{"id":"W4255925255","doi":"10.31235/osf.io/6p2r4","title":"Whitened Resumes: Race and Self-Presentation in the Labor Market","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Seekers; Diversity (politics); Transparency (behavior); Disadvantage; Presentation (obstetrics); Racism; Audit; Race (biology); Racial diversity; Social psychology; Variety (cybernetics); Psychology; Equal employment opportunity; Inequality; Sociology; Political science; Economics; Gender studies; Law; Management; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002888631,0.0000824558,0.000133222,0.00004298978,0.0005480971,0.0003771721,0.0006001527,0.0001935918,0.0002408248],"category_scores_gemma":[0.0003735955,0.00006315252,0.00003554857,0.00005375831,0.0001311429,0.000175036,0.0003725323,0.0002458684,0.00001082841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004369447,"about_ca_system_score_gemma":0.0001583475,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02508905,"about_ca_topic_score_gemma":0.02182966,"domain_scores_codex":[0.9982721,0.0008705376,0.0001173015,0.0002462573,0.0003330908,0.0001606641],"domain_scores_gemma":[0.9992425,0.0001740082,0.0001205958,0.0003503478,0.00007134872,0.00004113468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00006958903,0.0002383751,0.2990914,0.0001924032,0.00007506489,0.00001901554,0.5030811,0.00001144375,0.000003259761,0.02832269,0.1658134,0.00308227],"study_design_scores_gemma":[0.0003598486,0.00001139735,0.7995631,0.00003755083,0.00003764539,2.31939e-7,0.09206685,0.0001205915,0.000005259064,0.03070199,0.07686522,0.0002303421],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3702838,0.0001856321,0.00004119785,0.03523066,0.0004549207,0.0006105801,0.0000445537,0.0000648518,0.5930837],"genre_scores_gemma":[0.9855648,0.001818283,0.0005693091,0.0005015926,0.0001414677,0.00001063466,0.00001464311,0.000003087497,0.01137619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6152809,"threshold_uncertainty_score":0.9960194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041786182244123,"score_gpt":0.353812282666252,"score_spread":0.2496336644418397,"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."}}