{"meta":{"query_hash":"e23c56375b4a","filters":{"venue":"The Journal of Finance and Data Science"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/e23c56375b4a","api":"https://metacan.xera.ac/api/v1/cohort?venue=The+Journal+of+Finance+and+Data+Science"},"results":[{"id":"W2333568743","doi":"10.1016/j.jfds.2016.03.001","title":"Auto insurance fraud detection using unsupervised spectral ranking for anomaly","year":2016,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Pattern recognition (psychology); Anomaly detection; Computer science; Spectral clustering; Data mining; Outlier; Laplacian matrix; Rank (graph theory); Artificial intelligence; Categorical variable; Similarity (geometry); Ranking SVM; Mathematics; Machine learning; Cluster analysis; Graph; Theoretical computer science","score_opus":0.04567491940861173,"score_gpt":0.3052709651566672,"score_spread":0.25959604574805545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333568743","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":[0.0443184,0.00015087365,0.953326,0.00017608966,0.00003095822,0.00006240602,0.00009761986,0.00088358624,0.0009540871],"genre_scores_gemma":[0.65315,0.00014075032,0.3437066,0.00010621301,0.00010843521,0.00012819166,0.00056619203,0.0001345173,0.0019591022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741,0.0010496184,0.0001488752,0.0003762473,0.0008198736,0.0001955023],"domain_scores_gemma":[0.99469334,0.0023908361,0.0008596661,0.0008136792,0.0010417006,0.0002007374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002453348,0.0008135762,0.0013110664,0.003563061,0.00072184653,0.0014400101,0.0012906736,0.0009169106,0.0011375183],"category_scores_gemma":[0.008142428,0.00029736845,0.001022867,0.0021323257,0.00084018137,0.0019095669,0.0012351713,0.0014261268,0.00083297864],"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":[0.00027553327,0.0006549983,0.011584215,0.00017580176,0.00020924474,0.00022787027,0.00024653223,0.2763279,0.02178056,0.030950837,0.0059094485,0.651657],"study_design_scores_gemma":[0.0000061197184,0.000043632488,0.00091236655,0.00000503792,0.0000066828597,0.00007256366,0.00002635928,0.9836721,0.002127835,0.012550538,0.00055952393,0.000017225913],"about_ca_topic_score_codex":0.0015007756,"about_ca_topic_score_gemma":0.0020984935,"teacher_disagreement_score":0.003563061,"about_ca_system_score_codex":0.00074758334,"about_ca_system_score_gemma":0.0009966091,"threshold_uncertainty_score":0.0129746795},"labels":[],"label_agreement":null},{"id":"W3122700498","doi":"10.1016/j.jfds.2023.100096","title":"What do we learn from stock price reactions to China's first announcement of anti-corruption reforms?","year":2023,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Los Angeles; University of British Columbia; Chinese University of Hong Kong; National University of Singapore; Bank of Canada; Columbia University; University of Maryland; Philadelphia University; National Bureau of Economic Research; Lingnan University; Hebrew University of Jerusalem; University of Chicago","keywords":"Language change; China; Stock price; Stock (firearms); Monetary economics; Business; Economics; Financial economics; Political science; History; Law","score_opus":0.06784556057034127,"score_gpt":0.34051265476127035,"score_spread":0.27266709419092905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122700498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8373482,0.013292984,0.00063194265,0.10584468,0.0012792635,0.00004710222,0.0032716491,0.00004562823,0.038238578],"genre_scores_gemma":[0.98015445,0.006577916,0.00013289224,0.007097682,0.00095964526,0.00001293074,0.0010933764,0.000011476592,0.0039595584],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992285,0.00014383976,0.000056738743,0.000115626994,0.00023128034,0.00022405325],"domain_scores_gemma":[0.9918188,0.0016245916,0.0033634999,0.0003398847,0.0020926662,0.0007605287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021964316,0.00023417243,0.00039166672,0.0012467875,0.00062816346,0.0030664955,0.0004195463,0.0011696094,0.004338417],"category_scores_gemma":[0.010981805,0.00012407938,0.00034955287,0.0018928447,0.0011690953,0.0027401326,0.000954121,0.0021194073,0.0006401931],"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":[0.00018927151,0.00014157637,0.8662327,0.00036441677,0.0001556553,0.00059950096,0.008691665,0.00049236935,0.0004153584,0.007795383,0.036716215,0.078205876],"study_design_scores_gemma":[0.000020238365,0.00012099911,0.95151913,0.00034222635,0.00006480904,0.00008969974,0.014513367,0.0009205202,0.0005076556,0.0037472057,0.028093774,0.00006041622],"about_ca_topic_score_codex":0.062015153,"about_ca_topic_score_gemma":0.09405322,"teacher_disagreement_score":0.062015153,"about_ca_system_score_codex":0.0023530258,"about_ca_system_score_gemma":0.0019787964,"threshold_uncertainty_score":0.12330836},"labels":[],"label_agreement":null},{"id":"W4394964535","doi":"10.1016/j.jfds.2024.100129","title":"Deep unsupervised anomaly detection in high-frequency markets","year":2024,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Institut de Valorisation des Données; Mitacs","keywords":"Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Physics","score_opus":0.01669828190244714,"score_gpt":0.2716569828885816,"score_spread":0.25495870098613443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394964535","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":[0.33030102,0.0004458082,0.66657853,0.00037316902,0.0000399449,0.000027578639,0.00020687157,0.0011144106,0.0009127375],"genre_scores_gemma":[0.9657914,0.00011215796,0.0327924,0.00004797355,0.00003228905,0.000017221448,0.0002794828,0.000027627111,0.00089952047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938977,0.00015487973,0.00003257692,0.00013763155,0.00017283093,0.00011226948],"domain_scores_gemma":[0.9980562,0.00096280844,0.00036139388,0.000247364,0.0002828708,0.000089403235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013259869,0.00050212536,0.000777967,0.0010784073,0.0002817129,0.0007929704,0.0012829878,0.00076087436,0.00052157714],"category_scores_gemma":[0.004407231,0.00029117372,0.00042845443,0.0008842444,0.00070694706,0.0014189943,0.0009666023,0.0013011551,0.00016665389],"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":[0.00038340676,0.0003297758,0.0367606,0.00009555652,0.00018792103,0.0004982597,0.00017855407,0.6473175,0.01077979,0.017316254,0.002685347,0.28346694],"study_design_scores_gemma":[0.0000020471846,0.000009019405,0.000896573,0.0000014177972,0.0000020918742,0.000023713776,0.000006194792,0.995275,0.0006593134,0.003011818,0.00011044269,0.0000022195377],"about_ca_topic_score_codex":0.0033275967,"about_ca_topic_score_gemma":0.0029924433,"teacher_disagreement_score":0.0033275967,"about_ca_system_score_codex":0.00065893505,"about_ca_system_score_gemma":0.0006857076,"threshold_uncertainty_score":0.007012546},"labels":[],"label_agreement":null},{"id":"W4403260240","doi":"10.1016/j.jfds.2024.100139","title":"What drives liquidity in the Chinese credit bond markets?","year":2024,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":14,"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":"Hong Kong Baptist University; National University of Singapore; Tianjin University; Canadian Intensive Care Foundation; Capital University of Economics and Business; Central University of Finance and Economics; University of International Business and Economics; New York University; Volkswagen Foundation; Alexander von Humboldt-Stiftung","keywords":"Market liquidity; Bond; Bond market; Financial system; Business; Credit enhancement; Monetary economics; Credit risk; Economics; Finance; Credit reference","score_opus":0.03817398250286516,"score_gpt":0.29598633277787534,"score_spread":0.25781235027501015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403260240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963207,0.0004919717,0.00042012116,0.0005075626,0.0000060649027,0.0000109238745,0.00006121772,0.000007692652,0.002173751],"genre_scores_gemma":[0.9994242,0.00022915714,0.00006494742,0.000030160441,0.000014316408,0.000003514572,0.000021585469,0.0000014309549,0.00021075159],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998068,0.000043776898,0.000015113635,0.000037505477,0.00003990557,0.000056838053],"domain_scores_gemma":[0.9984402,0.00046593745,0.0007279767,0.00004535216,0.00016859677,0.00015196599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005611484,0.00022965642,0.0003309699,0.0012209127,0.00057603605,0.0021490057,0.00035140544,0.0005856158,0.0025156683],"category_scores_gemma":[0.0031181935,0.00023681152,0.0003654278,0.0010354,0.000925261,0.0017071547,0.00069150724,0.00041171224,0.00014913638],"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":[0.00034135274,0.0002206104,0.8860607,0.00034532795,0.0002754999,0.0019219816,0.003965404,0.012059333,0.015441843,0.046034902,0.001806221,0.031526737],"study_design_scores_gemma":[0.00007111859,0.000111317444,0.9166397,0.00005950704,0.00026296862,0.00023069339,0.0031606879,0.04910555,0.0030637577,0.025077479,0.0021369087,0.000080339145],"about_ca_topic_score_codex":0.018678248,"about_ca_topic_score_gemma":0.013889592,"teacher_disagreement_score":0.018678248,"about_ca_system_score_codex":0.0010925302,"about_ca_system_score_gemma":0.0009837662,"threshold_uncertainty_score":0.037139058},"labels":[],"label_agreement":null},{"id":"W4409149437","doi":"10.1016/j.jfds.2025.100163","title":"Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems","year":2025,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Haystack; Payment; Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning; World Wide Web","score_opus":0.029643996272867908,"score_gpt":0.3219928109928626,"score_spread":0.2923488147199947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409149437","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":[0.004636426,0.00014718309,0.9943592,0.00031919542,0.000014561293,0.000023931894,0.000031716496,0.00029287723,0.00017484595],"genre_scores_gemma":[0.37243515,0.00035515864,0.6249154,0.00034724636,0.0002241633,0.00016937974,0.0002455314,0.00011572683,0.0011923101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643886,0.0014974345,0.00024147105,0.0007444875,0.00078565377,0.00029207938],"domain_scores_gemma":[0.98998535,0.006550127,0.0010812116,0.00082125934,0.00122199,0.00034013108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005523994,0.0012611005,0.0016531487,0.0029145968,0.0010310399,0.0027894687,0.0033026105,0.002279737,0.0010630246],"category_scores_gemma":[0.01429675,0.00073123747,0.0018438814,0.0019692502,0.0027547025,0.0037964263,0.0029393057,0.003995632,0.0004317949],"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":[0.00010096695,0.00021231415,0.008010762,0.00012697362,0.00019982937,0.00021875894,0.00034465702,0.8094454,0.0023755122,0.054663856,0.0014797193,0.12282121],"study_design_scores_gemma":[0.000003533435,0.00001708981,0.00020249501,0.0000064799383,0.0000058693527,0.000015187068,0.000010159957,0.9821846,0.0002195831,0.017062623,0.00026430734,0.0000080409145],"about_ca_topic_score_codex":0.008428644,"about_ca_topic_score_gemma":0.0054874006,"teacher_disagreement_score":0.008428644,"about_ca_system_score_codex":0.0014953897,"about_ca_system_score_gemma":0.0018606058,"threshold_uncertainty_score":0.029214025},"labels":[],"label_agreement":null},{"id":"W4413049767","doi":"10.1016/j.jfds.2025.100165","title":"Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆","year":2025,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Extreme value theory; Reinforcement; Computer science; Risk analysis (engineering); Business; Artificial intelligence; Mathematics; Psychology; Social psychology; Statistics","score_opus":0.05512067823412605,"score_gpt":0.3696084126283005,"score_spread":0.3144877343941745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413049767","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":[0.02711695,0.00045495792,0.9691309,0.00040211453,0.00007931478,0.000059662187,0.000032141255,0.00037277967,0.0023511727],"genre_scores_gemma":[0.94091827,0.00018400386,0.056426696,0.00025683618,0.00007106528,0.00011951136,0.000066282846,0.00005056372,0.0019067354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930537,0.0002520554,0.000029598454,0.00013724272,0.00015435576,0.00012138408],"domain_scores_gemma":[0.996768,0.0023221653,0.00029659164,0.00011837136,0.00029819494,0.00019666804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019044599,0.00095884333,0.0016303943,0.00045826854,0.00034369068,0.00096909667,0.0013647648,0.0014327862,0.0020632176],"category_scores_gemma":[0.0065241246,0.00052523875,0.000499761,0.00035670376,0.0011350326,0.00088970695,0.0014692716,0.0019344764,0.00033082414],"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":[0.00007228669,0.000062898,0.0006189436,0.000050460392,0.000039162336,0.00006140066,0.000035106532,0.9692644,0.0004832821,0.0065970556,0.0006990196,0.02201599],"study_design_scores_gemma":[0.0000070478577,0.000024639447,0.000039042287,0.000004711849,0.000003753505,0.000009047004,0.0000023232924,0.9969001,0.0000976662,0.0028056547,0.00010342025,0.0000026916396],"about_ca_topic_score_codex":0.0029829254,"about_ca_topic_score_gemma":0.0021207167,"teacher_disagreement_score":0.0029829254,"about_ca_system_score_codex":0.00083082845,"about_ca_system_score_gemma":0.0017359665,"threshold_uncertainty_score":0.010071874},"labels":[],"label_agreement":null},{"id":"W4416028825","doi":"10.1016/j.jfds.2025.100171","title":"The economic impact of DeFi crime events on decentralized autonomous organizations (DAOs)","year":2025,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep Edouard Montpetit","funders":"Österreichische Forschungsförderungsgesellschaft","keywords":"Counterfactual thinking; Corporate governance; Economic impact analysis; Voting; Event study; Asset (computer security); Delegation; Economic cost","score_opus":0.012438790255824268,"score_gpt":0.30698245849461914,"score_spread":0.29454366823879485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416028825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98806494,0.000135911,0.0032330574,0.00055143505,0.000020653171,0.000044865777,0.0005809461,0.00002185816,0.007346355],"genre_scores_gemma":[0.9989857,0.00005907909,0.0003341729,0.000025830082,0.0000076327005,0.000013232053,0.00015244713,0.0000028515256,0.00041911192],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99866724,0.0005168186,0.000060698803,0.00015539455,0.00030442598,0.0002953903],"domain_scores_gemma":[0.991569,0.0034831779,0.003110291,0.00057838776,0.00076576404,0.0004934626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016388594,0.00020892792,0.00024380795,0.0009176876,0.00064493954,0.0015838094,0.00040308692,0.00042864337,0.0034203301],"category_scores_gemma":[0.010782055,0.00013051317,0.00021900874,0.0009383623,0.0011286519,0.0019367593,0.0016877387,0.000981992,0.00023011629],"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":[0.00077758066,0.0008512175,0.757392,0.00021648844,0.00020705181,0.0013558086,0.0009951205,0.0814574,0.0015198505,0.07965318,0.008475634,0.06709864],"study_design_scores_gemma":[0.00015311122,0.0009021334,0.68549556,0.00016562315,0.0001360631,0.00061705004,0.0046263663,0.23391649,0.0038227031,0.05704591,0.013001075,0.000117884265],"about_ca_topic_score_codex":0.007121105,"about_ca_topic_score_gemma":0.0090048835,"teacher_disagreement_score":0.007121105,"about_ca_system_score_codex":0.0015710464,"about_ca_system_score_gemma":0.00077271456,"threshold_uncertainty_score":0.014159322},"labels":[],"label_agreement":null}]}