{"id":"W4402268915","doi":"10.32920/26871400.v1","title":"Examining Potential Gender Bias in Automated Recruitment Systems in Canada and Its Impact on Immigrant Women","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Employer Branding and e-HRM","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Demographic economics; Gender bias; Political science; Psychology; Gender studies; Sociology; Economics; Social psychology; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006328358,0.000276131,0.0003251124,0.001854657,0.01866267,0.004683087,0.001176421,0.0008342246,0.004714098],"category_scores_gemma":[0.01284989,0.0002697933,0.0002904105,0.003648387,0.00619898,0.001184855,0.004361061,0.001214797,0.0002499207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05703171,"about_ca_system_score_gemma":0.09214132,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9827004,"about_ca_topic_score_gemma":0.98972,"domain_scores_codex":[0.9950748,0.001066505,0.00012772,0.0003608379,0.001503442,0.001866695],"domain_scores_gemma":[0.9896227,0.003296056,0.001545454,0.0003360943,0.003662885,0.001536804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002399653,0.0001191909,0.5004922,0.0001784064,0.00002481032,0.0009313219,0.4072384,0.0003140472,0.001421038,0.01189406,0.006454802,0.07069182],"study_design_scores_gemma":[0.00001232504,0.00008998797,0.348395,0.000291591,0.00002654383,0.0001635316,0.6141776,0.0007112494,0.001003552,0.0007627401,0.03429471,0.00007104663],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821005,0.0004015486,0.0003120856,0.003550034,0.0000347746,0.00005171437,0.0002129077,0.00001045145,0.01332602],"genre_scores_gemma":[0.9938008,0.000403345,0.00029715,0.0006807401,0.000007185802,0.00003083675,0.00008195599,0.00001050323,0.00468741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05703171,"threshold_uncertainty_score":0.4137959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106021206538993,"score_gpt":0.2889013112520387,"score_spread":0.1782991905981395,"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."}}