{"id":"W4391470757","doi":"10.1016/j.hrmr.2024.101012","title":"The ethical implications of big data in human resource management","year":2024,"lang":"en","type":"article","venue":"Human Resource Management Review","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"","keywords":"Human resource management; Big data; Business; Psychology; Environmental resource management; Knowledge management; Computer science; Economics","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":[0.1336546,0.000438998,0.0012089,0.004040287,0.003902077,0.0129885,0.002150981,0.01476458,0.002679837],"category_scores_gemma":[0.2573333,0.000573254,0.001025369,0.004008878,0.02056974,0.01432811,0.003869852,0.02076376,0.0004944434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00583739,"about_ca_system_score_gemma":0.02168692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0059813,"about_ca_topic_score_gemma":0.009624966,"domain_scores_codex":[0.8852301,0.07847196,0.005440181,0.002620522,0.02568996,0.002547258],"domain_scores_gemma":[0.3684921,0.5700057,0.01059049,0.008620545,0.03798145,0.004309757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001019003,0.00007448627,0.001716533,0.003097868,0.000166707,0.0002654291,0.002500342,0.0009001488,0.000272176,0.5934913,0.2556547,0.1417585],"study_design_scores_gemma":[0.00006534344,0.00006588358,0.003007406,0.01229611,0.0001209093,0.0002898699,0.004125562,0.001034126,0.0003869849,0.3961663,0.5823574,0.00008413573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.001566347,0.1441199,0.003449191,0.8241305,0.01291105,0.00003680119,0.00009765942,0.00001497447,0.0136735],"genre_scores_gemma":[0.1339169,0.1770481,0.007558975,0.6265901,0.04877124,0.0003438265,0.0001504481,0.00008822684,0.005532227],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.1336546,"threshold_uncertainty_score":0.7068415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09762761419773061,"score_gpt":0.3350194986233503,"score_spread":0.2373918844256197,"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."}}