{"id":"W4322718538","doi":"10.32920/21977015.v1","title":"Using tickets in employment standards inspections: Deterrence as effective enforcement in Ontario, Canada?","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Women's and Gender Studies et Recherches Féministes; York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Enforcement; Deterrence theory; Business; Deterrence (psychology); Context (archaeology); Government (linguistics); Law enforcement; Ticket; Public economics; Economics; Computer security; Political science; Law and economics; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002790404,0.0002415433,0.0003981573,0.001893898,0.003947823,0.002846149,0.001788498,0.0007615903,0.003301114],"category_scores_gemma":[0.01289021,0.0003945709,0.000353561,0.006034512,0.002720262,0.001179236,0.001304573,0.001311136,0.0002186739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1183271,"about_ca_system_score_gemma":0.1620186,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9988423,"about_ca_topic_score_gemma":0.9994185,"domain_scores_codex":[0.9957199,0.0004503106,0.0002112201,0.0003069128,0.002035602,0.001276169],"domain_scores_gemma":[0.9797118,0.002076878,0.005385154,0.0004502657,0.009052651,0.00332323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002283627,0.0002908882,0.9036735,0.000347441,0.00007142594,0.0003165583,0.01264088,0.001022719,0.000558376,0.007125177,0.01414109,0.05958354],"study_design_scores_gemma":[0.00001152129,0.00004266041,0.9785222,0.0001084589,0.00002311686,0.00002887511,0.008447057,0.0007986343,0.0001586425,0.0001780744,0.0116524,0.00002829675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500012,0.004060104,0.0005053876,0.01204469,0.00005834329,0.0001881431,0.003056381,0.00003094561,0.03005474],"genre_scores_gemma":[0.9840714,0.002462873,0.0005436708,0.0004949226,0.00002778009,0.00003686981,0.0009049782,0.00001250223,0.01144499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1183271,"threshold_uncertainty_score":0.8585275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07267841662524722,"score_gpt":0.3089777927276377,"score_spread":0.2362993761023905,"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."}}