{"id":"W4409085784","doi":"10.2139/ssrn.5121025","title":"Artificial Intelligence and the Labor Market","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Business; Labour economics; Artificial intelligence; Economics; Computer 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":[0.0006540365,0.0002113505,0.0003786095,0.0009274848,0.0003764758,0.002609886,0.0002375145,0.001015881,0.006813266],"category_scores_gemma":[0.00355137,0.0001285864,0.0001642119,0.001197691,0.001803346,0.001932219,0.0006094261,0.00100773,0.000397675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007874112,"about_ca_system_score_gemma":0.0005007352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001541945,"about_ca_topic_score_gemma":0.001029419,"domain_scores_codex":[0.9996381,0.0001894743,0.00001462819,0.00004767725,0.00008462031,0.00002549958],"domain_scores_gemma":[0.9971917,0.002369239,0.000199818,0.00008819743,0.00007960395,0.00007140062],"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":[0.00003908325,0.00007131671,0.004132495,0.0001566555,0.000044346,0.0001097527,0.0002709714,0.01215651,0.0002549953,0.9279959,0.008950133,0.04581783],"study_design_scores_gemma":[0.000009226058,0.000008310808,0.002030995,0.00004970248,0.000005211615,0.00004064867,0.0001203641,0.0193495,0.00005644111,0.9695629,0.008760965,0.000005670647],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3076391,0.1046841,0.104045,0.1139914,0.001311888,0.00005501178,0.001105754,0.000245489,0.3669223],"genre_scores_gemma":[0.9693015,0.01301762,0.004919975,0.001015336,0.0008445432,0.00002992207,0.0001385431,0.00001912264,0.01071332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006813266,"threshold_uncertainty_score":0.02279264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02077140786390574,"score_gpt":0.2423251506424373,"score_spread":0.2215537427785316,"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."}}