{"id":"W4220957410","doi":"10.1111/ijsa.12380","title":"Little cause for concern: Analysis of gender effects in structured employment references","year":2022,"lang":"en","type":"article","venue":"International Journal of Selection and Assessment","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Toronto Metropolitan University","funders":"","keywords":"Psychology; Selection (genetic algorithm); Personnel selection; Sample (material); Variety (cybernetics); Narrative; Gender bias; Selection bias; Social psychology; Management; Computer science; Statistics; Economics","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":[0.0007005762,0.00003694284,0.0001299681,0.0002919082,0.0001262938,0.00002812404,0.0001270498,0.00001795515,0.0002711092],"category_scores_gemma":[0.00003426446,0.00003571668,0.00007315687,0.0002488969,0.00002459005,0.0001088912,0.00003116848,0.0001022074,3.753454e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002756267,"about_ca_system_score_gemma":0.0001831754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006420169,"about_ca_topic_score_gemma":0.001008516,"domain_scores_codex":[0.9989824,0.000176554,0.0002144837,0.00007049536,0.0004887853,0.00006727932],"domain_scores_gemma":[0.9993448,0.0001043028,0.0002477068,0.00001924215,0.0002505629,0.00003333704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003169912,0.0004751246,0.8703655,0.00002142348,0.003596802,0.00001035394,0.02355421,0.008900065,0.001483394,0.07796095,0.0007483235,0.01256687],"study_design_scores_gemma":[0.001675672,0.0004669158,0.9478399,0.00001240039,0.0004396766,0.000004767114,0.02054982,0.002046146,0.0004865357,0.007319354,0.01903176,0.0001270817],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994285,0.00006702404,0.003441988,0.0008081623,0.0006011674,0.00009891378,0.00002476785,0.000002922674,0.0006700531],"genre_scores_gemma":[0.9990684,0.00007411413,0.000633762,0.00008574806,0.00005584208,0.000004031285,0.000005913526,0.000001228627,0.00007096696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07747439,"threshold_uncertainty_score":0.2968455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1392366273264262,"score_gpt":0.4176076078521629,"score_spread":0.2783709805257367,"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."}}