{"id":"W2940610044","doi":"10.1108/jmlc-09-2017-0048","title":"Anti-money laundering and moral intensity in suspicious activity reporting","year":2018,"lang":"en","type":"article","venue":"Journal of Money Laundering Control","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Compliance (psychology); Database transaction; Business; Accounting; Value (mathematics); Public relations; Originality; Political science; Psychology; Law; Finance; Social psychology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.002305984,0.0001759043,0.0006599047,0.000126855,0.000386486,0.0001620675,0.0001993294,0.0001331389,0.00001537938],"category_scores_gemma":[0.001526347,0.0001579816,0.0001296238,0.0001992288,0.0003071943,0.0007231634,0.00006615264,0.0004795256,0.000001317724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003006799,"about_ca_system_score_gemma":0.0001859077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00565716,"about_ca_topic_score_gemma":0.008048639,"domain_scores_codex":[0.9978423,0.0001316685,0.000802469,0.0002284923,0.0005093652,0.0004857785],"domain_scores_gemma":[0.9973988,0.000253135,0.001731804,0.0001398961,0.0002744593,0.0002019148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001269518,0.0004410741,0.787645,0.00009555998,0.0003223686,0.0009382488,0.08194637,0.0003447031,0.09035574,0.001175484,0.0006950976,0.03477081],"study_design_scores_gemma":[0.003870873,0.0005133568,0.9588454,0.0005276036,0.0001182873,0.0002998751,0.0217893,0.00275306,0.004795023,0.00262643,0.003151778,0.0007089977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951179,0.0003259808,0.001421109,0.00090129,0.0007587194,0.0001534923,0.000001783604,0.00003265203,0.001287073],"genre_scores_gemma":[0.9982271,0.0001368229,0.0001688642,0.0002192763,0.001127626,0.000001698243,1.095196e-7,0.00001333511,0.0001051618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1712004,"threshold_uncertainty_score":0.8551972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04269631780880192,"score_gpt":0.3123826285258935,"score_spread":0.2696863107170916,"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."}}