{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001428753,0.0002700791,0.0003453271,0.0002963468,0.0002887263,0.000371485,0.0004140648,0.00003743676,0.0007220568],"category_scores_gemma":[0.0001233663,0.0002730546,0.00008391391,0.0007011581,0.00001641033,0.001481378,0.0002436999,0.0002546372,0.0002807449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005944712,"about_ca_system_score_gemma":0.000006355153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003476593,"about_ca_topic_score_gemma":0.00004941731,"domain_scores_codex":[0.9981848,0.00007052458,0.0004474125,0.0005854383,0.0003421453,0.0003696676],"domain_scores_gemma":[0.9994355,0.00001283457,0.000235323,0.0002525447,0.0000335235,0.00003029605],"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":[0.0001964565,0.0006814511,0.02467291,0.05276806,0.0001178337,0.00005576868,0.003352556,0.000145194,0.0001263228,0.03605948,0.1777335,0.7040905],"study_design_scores_gemma":[0.001118855,0.00002563933,0.006243856,0.008696578,0.000512532,9.340087e-7,0.002707928,0.01740509,0.000003216083,0.0002780149,0.9621382,0.000869137],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3956367,0.03197728,0.01350427,0.03309858,0.003974271,0.01407154,0.000003528186,0.00405418,0.5036796],"genre_scores_gemma":[0.9762337,0.004605644,0.001250444,0.01518654,0.001183604,0.0003418958,0.0001744422,0.00007823735,0.0009455202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7844048,"threshold_uncertainty_score":0.9999722,"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."}}