{"id":"W4318486538","doi":"10.32920/21977024.v1","title":"Who to inspect? Using employee complaint data to inform workplace inspections in Ontario","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Centennial College; Toronto Metropolitan University","funders":"","keywords":"Complaint; Enforcement; Christian ministry; Business; Accounting; Public relations; Operations management; Engineering; Political science; Law","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":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003517322,0.0003883456,0.0005337034,0.001078603,0.000382646,0.0008340021,0.001111464,0.0001264048,0.0004123597],"category_scores_gemma":[0.0001270456,0.0003941194,0.00006554268,0.001452216,0.00003478648,0.0007050849,0.009045743,0.0005518085,0.001595324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006323893,"about_ca_system_score_gemma":0.0001357944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2736622,"about_ca_topic_score_gemma":0.9048491,"domain_scores_codex":[0.9976602,0.000006504273,0.0006180393,0.0008644013,0.0004072952,0.000443629],"domain_scores_gemma":[0.9981972,0.00003380774,0.0001733885,0.00135419,0.0002063784,0.00003502176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001159297,0.0001175522,0.6568555,0.0003521919,0.0001897579,0.00003115183,0.00147928,0.1467467,0.00001432397,0.02409487,0.1686203,0.001382494],"study_design_scores_gemma":[0.0002281564,0.000005905192,0.8403789,0.0005604598,0.00003788836,8.33388e-7,0.000665851,0.009472148,8.380916e-7,0.002704581,0.1453743,0.0005700714],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7943116,0.00002891348,0.01466501,0.01357977,0.00437792,0.002933235,0.00003970184,0.001350699,0.1687131],"genre_scores_gemma":[0.9463075,0.00001098744,0.009879628,0.00796463,0.001993058,0.0002320463,0.0006348414,0.000158844,0.03281851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6311868,"threshold_uncertainty_score":0.999851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2770489717194408,"score_gpt":0.3412195929696208,"score_spread":0.06417062125018003,"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."}}