{"id":"W2019688580","doi":"10.7202/050718ar","title":"The Impact of Canadian Training Programs on Long Term Unemployed","year":2005,"lang":"en","type":"article","venue":"Relations industrielles","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Term (time); Selection (genetic algorithm); Selection bias; Computer science; Econometrics; Simple (philosophy); Demographic economics; Econometric model; Actuarial science; Psychology; Economics; Statistics; Machine learning; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001546625,0.0004204077,0.000385225,0.001027418,0.00181874,0.001218912,0.001041186,0.0007531486,0.00451601],"category_scores_gemma":[0.005713582,0.0001511242,0.0005466716,0.00152294,0.0006663729,0.0004001809,0.001070885,0.001215676,0.0002269934],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0322364,"about_ca_system_score_gemma":0.03682936,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9851244,"about_ca_topic_score_gemma":0.9906772,"domain_scores_codex":[0.9985173,0.000185944,0.00002526087,0.00007312794,0.000347909,0.0008504102],"domain_scores_gemma":[0.9974606,0.0005118531,0.0004077379,0.00005119202,0.0007588412,0.000809685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001641014,0.0008791955,0.7598265,0.0003683349,0.0003218002,0.0006051934,0.001511744,0.05663495,0.001869509,0.02606896,0.01169053,0.1385824],"study_design_scores_gemma":[0.00006155125,0.0002279155,0.9682477,0.0001275612,0.0002489946,0.00004947088,0.00196012,0.01682079,0.0006158621,0.001113827,0.01048719,0.00003907777],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732662,0.00165763,0.0009760848,0.004525404,0.00005476481,0.0001060743,0.004184726,0.00003364945,0.0151954],"genre_scores_gemma":[0.9913903,0.0008657766,0.0003611767,0.0001974603,0.00001870668,0.00002802167,0.0009015018,0.000004284286,0.006232753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9677636,"threshold_uncertainty_score":0.2338925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.071326532789613,"score_gpt":0.2629771864714393,"score_spread":0.1916506536818263,"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."}}