{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003854979,0.000306647,0.0003970503,0.0004782634,0.0001299535,0.0002154599,0.0002144543,0.00008486995,0.0005931141],"category_scores_gemma":[0.00008253362,0.0003041424,0.00006393972,0.000396429,0.00003598962,0.0002321365,0.0008947427,0.0004098264,0.00002816338],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004242902,"about_ca_system_score_gemma":0.001007326,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9972132,"about_ca_topic_score_gemma":0.9997644,"domain_scores_codex":[0.9979957,0.0000160899,0.0004876736,0.000521234,0.0006296938,0.0003496187],"domain_scores_gemma":[0.9992795,0.00003603075,0.0002082885,0.0002747046,0.0001891552,0.00001229906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001570309,0.0001685716,0.7806175,0.000476446,0.0002037578,0.0001685715,0.0009728213,0.1862361,0.00001722115,0.01890985,0.006311507,0.005760646],"study_design_scores_gemma":[0.0008345353,0.00001146319,0.9426303,0.001321355,0.00003929911,8.787804e-7,0.001299644,0.01607257,0.00002363192,0.01402678,0.02308327,0.0006562506],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9174386,0.00008418368,0.001073014,0.0004645467,0.00251835,0.001884335,0.000006612927,0.0001599208,0.07637042],"genre_scores_gemma":[0.9969461,0.00001169804,0.0001028853,0.0004861942,0.0001488249,0.000277473,0.0000278403,0.00002559708,0.001973388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1701635,"threshold_uncertainty_score":0.9999411,"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."}}