{"id":"W4385222014","doi":"10.5465/amproc.2023.10825symposium","title":"Analytics and Algorithms in Human Resource Management","year":2023,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Computer science; Data science; Human resource management; Knowledge management","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":[0.01483473,0.001762443,0.001747347,0.006116055,0.001735963,0.01124099,0.002248552,0.004989506,0.006784657],"category_scores_gemma":[0.03648316,0.0007983031,0.001324204,0.008609436,0.01447257,0.01388422,0.004641516,0.008488854,0.002139051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004947955,"about_ca_system_score_gemma":0.004520048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004742197,"about_ca_topic_score_gemma":0.001588747,"domain_scores_codex":[0.98162,0.01050992,0.001064273,0.002215565,0.004070156,0.0005202134],"domain_scores_gemma":[0.9542624,0.03935749,0.001403866,0.002100334,0.00227002,0.0006058645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003078819,0.00006031393,0.001071285,0.0007133625,0.0000698833,0.00009350446,0.0009173191,0.009718555,0.0001469796,0.8734031,0.01975491,0.09402008],"study_design_scores_gemma":[0.00001121128,0.00002167802,0.0004198251,0.0004018175,0.00001118087,0.00005258956,0.0003115688,0.01599954,0.0001483244,0.923092,0.05949841,0.00003181746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005425612,0.2339885,0.5580915,0.09700273,0.004498642,0.0003799628,0.0005980402,0.0009553967,0.09905954],"genre_scores_gemma":[0.3015972,0.1753902,0.4725768,0.01243789,0.01470323,0.001381388,0.001152215,0.0004838957,0.02027715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01483473,"threshold_uncertainty_score":0.07845449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03287223161815853,"score_gpt":0.2668813639877163,"score_spread":0.2340091323695578,"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."}}