0.8646559,0.10145328,0.0029671069,0.00015337202,0.0006107729,0.0009904383,0.0054306956],"genre_scores_gemma":[0.1399248,0.01906512,0.80466163,0.023295145,0.006288103,0.00093637255,0.0008249125,0.0016176737,0.003386238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.7756833,0.19439305,0.006264934,0.009807569,0.01206251,0.0017887008],"domain_scores_gemma":[0.45939416,0.46400124,0.016984278,0.039767668,0.018162346,0.0016903337],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.20853336,0.00189335,0.005010674,0.0032316025,0.0017194746,0.009277209,0.0046982137,0.006181224,0.008532705],"category_scores_gemma":[0.66680753,0.0021815877,0.0038235046,0.0064078514,0.004950867,0.017409647,0.0073561426,0.012563979,0.005601751],"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":[0.00048679547,0.00018524662,0.016934402,0.0035545311,0.0032156487,0.0003190699,0.0037343116,0.0147997355,0.00068597955,0.29267958,0.061911933,0.60149276],"study_design_scores_gemma":[0.00021550417,0.00023809749,0.0054860646,0.0040663294,0.0006958769,0.00032053856,0.0009257774,0.035051543,0.0014032656,0.8602237,0.09112984,0.00024343417],"about_ca_topic_score_codex":0.004246216,"about_ca_topic_score_gemma":0.0045290915,"teacher_disagreement_score":0.79146665,"about_ca_system_score_codex":0.002235565,"about_ca_system_score_gemma":0.005325912,"threshold_uncertainty_score":0.97601926},"labels":[],"label_agreement":null},{"id":"W2886105060","doi":"10.1017/pan.2018.12","title":"Gendered Citation Patterns across Political Science and Social Science Methodology Fields","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":550,"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":"University of Cambridge","keywords":"Citation; Gender gap; Politics; Publication; Field (mathematics); Sociology; Social science; Gender studies; Political science; Demographic economics; Law","score_opus":0.7631996924929744,"score_gpt":0.6632523770784542,"score_spread":0.09994731541452018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886105060","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":[0.95260954,0.015139856,0.002838696,0.0023447468,0.000222157,0.000083945386,0.004828881,0.000107973916,0.021824297],"genre_scores_gemma":[0.98815656,0.0030255974,0.001421092,0.00023929618,0.00020841091,0.00007443689,0.002592058,0.00006391247,0.0042186957],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9889402,0.0025505754,0.0018259458,0.0016345289,0.0040759905,0.0009728789],"domain_scores_gemma":[0.8731368,0.062822014,0.026398592,0.00653233,0.027338697,0.0037714394],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.011473426,0.00029209265,0.00072366867,0.02032523,0.0010957437,0.0030604894,0.0008096595,0.0008195349,0.0056504486],"category_scores_gemma":[0.08999514,0.0002519291,0.0006383446,0.022967,0.001282634,0.0033045104,0.0027770174,0.0004759262,0.0014502082],"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":[0.00048814696,0.000076665456,0.8361839,0.001559638,0.00037452864,0.00045416696,0.022659881,0.00047727814,0.0038966676,0.015361723,0.006246523,0.112220846],"study_design_scores_gemma":[0.000025587316,0.00014929655,0.94802254,0.0006818757,0.00012596727,0.0007533218,0.01022582,0.00087687315,0.0021562409,0.01105881,0.0258733,0.000050362483],"about_ca_topic_score_codex":0.002861282,"about_ca_topic_score_gemma":0.0052390657,"teacher_disagreement_score":0.9885266,"about_ca_system_score_codex":0.0012714439,"about_ca_system_score_gemma":0.0014571588,"threshold_uncertainty_score":0.060678005},"labels":[],"label_agreement":null},{"id":"W2889291357","doi":"10.1017/pan.2018.30","title":"Ideological Scaling of Social Media Users: A Dynamic Lexicon Approach","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":20,"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; Université Laval","funders":"University of Cambridge; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Compute Canada; Université Laval","keywords":"Ideology; Social media; Voting behavior; Lexicon; Politics; Voting; Dimension (graph theory); Rhetoric; Sociology; Computer science; Political science; Linguistics; Artificial intelligence; Law; Mathematics; World Wide Web","score_opus":0.06638905123520183,"score_gpt":0.4087408830263073,"score_spread":0.34235183179110545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889291357","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":[0.3650187,0.00047788056,0.60438067,0.0012836664,0.00010892355,0.0006709094,0.0030793115,0.0006060119,0.024373896],"genre_scores_gemma":[0.8863655,0.00014607617,0.10949672,0.000094662864,0.000100083606,0.0006667295,0.0016891139,0.00009868506,0.0013425193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956117,0.002326533,0.00030332527,0.0009441947,0.0006386566,0.0001755778],"domain_scores_gemma":[0.98153025,0.013922385,0.0015937634,0.0016322875,0.001040803,0.00028058985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038665836,0.0005807362,0.00082382717,0.008680145,0.0013859529,0.0048352643,0.0011292418,0.0008682817,0.0031528957],"category_scores_gemma":[0.03665362,0.0005149004,0.00096833706,0.006090068,0.0019175758,0.005175007,0.0030136383,0.0010970529,0.0009227694],"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":[0.0005615852,0.00085816404,0.13616827,0.00059090875,0.0005833551,0.0006411146,0.020423194,0.02346293,0.010087119,0.29247984,0.006342283,0.5078012],"study_design_scores_gemma":[0.00008528243,0.00014801722,0.07080494,0.00015915559,0.00014138337,0.00042759028,0.00760382,0.55733764,0.0026335288,0.34724712,0.013264795,0.00014670703],"about_ca_topic_score_codex":0.002407346,"about_ca_topic_score_gemma":0.0027086833,"teacher_disagreement_score":0.008680145,"about_ca_system_score_codex":0.0013095596,"about_ca_system_score_gemma":0.00085068,"threshold_uncertainty_score":0.020448685},"labels":[],"label_agreement":null},{"id":"W2899570500","doi":"10.1017/pan.2018.53","title":"A Regression-with-Residuals Method for Estimating Controlled Direct Effects","year":2018,"lang":"en","type":"article","venue":"Political Analysis","topic":"Advanced Causal Inference Techniques","field":"Mathematics","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":"University of Toronto","funders":"","keywords":"Moderation; Respondent; Econometrics; Regression analysis; Regression; Causal inference; Confounding; Estimation; Mediation; Statistics; Outcome (game theory); Psychology; Computer science; Social psychology; Mathematics; Economics; Political science","score_opus":0.07879243387603321,"score_gpt":0.4727019433909369,"score_spread":0.39390950951490367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899570500","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":[0.00081846584,0.0001399465,0.9978224,0.00013768252,0.000043117307,0.00009333388,0.000116497366,0.0002229722,0.0006056393],"genre_scores_gemma":[0.04243194,0.00047753894,0.952587,0.00021670862,0.00015201926,0.00097070675,0.00047612423,0.00028760007,0.0024004222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97769755,0.017505884,0.0006587212,0.0020958146,0.0017275464,0.0003144758],"domain_scores_gemma":[0.9559105,0.03486425,0.0019698332,0.0052462476,0.0018196779,0.0001894577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026687995,0.0022302128,0.0019069323,0.0036376626,0.00071372563,0.0016349023,0.00395595,0.002027147,0.014005141],"category_scores_gemma":[0.102502644,0.0008545191,0.003212156,0.0054244655,0.0019085575,0.0033398422,0.0024782019,0.0047146576,0.003344693],"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":[0.00012805976,0.00017069027,0.004751493,0.00056467176,0.0006166609,0.00018744942,0.0005415826,0.03704831,0.001375843,0.70181954,0.005641825,0.24715392],"study_design_scores_gemma":[0.00018309367,0.00034740003,0.0033661877,0.00024331664,0.0003417761,0.00024133497,0.00022496791,0.3923951,0.0024557593,0.5555339,0.044526134,0.00014100969],"about_ca_topic_score_codex":0.007366481,"about_ca_topic_score_gemma":0.0055037723,"teacher_disagreement_score":0.026687995,"about_ca_system_score_codex":0.0010493496,"about_ca_system_score_gemma":0.00287208,"threshold_uncertainty_score":0.1411413},"labels":[],"label_agreement":null},{"id":"W2954211942","doi":"10.1017/pan.2019.26","title":"Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora","year":2019,"lang":"en","type":"article","venue":"Political Analysis","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":165,"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":"Ideology; Metadata; Parliament; Computer science; Context (archaeology); Politics; Word (group theory); Natural language processing; Language model; Linguistics; Artificial intelligence; Data science; Sociology; Political science; World Wide Web; Law; History","score_opus":0.0491700587411518,"score_gpt":0.39750142558781854,"score_spread":0.3483313668466667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954211942","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":[0.14657316,0.000847662,0.8410559,0.00083020155,0.00019273818,0.0001797388,0.0044316407,0.0017764557,0.0041124164],"genre_scores_gemma":[0.6527936,0.0005202238,0.3331651,0.00012344691,0.00012443267,0.0007691974,0.009010492,0.00044969225,0.0030437435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807966,0.001132824,0.00015122928,0.0003352782,0.00022846964,0.000072592644],"domain_scores_gemma":[0.99116313,0.0060902974,0.00089387456,0.0011332431,0.0005753563,0.00014417733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029812737,0.00076236215,0.00040668374,0.0029551787,0.00082576997,0.0018701036,0.0007007482,0.0008099245,0.003794079],"category_scores_gemma":[0.022395846,0.0003959334,0.0007372224,0.004168221,0.0009957369,0.004091859,0.0016634316,0.002218788,0.0017810964],"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":[0.00049577473,0.0005202418,0.042878926,0.0007385371,0.00044393982,0.0002946585,0.0030602592,0.15314955,0.010851418,0.11013558,0.0191697,0.65826136],"study_design_scores_gemma":[0.000034421773,0.00012396515,0.011118895,0.000119819524,0.00004789968,0.00017866972,0.0007654727,0.84453577,0.0028899373,0.12703583,0.013091756,0.000057522615],"about_ca_topic_score_codex":0.0025360833,"about_ca_topic_score_gemma":0.005868912,"teacher_disagreement_score":0.003794079,"about_ca_system_score_codex":0.00089287077,"about_ca_system_score_gemma":0.0007929655,"threshold_uncertainty_score":0.01576668},"labels":[],"label_agreement":null},{"id":"W3021168907","doi":"10.1093/pan/mpt011","title":"Measuring Unemployment Insurance Generosity","year":2013,"lang":"en","type":"article","venue":"Political Analysis","topic":"Employment and Welfare Studies","field":"Health Professions","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 du Québec à Montréal","funders":"","keywords":"Generosity; Unemployment; Asset (computer security); Duration (music); Economics; Metric (unit); Market liquidity; Actuarial science; Labour economics; Macroeconomics; Computer science; Operations management","score_opus":0.10380383236956467,"score_gpt":0.37993179762658447,"score_spread":0.27612796525701977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021168907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899136,0.00015166726,0.0051130843,0.0001831401,0.000006801301,0.000027638473,0.00040751998,0.000012453098,0.0041840565],"genre_scores_gemma":[0.9978654,0.00004983978,0.0016226458,0.00001772042,0.0000067509627,0.00001867317,0.00015848326,0.0000013778683,0.00025902878],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982817,0.000943635,0.00013730889,0.0001378321,0.0003424894,0.00015705143],"domain_scores_gemma":[0.99291056,0.0033951348,0.0023975442,0.0004949211,0.0003785434,0.00042319673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002506233,0.00018421089,0.00029320281,0.0023053929,0.0003954323,0.000852282,0.00034592632,0.0005855751,0.0023483392],"category_scores_gemma":[0.015383265,0.00011611405,0.00027386262,0.001424108,0.00046396264,0.00077717047,0.0014310217,0.00034818312,0.00015389515],"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":[0.0003206782,0.00021567108,0.91134715,0.00009022941,0.00029292773,0.00008062852,0.0015156602,0.01548267,0.002281183,0.017141653,0.00073661614,0.050495025],"study_design_scores_gemma":[0.000020049165,0.00045896912,0.9406921,0.00010455706,0.00012250147,0.00031239277,0.0028062535,0.039018072,0.0017927936,0.011437613,0.0031719469,0.00006274258],"about_ca_topic_score_codex":0.002054641,"about_ca_topic_score_gemma":0.0031826922,"teacher_disagreement_score":0.002506233,"about_ca_system_score_codex":0.00071572134,"about_ca_system_score_gemma":0.0003167436,"threshold_uncertainty_score":0.013254344},"labels":[],"label_agreement":null},{"id":"W3121207479","doi":"10.1093/pan/mpn007","title":"Lexical Cohesion Analysis of Political Speech","year":2008,"lang":"en","type":"article","venue":"Political Analysis","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":70,"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":"Cohesion (chemistry); Rhetorical question; Computer science; Linguistics; Annotation; Natural language processing; Lexical density; Representation (politics); Politics; Interpretation (philosophy); Artificial intelligence; Lexical item; Political science","score_opus":0.021939312034272573,"score_gpt":0.3055167210051506,"score_spread":0.283577408970878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121207479","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":[0.40916166,0.0014216729,0.5642508,0.00065145193,0.00019583876,0.00039161943,0.0021201256,0.0020494591,0.019757338],"genre_scores_gemma":[0.8546861,0.0002940116,0.1404442,0.000052419186,0.00014065477,0.00028889644,0.001695412,0.00028599644,0.0021122545],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9975199,0.0010566231,0.0001820301,0.00048109828,0.0006519725,0.00010830949],"domain_scores_gemma":[0.9899714,0.0064846887,0.0013023039,0.00064115913,0.0014573452,0.00014314573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013599945,0.00032369007,0.00046583675,0.007816614,0.0013747966,0.0021022223,0.00059430033,0.0004351572,0.0026675786],"category_scores_gemma":[0.013281947,0.00024215248,0.00034039916,0.0034980506,0.0010316711,0.0024926222,0.0013820246,0.00065111835,0.00068506785],"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":[0.00064572704,0.00015758154,0.031676102,0.001569755,0.0002234356,0.001058562,0.03187554,0.0060863052,0.14193274,0.08701584,0.009741051,0.6880173],"study_design_scores_gemma":[0.00015523435,0.0004701508,0.2110816,0.00061166176,0.00046260917,0.0022416872,0.02651807,0.27297094,0.14091678,0.18026294,0.16385898,0.0004493843],"about_ca_topic_score_codex":0.0012846568,"about_ca_topic_score_gemma":0.00158943,"teacher_disagreement_score":0.007816614,"about_ca_system_score_codex":0.00056143914,"about_ca_system_score_gemma":0.00062893174,"threshold_uncertainty_score":0.008923948},"labels":[],"label_agreement":null},{"id":"W3168278108","doi":"10.1017/pan.2021.37","title":"Cross-Domain Topic Classification for Political Texts","year":2021,"lang":"en","type":"article","venue":"Political Analysis","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":47,"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":"H2020 European Research Council; University of Essex; Università Bocconi; Eidgenössische Technische Hochschule Zürich; Wissenschaftszentrum Berlin für Sozialforschung; York University; London School of Economics and Political Science","keywords":"Computer science; Classifier (UML); Domain (mathematical analysis); Artificial intelligence; Natural language processing; Labeled data; Machine learning; Mathematics","score_opus":0.08141688647427724,"score_gpt":0.46420526801067685,"score_spread":0.3827883815363996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168278108","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":[0.3617499,0.002473823,0.6189905,0.0007665396,0.0003785077,0.0006810228,0.0026359626,0.0034607335,0.008863056],"genre_scores_gemma":[0.8198316,0.00031054844,0.17019422,0.000113159935,0.00023699296,0.00043221263,0.0062342165,0.00024522585,0.0024018434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9903688,0.0056528584,0.00056499935,0.0018368787,0.0011742477,0.00040218173],"domain_scores_gemma":[0.9542038,0.034412105,0.0022740709,0.0039718486,0.0043877466,0.0007503316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016094046,0.0011365734,0.0011051217,0.008384028,0.0016346251,0.003365829,0.0012400723,0.0016868294,0.002248029],"category_scores_gemma":[0.03532381,0.00036388586,0.0012518055,0.004605992,0.0011231225,0.0030000785,0.002468018,0.002351138,0.0016114058],"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":[0.0018043197,0.001544241,0.08518912,0.0009282091,0.0008309933,0.00053307874,0.0032335203,0.14251564,0.01454544,0.015480453,0.016773254,0.7166218],"study_design_scores_gemma":[0.000051325234,0.00014124278,0.015383093,0.00006976205,0.00006134583,0.00020761561,0.00086624554,0.9575075,0.007924411,0.0116162645,0.0061190506,0.000052155967],"about_ca_topic_score_codex":0.002408859,"about_ca_topic_score_gemma":0.0024083434,"teacher_disagreement_score":0.016094046,"about_ca_system_score_codex":0.0014657484,"about_ca_system_score_gemma":0.00095501775,"threshold_uncertainty_score":0.08511448},"labels":[],"label_agreement":null},{"id":"W3172401221","doi":"10.1017/pan.2021.15","title":"Multi-Label Prediction for Political Text-as-Data","year":2021,"lang":"en","type":"article","venue":"Political Analysis","topic":"Topic Modeling","field":"Computer Science","cited_by":9,"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; Centre for Social Innovation","funders":"Compute Canada; National Science Foundation","keywords":"Computer science; Machine learning; Artificial intelligence; Code (set theory); Set (abstract data type); Supervised learning; Training set; Source code; Government (linguistics); Data set; Association (psychology); Natural language processing; Artificial neural network; Psychology","score_opus":0.0953883885612755,"score_gpt":0.35060281141808164,"score_spread":0.2552144228568061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172401221","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":[0.48619235,0.0023071982,0.48628315,0.0055090063,0.0009017647,0.00031570622,0.008015973,0.0058767158,0.004598022],"genre_scores_gemma":[0.90976506,0.00018247019,0.082297616,0.0002096775,0.00040173184,0.0001532105,0.0058710864,0.0001488445,0.00097033434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948737,0.0030751047,0.00018406127,0.0009607768,0.0006434192,0.00026299513],"domain_scores_gemma":[0.93579805,0.05084828,0.0042364253,0.0040585254,0.004091955,0.00096666324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008189013,0.000898994,0.0008082141,0.0039976877,0.0011733538,0.0018360504,0.0014283261,0.0015025144,0.002349821],"category_scores_gemma":[0.037890643,0.0002976839,0.0006366962,0.0038071391,0.00083386333,0.0033324647,0.0013857464,0.003500791,0.0014758088],"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":[0.0010158474,0.0015153427,0.15514117,0.0005143407,0.00036351182,0.00048894476,0.0009439923,0.4039959,0.0049028606,0.019041872,0.04565397,0.36642224],"study_design_scores_gemma":[0.000008575915,0.000014952552,0.0030877914,0.000017473752,0.000006404688,0.0000144748155,0.00006177874,0.9851732,0.0007455895,0.009823857,0.0010364015,0.000009480222],"about_ca_topic_score_codex":0.005781762,"about_ca_topic_score_gemma":0.007544234,"teacher_disagreement_score":0.008189013,"about_ca_system_score_codex":0.0012251548,"about_ca_system_score_gemma":0.0008982957,"threshold_uncertainty_score":0.04330814},"labels":[],"label_agreement":null},{"id":"W4224235445","doi":"10.1017/pan.2022.4","title":"Balance as a Pre-Estimation Test for Time Series Analysis","year":2022,"lang":"en","type":"article","venue":"Political Analysis","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","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":"Balance (ability); Econometrics; Series (stratigraphy); Interpretation (philosophy); Estimation; Balance theory; Balance test; Computer science; Balance equation; Detailed balance; Empirical research; Time series; Test (biology); Mathematics; Statistics; Economics; Psychology; Statistical physics; Social psychology","score_opus":0.02767210004469906,"score_gpt":0.24458149616656072,"score_spread":0.21690939612186166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224235445","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":[0.19442673,0.0007838345,0.77989274,0.004284504,0.0005139472,0.00028126236,0.0012349181,0.0009582341,0.0176238],"genre_scores_gemma":[0.94941354,0.0001598025,0.046286516,0.00048118905,0.0002469564,0.00039157775,0.0010464932,0.00017496472,0.0017989924],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96558917,0.024646653,0.0022571278,0.0026354394,0.0039079194,0.0009636944],"domain_scores_gemma":[0.6462056,0.31666118,0.01436702,0.010149747,0.010892713,0.0017237323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03827191,0.00059114606,0.0016325782,0.004827625,0.0010865239,0.0029943816,0.0014633916,0.0021979378,0.0106315445],"category_scores_gemma":[0.28543377,0.0005082437,0.0012455834,0.0037818,0.003926563,0.0078443615,0.0031496766,0.0029683968,0.0010729326],"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":[0.0015461895,0.0004420551,0.20354393,0.0007139577,0.0011911892,0.0011783404,0.0025520583,0.05438928,0.0046148584,0.5354878,0.011471052,0.1828693],"study_design_scores_gemma":[0.0001813227,0.0010335419,0.047829103,0.00038936408,0.00019580183,0.00040731987,0.0011243722,0.3507369,0.005541608,0.5795937,0.01282802,0.00013889256],"about_ca_topic_score_codex":0.0014177245,"about_ca_topic_score_gemma":0.00049665524,"teacher_disagreement_score":0.03827191,"about_ca_system_score_codex":0.0011364266,"about_ca_system_score_gemma":0.0014914974,"threshold_uncertainty_score":0.20240366},"labels":[],"label_agreement":null},{"id":"W4246729553","doi":"10.1017/s104719870000663x","title":"PAN volume 15 issue 4 Cover and Front matter","year":2007,"lang":"en","type":"article","venue":"Political Analysis","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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 California, Los Angeles; Universiteit van Amsterdam; Northwestern University; University of Warwick; Michigan State University; University of Rochester; University of Pittsburgh; University of Oxford; College of Engineering, Michigan State University; Harvard University; California Institute of Technology; York University; University of Cambridge; Yale University","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Environmental science; Oceanography; Geology; Physics; Engineering; Thermodynamics; Mechanical engineering","score_opus":0.019737274494080557,"score_gpt":0.21826491343602697,"score_spread":0.19852763894194642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246729553","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":[0.002025229,0.0029689197,0.0007198887,0.003848188,0.014878118,0.00008015751,0.00093399873,0.00066307595,0.9738824],"genre_scores_gemma":[0.0033448956,0.0009577416,0.00020526256,0.00054353726,0.0024175383,0.000018281336,0.0003840649,0.0002077804,0.9919208],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996444,0.000030286656,0.000013072998,0.00010326597,0.00015395919,0.000054940465],"domain_scores_gemma":[0.99936193,0.00010066534,0.000030991396,0.00008923534,0.00019829167,0.00021901194],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044323958,0.0010208582,0.0010340258,0.0018736573,0.0016118116,0.004617411,0.00066549645,0.0021976065,0.76606387],"category_scores_gemma":[0.0009901861,0.0003404639,0.00077815156,0.0015101747,0.0006900948,0.0022868859,0.0015746416,0.0014053482,0.58631855],"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":[0.00010826672,0.00011257099,0.00029154978,0.00023775616,0.000013130937,0.00008945205,0.00003419582,0.00008323055,0.0017363554,0.006853614,0.87713045,0.11330947],"study_design_scores_gemma":[0.000009282742,0.000032076096,0.0009348912,0.0000603345,0.0000064128394,0.00007426927,0.000029504014,0.00010342793,0.00040930283,0.0014692427,0.9968676,0.0000037551015],"about_ca_topic_score_codex":0.0006054236,"about_ca_topic_score_gemma":0.001727781,"teacher_disagreement_score":0.23393613,"about_ca_system_score_codex":0.0008536291,"about_ca_system_score_gemma":0.00092187925,"threshold_uncertainty_score":0.33368146},"labels":[],"label_agreement":null},{"id":"W4249156404","doi":"10.1017/s1047198700006914","title":"PAN volume 16 issue 3 Cover and Front matter","year":2008,"lang":"en","type":"article","venue":"Political Analysis","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","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 Illinois at Urbana-Champaign; Universität Mannheim; Simon Fraser University; University of Warwick; York University; University of California, Los Angeles; University of South Carolina; University of Rochester; Michigan State University; College of Engineering, Michigan State University; Harvard University; California Institute of Technology; Ohio State University; Princeton University; University of Essex; University of Washington; Massachusetts Institute of Technology; Yale University","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Computer science; Action (physics); Content (measure theory); Environmental science; Meteorology; Mathematics; Physics; Engineering; Mechanical engineering; Thermodynamics","score_opus":0.025801713557422862,"score_gpt":0.20479136569643924,"score_spread":0.17898965213901638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249156404","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":[0.0011682401,0.0022890323,0.0006237629,0.0032252113,0.011088515,0.00008797955,0.0012287903,0.0010511099,0.9792373],"genre_scores_gemma":[0.002441642,0.001028482,0.00028754005,0.00083644787,0.001648176,0.00002308389,0.0006518144,0.0002687555,0.9928141],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961543,0.000026862335,0.00001608633,0.00010091843,0.00019468793,0.00004598337],"domain_scores_gemma":[0.9991448,0.00009474518,0.00003229462,0.00013782212,0.0003455236,0.000244841],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005021196,0.0007822739,0.00070210686,0.0014648793,0.0012755807,0.0046409094,0.0006124758,0.0018273304,0.8219956],"category_scores_gemma":[0.0013538335,0.00027982946,0.0005422302,0.0012028975,0.00053044304,0.0020474223,0.0014703611,0.0013124289,0.7157799],"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":[0.00005363991,0.000060530063,0.00028690562,0.00018030278,0.000007639128,0.00007968267,0.00002720753,0.000039660332,0.0012695447,0.0026850784,0.8904586,0.10485128],"study_design_scores_gemma":[0.0000044114386,0.000013251676,0.0006679105,0.000055242283,0.000002757119,0.00006465392,0.000020412881,0.000042344298,0.00024633546,0.00048238307,0.9983979,0.0000023760147],"about_ca_topic_score_codex":0.0009118049,"about_ca_topic_score_gemma":0.0024433392,"teacher_disagreement_score":0.8219956,"about_ca_system_score_codex":0.0007986139,"about_ca_system_score_gemma":0.0010494445,"threshold_uncertainty_score":0.2539016},"labels":[],"label_agreement":null},{"id":"W4251568317","doi":"10.1093/pan/mpu001","title":"Measuring Transparency","year":2014,"lang":"en","type":"article","venue":"Political Analysis","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":211,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Transparency (behavior); Newspaper; Accountability; Index (typography); Construct (python library); Autocracy; Computer science; Business; Political science; Economics; Econometrics; Democracy; Advertising; Computer security; Law","score_opus":0.07131615392218915,"score_gpt":0.30180404825674334,"score_spread":0.2304878943345542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251568317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44896054,0.0028889826,0.099670805,0.008318465,0.00060851005,0.0020117,0.012086345,0.00079159025,0.424663],"genre_scores_gemma":[0.9590645,0.0009527309,0.028597675,0.00078330917,0.00018626661,0.00069826364,0.0026785084,0.00007366463,0.0069651185],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97880894,0.007963083,0.0025527058,0.0014378895,0.008402723,0.00083471334],"domain_scores_gemma":[0.9049217,0.041849118,0.026272066,0.008081581,0.016687103,0.0021883831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012510276,0.00042960656,0.00035971915,0.0051585515,0.0012664234,0.0032828269,0.0005658428,0.00089100405,0.011846594],"category_scores_gemma":[0.0806505,0.0001972685,0.00057301007,0.005072367,0.0013808596,0.0038942196,0.0029048782,0.0013069336,0.002056153],"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":[0.00024686512,0.00047917385,0.3877355,0.0012857977,0.00045799025,0.00019436852,0.014422633,0.0043766736,0.0037330645,0.15788756,0.040848177,0.38833213],"study_design_scores_gemma":[0.00008301284,0.00077148393,0.56722367,0.0018077174,0.0002729914,0.0010436525,0.014972411,0.00855126,0.007706042,0.13957073,0.25768605,0.0003109835],"about_ca_topic_score_codex":0.0017823152,"about_ca_topic_score_gemma":0.0017705198,"teacher_disagreement_score":0.012510276,"about_ca_system_score_codex":0.0021707646,"about_ca_system_score_gemma":0.0025194786,"threshold_uncertainty_score":0.066161394},"labels":[],"label_agreement":null},{"id":"W4311478748","doi":"10.1017/pan.2022.27","title":"Bridging the Grade Gap: Reducing Assessment Bias in a Multi-Grader Class","year":2022,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","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; American Political Science Association","keywords":"Bridging (networking); Grading (engineering); Interpersonal communication; Computer science; Mathematics education; Class (philosophy); Best practice; Survey data collection; Differential (mechanical device); Psychology; Social psychology; Statistics; Mathematics; Political science; Artificial intelligence","score_opus":0.20146320740539428,"score_gpt":0.44198460399315764,"score_spread":0.24052139658776336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311478748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8808134,0.0003880009,0.09218893,0.004111688,0.0006164366,0.0028737914,0.0006606604,0.0014163938,0.01693073],"genre_scores_gemma":[0.9598503,0.000046273002,0.034424324,0.0005848538,0.00013172753,0.0020580955,0.00023713104,0.00018308224,0.0024842245],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.84961146,0.096029386,0.010170456,0.013040556,0.026661728,0.0044863606],"domain_scores_gemma":[0.49836564,0.2623565,0.053194694,0.09338865,0.08082102,0.011873564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17948829,0.00084508077,0.0022093556,0.005309641,0.0039120577,0.0064860317,0.004474999,0.0022960017,0.008364282],"category_scores_gemma":[0.4621571,0.0010107409,0.0010598772,0.002876188,0.0020944064,0.003985193,0.00917146,0.0026210572,0.0032191514],"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":[0.0023270855,0.0021890425,0.4850753,0.00046702308,0.0003665479,0.00052380125,0.047641683,0.001657292,0.0072753336,0.00715774,0.01906938,0.42624974],"study_design_scores_gemma":[0.00055613474,0.0038604804,0.81761074,0.0015224817,0.00049092254,0.0009508898,0.024953438,0.039554924,0.028163472,0.033231176,0.04860606,0.0004992487],"about_ca_topic_score_codex":0.0026217727,"about_ca_topic_score_gemma":0.0050107646,"teacher_disagreement_score":0.17948829,"about_ca_system_score_codex":0.002548046,"about_ca_system_score_gemma":0.0028407262,"threshold_uncertainty_score":0.94923615},"labels":[],"label_agreement":null},{"id":"W4391647286","doi":"10.1017/pan.2023.44","title":"Making a Difference: The Consequences of Electoral Experiments","year":2024,"lang":"en","type":"article","venue":"Political Analysis","topic":"Electoral Systems and Political Participation","field":"Social Sciences","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":"York University; American Political Science Association; National Science Foundation","keywords":"Aggregate (composite); Psychological intervention; Computer science; Affect (linguistics); External validity; Internal validity; Statistical power; Upper and lower bounds; Econometrics; Power (physics); Economics; Social psychology; Psychology; Mathematics; Statistics","score_opus":0.13593031225856952,"score_gpt":0.45492309002168674,"score_spread":0.3189927777631172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391647286","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":[0.16588202,0.00078839937,0.73990256,0.018447798,0.000720763,0.0027983582,0.00029207207,0.00022582446,0.07094225],"genre_scores_gemma":[0.86484337,0.0002975541,0.12349076,0.0039232476,0.00031436415,0.0042513064,0.00006342685,0.00007030267,0.0027457126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"randomized_trial","domain_scores_codex":[0.71126884,0.25133532,0.0074714976,0.010686898,0.01671547,0.0025219708],"domain_scores_gemma":[0.24564685,0.6763765,0.025749216,0.042903442,0.007515046,0.0018089925],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23452294,0.0010007508,0.0022084739,0.0012369928,0.0024551116,0.005683541,0.00266593,0.0047262963,0.0092215],"category_scores_gemma":[0.5008982,0.00091406005,0.0016266891,0.0009796568,0.023256198,0.0077761416,0.0059345844,0.007558897,0.0006803536],"study_design_candidate":"randomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011384893,0.00061069435,0.0055936505,0.00041198565,0.00028556358,0.00016094341,0.0014874331,0.012625818,0.0018788981,0.94179136,0.0011591695,0.032855902],"study_design_scores_gemma":[0.0005306725,0.0009516993,0.002711228,0.00020075185,0.00011203026,0.00008864969,0.00037949512,0.022181412,0.0026488646,0.96604973,0.004093046,0.00005230476],"about_ca_topic_score_codex":0.00037128915,"about_ca_topic_score_gemma":0.00039477897,"teacher_disagreement_score":0.23452294,"about_ca_system_score_codex":0.002429743,"about_ca_system_score_gemma":0.0035848764,"threshold_uncertainty_score":0.9439695},"labels":[],"label_agreement":null},{"id":"W4394867428","doi":"10.1017/pan.2025.10007","title":"Measuring the Quality of Answers in Political Q&As with Large Language Models","year":2025,"lang":"en","type":"preprint","venue":"Political Analysis","topic":"Expert finding and Q&A systems","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":"Operationalization; Quality (philosophy); Parallels; House of Commons; Context (archaeology); Relevance (law); Computer science; Period (music); Politics; Measure (data warehouse); Parliament; Epistemology; Political science; Data mining; Law; History","score_opus":0.07669152858246686,"score_gpt":0.35596429444763544,"score_spread":0.2792727658651686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394867428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7262837,0.0010023243,0.26320902,0.002133145,0.00006289796,0.00015787443,0.0013748433,0.0016382943,0.0041379374],"genre_scores_gemma":[0.9770666,0.000051669485,0.021509375,0.00009371736,0.000035787198,0.000038207956,0.00072014617,0.000057413192,0.00042716064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98610145,0.0095593715,0.000566242,0.0014058208,0.0019220242,0.00044520944],"domain_scores_gemma":[0.88468456,0.09736057,0.006502366,0.005658692,0.0045942236,0.0011995616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017960161,0.00062510563,0.0010864364,0.0040925276,0.0008363174,0.0038577067,0.0012621417,0.0019937379,0.0016044062],"category_scores_gemma":[0.09132938,0.0004511921,0.0007200701,0.0030574037,0.0016272787,0.0048555876,0.0021983616,0.0017975159,0.0005509844],"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":[0.002128265,0.0008744613,0.1767652,0.00052179385,0.00094717427,0.00037524273,0.003968923,0.57220995,0.014402506,0.021973623,0.0054003038,0.20043264],"study_design_scores_gemma":[0.000019972551,0.00007360539,0.008712864,0.000016868435,0.000033670673,0.00003478796,0.00023078141,0.97628194,0.0025358128,0.011174823,0.00085362216,0.00003118486],"about_ca_topic_score_codex":0.009488025,"about_ca_topic_score_gemma":0.011327519,"teacher_disagreement_score":0.017960161,"about_ca_system_score_codex":0.0019110242,"about_ca_system_score_gemma":0.0012951707,"threshold_uncertainty_score":0.09498352},"labels":[],"label_agreement":null},{"id":"W4404505079","doi":"10.1017/pan.2024.19","title":"News Sharing on Social Media: Mapping the Ideology of News Media, Politicians, and the Mass Public","year":2024,"lang":"en","type":"article","venue":"Political Analysis","topic":"Social Media and Politics","field":"Social Sciences","cited_by":9,"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":"Charles Koch Foundation; Craig Newmark Philanthropies; York University; John S. and James L. Knight Foundation; National Science Foundation","keywords":"Ideology; Mass media; News media; Media studies; Political science; Social media; Sociology; Politics; Law","score_opus":0.09068242379012109,"score_gpt":0.3412592340291247,"score_spread":0.25057681023900363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404505079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949544,0.00004202135,0.0008529543,0.000082984996,0.0000034447728,0.000009368053,0.00013330957,0.0000070112437,0.003914567],"genre_scores_gemma":[0.9993405,0.00002313527,0.00034610037,0.0000075262665,0.0000089687,0.000007836729,0.000099918776,0.0000028235722,0.0001631799],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99714893,0.0017035638,0.00011684106,0.00027539145,0.0005220646,0.00023325317],"domain_scores_gemma":[0.98196983,0.010159612,0.005162747,0.001018682,0.0011413959,0.00054777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024438133,0.00021189344,0.00025481297,0.004031773,0.00092357036,0.0024689997,0.00028659738,0.00033250314,0.0029152231],"category_scores_gemma":[0.016239952,0.00015517639,0.00020795566,0.0030940305,0.00090641977,0.0016652904,0.0016282139,0.00043318074,0.00040983947],"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":[0.00027142925,0.00015193541,0.9495536,0.0000691567,0.00012466832,0.000074263524,0.008354672,0.0010206325,0.0017099518,0.0036287145,0.0006304402,0.034410715],"study_design_scores_gemma":[0.000024187095,0.00016245534,0.9415943,0.000080180915,0.00008663342,0.00018942449,0.030842744,0.01364898,0.0033488385,0.0048647285,0.005120919,0.000036633748],"about_ca_topic_score_codex":0.0025978053,"about_ca_topic_score_gemma":0.0020386716,"teacher_disagreement_score":0.004031773,"about_ca_system_score_codex":0.00049108575,"about_ca_system_score_gemma":0.00034892163,"threshold_uncertainty_score":0.012924314},"labels":[],"label_agreement":null},{"id":"W4406488529","doi":"10.1017/pan.2024.25","title":"MODGIRT: Multidimensional Dynamic Scaling of Aggregate Survey Data","year":2025,"lang":"en","type":"article","venue":"Political Analysis","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","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":"Aggregate (composite); Scaling; Multidimensional scaling; Computer science; Econometrics; Mathematics; Machine learning; Materials science","score_opus":0.07137923242633668,"score_gpt":0.30451429354592047,"score_spread":0.2331350611195838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406488529","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":[0.020974707,0.00026971227,0.9432186,0.0006557801,0.00013265584,0.00063756044,0.014795711,0.016407235,0.0029080492],"genre_scores_gemma":[0.19290322,0.00027098358,0.77873254,0.00027429828,0.00012013098,0.003563925,0.018767752,0.0033599336,0.002007176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98076403,0.013638139,0.0010447513,0.0026265369,0.0015848046,0.00034176893],"domain_scores_gemma":[0.9410132,0.031622972,0.005109885,0.019050607,0.0026825187,0.0005208023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022357995,0.0013116709,0.0014917906,0.004216777,0.00077176193,0.003684956,0.0026001935,0.0013055127,0.017736258],"category_scores_gemma":[0.11748261,0.001264284,0.0034202537,0.007542385,0.0010653335,0.0023664548,0.0038927419,0.0031300948,0.0063444804],"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":[0.000575413,0.00039287645,0.078766786,0.00143587,0.0021483176,0.00060878805,0.0029498043,0.15266898,0.0026884999,0.18768455,0.11743998,0.45264015],"study_design_scores_gemma":[0.00021096558,0.00023674573,0.030991524,0.00036071916,0.00017937574,0.00044876896,0.0004615772,0.6686477,0.0020585996,0.23125933,0.06494612,0.0001985321],"about_ca_topic_score_codex":0.0053954907,"about_ca_topic_score_gemma":0.0047923895,"teacher_disagreement_score":0.022357995,"about_ca_system_score_codex":0.0012782338,"about_ca_system_score_gemma":0.0018380142,"threshold_uncertainty_score":0.11824185},"labels":[],"label_agreement":null},{"id":"W4406489459","doi":"10.1017/pan.2024.24","title":"Decoupling Visualization and Testing when Presenting Confidence Intervals","year":2025,"lang":"en","type":"article","venue":"Political Analysis","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","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":"Western University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Decoupling (probability); Confidence interval; Computer science; Visualization; Reliability engineering; Statistics; Data mining; Mathematics; Engineering; Control engineering","score_opus":0.5242550988197755,"score_gpt":0.599761551093115,"score_spread":0.07550645227333952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406489459","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":[0.0030844568,0.00053019635,0.9802777,0.0037841864,0.00067230937,0.0003139264,0.00044213908,0.0063746558,0.004520281],"genre_scores_gemma":[0.10353828,0.00054026087,0.8868158,0.0014658744,0.00081365235,0.0016249607,0.00040332967,0.0034813518,0.0013165358],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.84227955,0.1329574,0.008906991,0.0036213277,0.011386707,0.00084808],"domain_scores_gemma":[0.34734794,0.5793385,0.018563827,0.033794846,0.018368315,0.0025865412],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.106211066,0.002418584,0.00236991,0.0069400114,0.0016137894,0.012497641,0.004849428,0.0034472437,0.028797865],"category_scores_gemma":[0.56053746,0.00187583,0.002116257,0.0058617336,0.006810508,0.014202811,0.011078047,0.008367457,0.005160507],"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":[0.0020198,0.00023492087,0.0042754943,0.0033653765,0.00041436564,0.0010075214,0.010817268,0.015049184,0.0045488277,0.4545658,0.07014492,0.43355656],"study_design_scores_gemma":[0.000493925,0.0003026504,0.0018557345,0.001991465,0.0001606929,0.0007176203,0.0014168364,0.08390572,0.009583262,0.7989698,0.100304626,0.0002977009],"about_ca_topic_score_codex":0.0007580756,"about_ca_topic_score_gemma":0.0008053109,"teacher_disagreement_score":0.89378893,"about_ca_system_score_codex":0.0018003244,"about_ca_system_score_gemma":0.0033937597,"threshold_uncertainty_score":0.5617045},"labels":[],"label_agreement":null},{"id":"W4414843801","doi":"10.1017/pan.2025.10018","title":"Survey Professionalism: New Evidence from Web Browsing Data","year":2025,"lang":"en","type":"article","venue":"Political Analysis","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","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 Research Council; University of Cambridge; Charles Koch Foundation; John S. and James L. Knight Foundation; Craig Newmark Philanthropies; New York University; York University","keywords":"Web survey; Quality (philosophy); Demographics; Survey data collection; Selection (genetic algorithm); Survey research; Resource (disambiguation); Data quality","score_opus":0.6685309904221518,"score_gpt":0.5838093600588632,"score_spread":0.08472163036328861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414843801","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":[0.8661204,0.00801637,0.025198441,0.025321973,0.00029373044,0.0005311489,0.005212329,0.000116644835,0.06918902],"genre_scores_gemma":[0.9885011,0.0013586892,0.0030995635,0.003484363,0.000270869,0.00029052384,0.001346728,0.0000989227,0.0015492645],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.6966582,0.22520515,0.015806697,0.017562697,0.040904317,0.0038629347],"domain_scores_gemma":[0.09902799,0.7606494,0.067993194,0.041982155,0.027196212,0.0031511246],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17572711,0.00036024113,0.00087371434,0.005152772,0.0022819042,0.007243221,0.00348728,0.002592773,0.012860199],"category_scores_gemma":[0.5593306,0.00094932516,0.00089958956,0.009877373,0.006106188,0.007920747,0.0062245214,0.0034317954,0.0019325194],"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":[0.0011312759,0.00040683837,0.8801553,0.0018276797,0.0006313428,0.00024665357,0.032519888,0.0002742118,0.00025267745,0.006453982,0.009004953,0.06709525],"study_design_scores_gemma":[0.00031460504,0.0005200247,0.870587,0.0058303606,0.000809223,0.0009036321,0.04218043,0.004552594,0.0015350828,0.015280309,0.05736356,0.00012315999],"about_ca_topic_score_codex":0.012191697,"about_ca_topic_score_gemma":0.011111775,"teacher_disagreement_score":0.8242729,"about_ca_system_score_codex":0.0022478644,"about_ca_system_score_gemma":0.003464202,"threshold_uncertainty_score":0.9293449},"labels":[],"label_agreement":null}]}