{"id":"W7010350239","doi":"","title":"Hiring Algorithms in the Canadian Private Sector: Examining the Promise of\\tGreater Workplace Equality","year":2019,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Job evaluation; Personnel selection; Job loss; Key (lock); Equal employment opportunity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004282008,0.0001010595,0.0001478676,0.00009476087,0.0006897826,0.000398469,0.0007932632,0.00009824852,0.0008706493],"category_scores_gemma":[0.0003050179,0.00006756752,0.00005259029,0.0004720337,0.0003106333,0.0003265743,0.00007695666,0.0003264506,0.000155496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000286104,"about_ca_system_score_gemma":0.0005074764,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6170889,"about_ca_topic_score_gemma":0.9109453,"domain_scores_codex":[0.9974135,0.0008141421,0.0002446199,0.0002393684,0.0007809764,0.0005073913],"domain_scores_gemma":[0.9989495,0.0002914444,0.00008259126,0.0003966646,0.0001004118,0.0001793392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002209098,0.00006892704,0.8036206,0.00007848543,0.00002733491,0.00001562211,0.03014009,0.00004127156,0.000143769,0.1620217,0.001500413,0.002319682],"study_design_scores_gemma":[0.001531879,0.0001027263,0.7820697,0.0002153077,0.00003998284,0.000002821899,0.03300246,0.0002042463,0.0005385966,0.01275953,0.1689359,0.0005968062],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8645567,0.000134455,0.000007834988,0.002400619,0.0002515405,0.0006686263,0.000007799456,0.00001942213,0.131953],"genre_scores_gemma":[0.9929786,0.00002027917,0.00008329494,0.0005049114,0.0001653079,0.00003976237,0.00000379183,0.000009564054,0.00619451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2938564,"threshold_uncertainty_score":0.9532999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06085036033514326,"score_gpt":0.3311175708131527,"score_spread":0.2702672104780094,"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."}}