{"id":"W4401730693","doi":"10.2139/ssrn.4929303","title":"Can AI Distort Human Capital? *","year":2024,"lang":"it","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Human capital; Business; Economics; Market economy","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":[0.002092983,0.0002658853,0.0003903684,0.0007162794,0.0006672334,0.003728732,0.000556608,0.002019519,0.01567008],"category_scores_gemma":[0.01741288,0.0001489134,0.000270788,0.001142069,0.002411121,0.002396603,0.0009612446,0.001889513,0.001221454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001544509,"about_ca_system_score_gemma":0.000787595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006283468,"about_ca_topic_score_gemma":0.003769901,"domain_scores_codex":[0.9992945,0.0002549204,0.00003680843,0.000113858,0.0001159356,0.0001839755],"domain_scores_gemma":[0.9883189,0.007054693,0.002612575,0.0007318695,0.0007149096,0.0005669489],"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":[0.0005641268,0.000197831,0.06150092,0.0002934166,0.0002102114,0.0009913676,0.00139143,0.0131998,0.001301618,0.7714131,0.02562402,0.1233122],"study_design_scores_gemma":[0.00008712748,0.0002319074,0.04383669,0.000150691,0.0001030418,0.0003881307,0.0019952,0.01931827,0.00112604,0.8863994,0.04631162,0.00005191507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4960281,0.007350859,0.01937756,0.1667911,0.001652356,0.00006364814,0.00112904,0.0003776977,0.3072296],"genre_scores_gemma":[0.9847609,0.001530959,0.0004076421,0.001910026,0.0004776079,0.000008370808,0.00004265704,0.00001796498,0.01084386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01567008,"threshold_uncertainty_score":0.05242169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114767276024136,"score_gpt":0.2941703787831009,"score_spread":0.2830227060228596,"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."}}