{"id":"W4392898263","doi":"10.61838/kman.aitech.1.2.1","title":"AI and the Future of Work: Adapting to Change While Ensuring Social Equity","year":2023,"lang":"en","type":"article","venue":"","topic":"Technostress in Professional Settings","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Champion; Sophistication; Equity (law); Public relations; Workforce; Craft; Business; Political science; Sociology; Social science","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":[0.02922763,0.0007531565,0.001027097,0.002345869,0.01924308,0.03122376,0.002859146,0.01002566,0.01059409],"category_scores_gemma":[0.0222713,0.0005798316,0.000960073,0.00128833,0.06200334,0.03239194,0.03356446,0.01166553,0.002813634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008960784,"about_ca_system_score_gemma":0.02480925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004877632,"about_ca_topic_score_gemma":0.00599207,"domain_scores_codex":[0.971307,0.01538081,0.0009649128,0.002984126,0.004840067,0.004523165],"domain_scores_gemma":[0.969029,0.01090743,0.002058979,0.003206281,0.002871969,0.01192627],"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":[0.00004434308,0.0001479049,0.003495997,0.0003218378,0.00004766818,0.0003063386,0.05502558,0.0007359285,0.0008375187,0.8125369,0.02599272,0.1005073],"study_design_scores_gemma":[0.00001459013,0.00008574645,0.001628922,0.000896109,0.00001598292,0.0002047238,0.03145711,0.0004506985,0.0002488436,0.618671,0.3462682,0.00005807077],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02920138,0.0151849,0.04081453,0.6311697,0.003461553,0.0001868796,0.00008103829,0.0002778294,0.2796222],"genre_scores_gemma":[0.8546661,0.01228291,0.02850841,0.07182858,0.003149494,0.0004762883,0.0001071449,0.0002748957,0.02870617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03122376,"threshold_uncertainty_score":0.1545723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0966110103702461,"score_gpt":0.4031089721307777,"score_spread":0.3064979617605316,"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."}}