{"id":"W1997616723","doi":"10.2202/1935-1690.2117","title":"The Impact of Aggregate and Sectoral Fluctuations on Training Decisions","year":2010,"lang":"en","type":"article","venue":"The B E Journal of Macroeconomics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Acadia University; Toronto Metropolitan University","funders":"","keywords":"Economics; Productivity; Shock (circulatory); Position (finance); Training (meteorology); Aggregate (composite); Production (economics); Order (exchange); Reservation; Work (physics); Labour economics; Macroeconomics; Computer science; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001580598,0.0002328881,0.0004644718,0.000482323,0.0003944472,0.001847738,0.0006099272,0.0009115995,0.00420805],"category_scores_gemma":[0.006305742,0.0002267524,0.0003863957,0.0009940559,0.0006612408,0.0006382348,0.000618294,0.0009591518,0.0004011157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002686745,"about_ca_system_score_gemma":0.001353614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0905216,"about_ca_topic_score_gemma":0.09123593,"domain_scores_codex":[0.9990333,0.0002277657,0.00004318215,0.0001540269,0.0001253035,0.000416373],"domain_scores_gemma":[0.9963529,0.001452556,0.001184508,0.0002647274,0.0002573182,0.0004879746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007479798,0.0002612775,0.4852786,0.0001211572,0.0002161493,0.0006145653,0.0005650112,0.4332776,0.003360184,0.02810032,0.00504536,0.04241174],"study_design_scores_gemma":[0.0000414555,0.0002143802,0.7273365,0.00005190519,0.0001081598,0.00009220806,0.001430138,0.2493166,0.001299758,0.01548743,0.004545896,0.00007564502],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848557,0.000344535,0.004889942,0.001632858,0.00002854238,0.00002741836,0.001796813,0.00006432887,0.00635994],"genre_scores_gemma":[0.9982226,0.0001284415,0.0001654963,0.00005007157,0.000008469788,0.000004144392,0.0002263633,0.000004744931,0.00118965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0905216,"threshold_uncertainty_score":0.1799894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03790258031250134,"score_gpt":0.2787805868991856,"score_spread":0.2408780065866842,"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."}}