{"id":"W4416347361","doi":"10.1111/caje.70025","title":"Racial bias in criminal sentencing: Historical evidence from Chinese railway workers in British Columbia","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Culture, Economy, and Development Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Queen's University; University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada; Minnesota Population Center, University of Minnesota","keywords":"Recidivism; Criminal justice; Immigration; Context (archaeology); Racial bias; Economic shortage; Prison","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001418043,0.0002067319,0.0003414065,0.002242721,0.005213474,0.001380724,0.001090782,0.000638198,0.002807401],"category_scores_gemma":[0.005462595,0.0002768074,0.0001472959,0.006027703,0.001737632,0.0005204428,0.001367998,0.001265831,0.0003568988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01123575,"about_ca_system_score_gemma":0.01056206,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.97149,"about_ca_topic_score_gemma":0.9888806,"domain_scores_codex":[0.9986631,0.0002262655,0.00008752594,0.0002379195,0.0003251223,0.0004600702],"domain_scores_gemma":[0.9919492,0.0009593427,0.0016279,0.0005206519,0.003757293,0.001185542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007628879,0.00006248924,0.9530274,0.00004353758,0.00004189844,0.0003398395,0.03563293,0.00008677237,0.0003481806,0.0004747525,0.001651507,0.008214417],"study_design_scores_gemma":[0.000002344128,0.00001484416,0.9791028,0.00005187532,0.00001854989,0.00006212667,0.0188111,0.00009852566,0.0001026596,0.00005025215,0.001668384,0.0000164608],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975985,0.0001611919,0.00004205719,0.0002553021,0.000004701526,0.000009165566,0.0003201326,0.000001336399,0.001607493],"genre_scores_gemma":[0.9982992,0.0002301115,0.00004447967,0.00009467374,0.000005355378,0.00001018052,0.0002523307,0.000003554217,0.001060055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02851003,"threshold_uncertainty_score":0.08152151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1749452472886963,"score_gpt":0.2369374481799732,"score_spread":0.06199220089127686,"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."}}