{"id":"W4391597130","doi":"10.32920/25164566.v1","title":"Employee and Workforce Adaptation as a Result of Artificial Intelligence Deployment","year":2024,"lang":"en","type":"preprint","venue":"","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Workforce; Personalization; Adaptation (eye); Software deployment; Productivity; Unemployment; Business; Marketing; Engineering; Economics; Economic growth; Psychology","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":[0.004937245,0.0001887087,0.000172297,0.001140676,0.001519506,0.003658412,0.001009175,0.001070646,0.009916876],"category_scores_gemma":[0.0192938,0.0001695447,0.0002887691,0.001647084,0.0009004647,0.002242862,0.002905279,0.0009603167,0.002010392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002498985,"about_ca_system_score_gemma":0.002237391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004546403,"about_ca_topic_score_gemma":0.006231673,"domain_scores_codex":[0.9964582,0.001405846,0.0001566018,0.0003658439,0.0009060657,0.0007073412],"domain_scores_gemma":[0.9900158,0.004278662,0.001363324,0.001219106,0.001905078,0.001217976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005284026,0.000953296,0.2469512,0.0004463754,0.00006353777,0.002550226,0.05584136,0.004868215,0.006339668,0.05147255,0.04778143,0.5822036],"study_design_scores_gemma":[0.00005901517,0.0007905802,0.5271071,0.0004304862,0.00004679789,0.001295536,0.125924,0.01226152,0.003724977,0.02353733,0.3047125,0.000110272],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8410356,0.0006642405,0.005080989,0.0160175,0.0003126716,0.0001468313,0.0005729722,0.0002450172,0.1359242],"genre_scores_gemma":[0.9794676,0.0003033613,0.001233853,0.001095214,0.00008350547,0.00005981377,0.0002500542,0.00004040064,0.01746623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009916876,"threshold_uncertainty_score":0.03317523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0762027631985754,"score_gpt":0.2906655375884638,"score_spread":0.2144627743898884,"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."}}