{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007467828,0.0002262484,0.0002912231,0.001643336,0.0001432268,0.0001342093,0.00055166,0.0001326607,0.00002277255],"category_scores_gemma":[0.00001614645,0.0002277153,0.00005310018,0.001968845,0.0001445649,0.0007387498,0.001283456,0.0002289149,0.00005368835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002653475,"about_ca_system_score_gemma":6.877803e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001748569,"about_ca_topic_score_gemma":0.000001104045,"domain_scores_codex":[0.998247,0.00000170037,0.0004351104,0.0004757747,0.0003895587,0.0004508093],"domain_scores_gemma":[0.9996206,0.00001310914,0.0002362823,0.000093554,0.00002384775,0.00001260766],"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":[0.0000199381,0.00006574376,0.02403909,0.002193501,0.0001174921,0.0000239275,0.00007586426,0.00003356327,0.0001073342,0.8543146,0.061021,0.05798791],"study_design_scores_gemma":[0.001848384,0.00002933067,0.2898776,0.0005361058,0.0002572991,0.000001608842,0.01047501,0.003942618,0.0002568306,0.1122354,0.5798082,0.0007317272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6762229,0.0001304941,0.00004610885,0.007999052,0.00004643909,0.0009305974,0.000001402597,0.001069374,0.3135536],"genre_scores_gemma":[0.993179,0.0004435937,0.0006620739,0.001090973,0.0001552778,0.00007212497,0.00000898258,0.00003951343,0.004348496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7420793,"threshold_uncertainty_score":0.9285961,"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."}}