{"id":"W2584472969","doi":"10.1016/j.red.2017.01.008","title":"Complex-task biased technological change and the labor market","year":2017,"lang":"en","type":"article","venue":"Review of Economic Dynamics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"European Research Council; Social Sciences and Humanities Research Council of Canada; Seventh Framework Programme","keywords":"Task (project management); Wage; Set (abstract data type); Contrast (vision); Principal (computer security); Economics; Order (exchange); Technological change; Labour economics; Econometrics; Computer science; Artificial intelligence; Computer security; Macroeconomics; Management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001008819,0.0002875463,0.0005218205,0.001090636,0.0003851374,0.001716312,0.0005146078,0.0009906736,0.002151237],"category_scores_gemma":[0.003491869,0.0001935672,0.0002322732,0.002069634,0.002833518,0.002094257,0.000780004,0.001298438,0.0003336145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298618,"about_ca_system_score_gemma":0.001034406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00282013,"about_ca_topic_score_gemma":0.003462605,"domain_scores_codex":[0.9997451,0.00007439009,0.00001434019,0.000063819,0.00006356892,0.00003893685],"domain_scores_gemma":[0.9980487,0.001330871,0.0002281029,0.00008952081,0.0002104736,0.00009239176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001313661,0.0002331635,0.0149915,0.001696146,0.0001547133,0.0002148021,0.0005712502,0.01347591,0.00147353,0.5355129,0.01361271,0.4179319],"study_design_scores_gemma":[0.00002892141,0.0001149928,0.06166431,0.001087972,0.00006919754,0.000432105,0.0007271141,0.008385386,0.0004134844,0.8047385,0.122271,0.00006697365],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.07034587,0.8727815,0.01016587,0.0264595,0.0005516605,0.0000186966,0.0002047273,0.0000195424,0.0194527],"genre_scores_gemma":[0.3032953,0.6874213,0.001834549,0.001276353,0.002353107,0.00002371976,0.0001135182,0.0000099308,0.003672177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00282013,"threshold_uncertainty_score":0.009422183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0732156394606833,"score_gpt":0.2797650239628332,"score_spread":0.2065493845021499,"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."}}