{"id":"W3080261556","doi":"10.1177/2379298120942928","title":"Ranking Candidates: An Experiential Exercise in Personnel Selection","year":2020,"lang":"en","type":"article","venue":"Management Teaching Review","topic":"Management and Marketing Education","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Ranking (information retrieval); Selection (genetic algorithm); Personnel selection; Human resource management; Psychology; Experiential learning; Medical education; Process (computing); Knowledge management; Adjunct; Computer science; Applied psychology; Mathematics education; Management; Medicine; Information retrieval; Artificial intelligence","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":[0.009950863,0.001479005,0.0008125284,0.00107087,0.002417258,0.002710067,0.001895643,0.002224315,0.02591934],"category_scores_gemma":[0.02368235,0.0005096323,0.0006443427,0.0009596157,0.0009281912,0.002126094,0.002931578,0.002493644,0.008684262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009905169,"about_ca_system_score_gemma":0.001620483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002633046,"about_ca_topic_score_gemma":0.0009635313,"domain_scores_codex":[0.9921113,0.005411422,0.0003145444,0.0005096298,0.001020523,0.0006324847],"domain_scores_gemma":[0.9796209,0.01538946,0.0006031295,0.001104864,0.001513971,0.001767735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001878942,0.01474196,0.008006113,0.0008712145,0.00008859183,0.00233974,0.04347005,0.007973576,0.02821204,0.01839947,0.1880194,0.685999],"study_design_scores_gemma":[0.001009723,0.01204095,0.02083703,0.0007586221,0.0001579501,0.004254219,0.05840483,0.06846559,0.03337132,0.07490169,0.7252042,0.0005938401],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.4729966,0.0006451887,0.2952665,0.01476357,0.001817428,0.007566807,0.0009501874,0.004942976,0.2010508],"genre_scores_gemma":[0.5228212,0.0005845939,0.398939,0.001906961,0.0006985022,0.002631142,0.0008865292,0.0005908966,0.07094127],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02591934,"threshold_uncertainty_score":0.08670884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015775365201577,"score_gpt":0.2618542697782504,"score_spread":0.2416965161262347,"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."}}