{"id":"W2071780082","doi":"10.1108/13590791011033935","title":"Combating white‐collar crime in Canada: serving victim needs and market integrity","year":2010,"lang":"en","type":"article","venue":"Journal of Financial Crime","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Conceptual framework; Context (archaeology); White-collar crime; Politics; Originality; Law enforcement; Enforcement; Public relations; Value (mathematics); Law and economics; Sociology; Political science; Criminology; Law; Social science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005254335,0.0001276425,0.0002872749,0.000235859,0.0001566798,0.0001206402,0.0001750147,0.00005075946,0.0001415644],"category_scores_gemma":[0.000451197,0.0001131579,0.00004643901,0.0003955413,0.00003102714,0.0006234492,0.000112439,0.0004931027,0.000002869505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007825212,"about_ca_system_score_gemma":0.0003234466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.090413,"about_ca_topic_score_gemma":0.422001,"domain_scores_codex":[0.9990137,0.00001123453,0.0004370284,0.00009152146,0.0002454895,0.0002010853],"domain_scores_gemma":[0.9991424,0.00004308686,0.0003840378,0.00008897281,0.0003222063,0.00001928353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001094626,0.00004821478,0.9417481,0.0001409018,0.00001695777,0.00005846274,0.0002544958,0.00001733994,0.0008609048,0.008406178,0.0362795,0.01205948],"study_design_scores_gemma":[0.0004313198,0.000008648299,0.9663082,0.00009477138,0.00001877675,0.00001383448,0.0006443622,0.0008562204,0.00002484506,0.001497053,0.02996952,0.0001324754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9730151,0.0001379949,0.0000554254,0.002407729,0.001067218,0.00007348205,9.029223e-7,0.000008121884,0.02323407],"genre_scores_gemma":[0.9970009,0.00001011782,0.0004646203,0.001454798,0.0009605514,0.000001228701,4.314734e-7,0.000009667338,0.00009772397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.331588,"threshold_uncertainty_score":0.915644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413936084940573,"score_gpt":0.2191071237954945,"score_spread":0.2049677629460888,"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."}}