{"id":"W4404949790","doi":"10.1111/eufm.12525","title":"Financial Market Misconduct: A Bibliometric Perspective","year":2024,"lang":"en","type":"article","venue":"European Financial Management","topic":"Risk Management in Financial Firms","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Royal Bank of Canada","keywords":"Perspective (graphical); Misconduct; Field (mathematics); Scientific misconduct; Data science; Bibliometrics; Political science; Computer science; Library science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics","insufficient_payload"],"category_scores_codex":[0.001818911,0.0006728941,0.0004530133,0.02205354,0.0004734643,0.001935075,0.001219639,0.00009750176,0.001456227],"category_scores_gemma":[0.0005575526,0.000671803,0.0003953998,0.04220939,0.0001575261,0.002110672,0.001457369,0.0005228791,0.01325801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002694432,"about_ca_system_score_gemma":0.00005255824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002462151,"about_ca_topic_score_gemma":0.00003876592,"domain_scores_codex":[0.9958244,0.00006928846,0.0007402413,0.00141267,0.0009133118,0.001040063],"domain_scores_gemma":[0.9986333,0.00005685366,0.0002089897,0.000829278,0.0002304854,0.00004109245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005029862,0.0001170872,0.0002536653,0.0004930889,0.00004566988,0.001613537,0.00005990775,0.00001713732,0.00001346281,0.5008933,0.382931,0.1135119],"study_design_scores_gemma":[0.0005432405,0.00003762985,0.08463542,0.0002878995,0.0001999275,0.000005192579,0.0001398139,0.0004279855,0.000006195459,0.02922696,0.8837035,0.0007861909],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02036842,0.002311332,0.004694311,0.001756315,0.005867936,0.001298902,0.00001766847,0.00159102,0.9620941],"genre_scores_gemma":[0.9358351,0.0006522606,0.001186576,0.006703896,0.0106869,0.0001453466,0.00005558286,0.0003465579,0.04438782],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9177063,"threshold_uncertainty_score":0.9995733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765534562063555,"score_gpt":0.2310250391357287,"score_spread":0.2133696935150931,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}