{"id":"W3174530213","doi":"10.1002/jae.2877","title":"Dynamic treatment effects of job training","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Earnings; Relevance (law); Economics; Discrete choice; Econometrics; Training (meteorology); Continuation; Computer science; Accounting","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":[0.008751504,0.0006428258,0.002079973,0.0008683681,0.0007435306,0.00211218,0.00206816,0.001495509,0.04428426],"category_scores_gemma":[0.02256252,0.0004748755,0.002327033,0.000997636,0.001541345,0.001190161,0.001657066,0.003576542,0.002089675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002177687,"about_ca_system_score_gemma":0.001766792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009004437,"about_ca_topic_score_gemma":0.005644132,"domain_scores_codex":[0.9927524,0.004274674,0.0002350204,0.001099407,0.0005399655,0.001098458],"domain_scores_gemma":[0.9753474,0.01773591,0.003224436,0.002318589,0.0005251077,0.0008486414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.01328735,0.005126963,0.2569646,0.001217918,0.004355178,0.0006366649,0.001132727,0.3086211,0.002894886,0.1529093,0.01846669,0.2343866],"study_design_scores_gemma":[0.003237364,0.006679994,0.3372578,0.0009138671,0.004559468,0.0003372941,0.002533819,0.4508705,0.007633917,0.1483556,0.03725195,0.0003684678],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8909832,0.00191933,0.0657092,0.006819687,0.0004675903,0.00111597,0.007564716,0.0006686797,0.02475168],"genre_scores_gemma":[0.9846097,0.0002186369,0.002496787,0.0004217119,0.000119053,0.000272797,0.000781667,0.00003701657,0.01104258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04428426,"threshold_uncertainty_score":0.1481456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140853435375063,"score_gpt":0.2273393093458169,"score_spread":0.1959307749920662,"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."}}