{"id":"W1566175038","doi":"10.1111/caje.12421","title":"Occupational mobility and the returns to training","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Toronto","funders":"Economic and Social Research Council","keywords":"Human capital; Government (linguistics); Training (meteorology); Labour economics; Economics; Capital (architecture); Selection (genetic algorithm); Demographic economics; Business; Psychology; Economic growth; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.000795819,0.0001500926,0.0003188502,0.0009696647,0.0004930574,0.001178844,0.0004273425,0.0006551057,0.01813888],"category_scores_gemma":[0.007792317,0.00009022996,0.0002420306,0.001406582,0.000692618,0.0007742724,0.001119021,0.0009025064,0.001181807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009644511,"about_ca_system_score_gemma":0.0005216701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01407696,"about_ca_topic_score_gemma":0.01016153,"domain_scores_codex":[0.9994516,0.0001296697,0.00002585783,0.00004998646,0.00008121159,0.0002616041],"domain_scores_gemma":[0.9935576,0.002477962,0.002044853,0.0002678967,0.0003564964,0.001295159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004427216,0.0004679823,0.8686861,0.0001414067,0.0001592335,0.0007609367,0.001303348,0.01309899,0.0005540294,0.05440807,0.005654569,0.0543226],"study_design_scores_gemma":[0.0000210355,0.0001157177,0.9533647,0.0001109613,0.00003401927,0.000285932,0.00299918,0.00646257,0.0002679715,0.02701683,0.00930307,0.00001804422],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726844,0.001629486,0.001111767,0.004263023,0.00004845668,0.00001573004,0.000855401,0.00002262714,0.01936916],"genre_scores_gemma":[0.9970163,0.0002275345,0.00003971829,0.00004393262,0.00003864813,0.000003929172,0.0001365801,0.000002537972,0.002490922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01813888,"threshold_uncertainty_score":0.06068063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1964649793321989,"score_gpt":0.1977185383843804,"score_spread":0.001253559052181441,"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."}}