{"id":"W4410512025","doi":"10.33423/jabe.v27i3.7644","title":"Enhancing Employee Retention: Predicting Attrition Using Machine Learning Models","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Attrition; Employee retention; Computer science; Machine learning; Artificial intelligence; Knowledge management; Business; Marketing; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003814097,0.000139365,0.0003042408,0.0004262401,0.0002903155,0.0002958447,0.0001377792,0.0001033908,0.0000113668],"category_scores_gemma":[0.00004015009,0.0001325709,0.00005813448,0.000276487,0.0000450612,0.001140813,0.0001620343,0.0002340107,0.000001922494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005140356,"about_ca_system_score_gemma":0.00003137796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007421648,"about_ca_topic_score_gemma":0.00004097769,"domain_scores_codex":[0.9991139,0.000002120165,0.0005038218,0.0001525709,0.00005739094,0.0001701518],"domain_scores_gemma":[0.9991393,0.0000254482,0.0005588996,0.00008477289,0.0001834443,0.000008155514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001287329,0.0002677086,0.2269361,0.00303447,0.0006619937,0.0000380535,0.0002901266,0.3573452,0.02478523,0.3092979,0.0004912247,0.07556476],"study_design_scores_gemma":[0.004571298,0.00004447901,0.02418858,0.001380566,0.0007823634,0.00007819608,0.004489117,0.6651119,0.002640575,0.2803012,0.01534852,0.001063208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808219,0.0003508406,0.01466682,0.0003654462,0.0003207029,0.00008389258,7.736519e-7,0.0000574102,0.0033322],"genre_scores_gemma":[0.996604,0.0004171979,0.002141818,0.0002435337,0.0005427325,0.000001978465,0.000004662069,0.00001630847,0.00002775428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3077667,"threshold_uncertainty_score":0.5406086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03216298367089593,"score_gpt":0.2102283357084472,"score_spread":0.1780653520375512,"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."}}