{"id":"W4385216701","doi":"10.5465/amproc.2023.15536symposium","title":"Looking at the What, How, and Why at Various Stages of the Personnel Selection Process","year":2023,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Employer Branding and e-HRM","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Selection (genetic algorithm); Psychology; Personnel selection; Promotion (chess); Personality; Sociology; Social psychology; Management; Computer science; Law; Artificial intelligence; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02617907,0.0005300387,0.0004075425,0.001542051,0.002009646,0.003661866,0.000551231,0.0011161,0.005272663],"category_scores_gemma":[0.06775503,0.000362611,0.0008064858,0.0009492229,0.002528973,0.002434986,0.001452169,0.001378952,0.001895742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007890027,"about_ca_system_score_gemma":0.001160912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007836975,"about_ca_topic_score_gemma":0.001032034,"domain_scores_codex":[0.9807625,0.01383465,0.0006492565,0.0007326677,0.002920287,0.001100544],"domain_scores_gemma":[0.9262881,0.06067419,0.005660584,0.002038126,0.003483137,0.001855855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001526783,0.001174949,0.2709305,0.002200116,0.0002597496,0.0008737909,0.1476948,0.000763412,0.05345368,0.007501121,0.00579862,0.5078225],"study_design_scores_gemma":[0.0001012682,0.003746921,0.7896586,0.0009204496,0.0001728557,0.0007297661,0.1441836,0.001384869,0.01895569,0.01410479,0.02575299,0.0002882702],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554132,0.001784249,0.01292419,0.005151993,0.000312321,0.0007824772,0.0000917115,0.0001724743,0.02336748],"genre_scores_gemma":[0.9849407,0.001157552,0.007435509,0.0008223692,0.0001599486,0.0002341605,0.00005294549,0.00004840191,0.005148445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02617907,"threshold_uncertainty_score":0.1384498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02031870754036316,"score_gpt":0.237133189628015,"score_spread":0.2168144820876518,"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."}}