{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05009982,0.0004021481,0.0006676456,0.001916554,0.001370527,0.001911579,0.001420752,0.001204184,0.01296834],"category_scores_gemma":[0.416656,0.00040345,0.0008504263,0.002089001,0.002803631,0.002021285,0.002872107,0.001130333,0.001022144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001535477,"about_ca_system_score_gemma":0.001551057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002391731,"about_ca_topic_score_gemma":0.003675342,"domain_scores_codex":[0.9093812,0.06226657,0.004988575,0.005583106,0.01597571,0.001804907],"domain_scores_gemma":[0.4576985,0.4564753,0.03816693,0.02224755,0.02270067,0.002711093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006182503,0.0008184743,0.7552861,0.001730449,0.0006311337,0.0006829967,0.08344093,0.000541251,0.004514148,0.008640869,0.006815115,0.1307161],"study_design_scores_gemma":[0.0001556514,0.002041718,0.9412364,0.00102072,0.0002907083,0.0003928061,0.03480664,0.00174778,0.003550849,0.004324968,0.0103307,0.0001011877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695994,0.0007116605,0.0105111,0.00181661,0.00041091,0.0007503687,0.000896834,0.00009795308,0.01520523],"genre_scores_gemma":[0.9949608,0.00006648428,0.002046857,0.0004751551,0.00009691819,0.0005215603,0.0002031745,0.00005408119,0.001575048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05009982,"threshold_uncertainty_score":0.2649564,"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."}}