{"id":"W1583486431","doi":"","title":"Employee Training in Canada","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","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":"Receipt; Training (meteorology); Demographic economics; Payroll; Merge (version control); Literacy; Political science; Psychology; Business; Economics; Geography; Accounting; Pedagogy; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.0005642312,0.0001941876,0.0002718517,0.001711975,0.002501354,0.001711545,0.0007585121,0.0004907337,0.01075549],"category_scores_gemma":[0.002352651,0.0001777507,0.0003756615,0.003854146,0.0002725275,0.0003458795,0.0006837972,0.0009931939,0.0008532863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03433138,"about_ca_system_score_gemma":0.06148975,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951034,"about_ca_topic_score_gemma":0.9979967,"domain_scores_codex":[0.9985555,0.00004519599,0.00003891975,0.0001070129,0.000541861,0.0007114869],"domain_scores_gemma":[0.9974783,0.0001969481,0.0003029922,0.00004861,0.00115566,0.000817429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001664204,0.0001328138,0.7325184,0.0003432748,0.00008128701,0.0004548494,0.003030346,0.001854711,0.000513192,0.01112826,0.1016302,0.1481461],"study_design_scores_gemma":[0.00001306631,0.00002472174,0.9110374,0.0001168077,0.00001645933,0.00006828002,0.001831091,0.0007889236,0.0001853394,0.0001883812,0.08571037,0.00001916384],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8013715,0.00612406,0.001193796,0.01104935,0.0003313216,0.0002114282,0.07266089,0.000181319,0.1068764],"genre_scores_gemma":[0.9124969,0.002653987,0.0008475975,0.001894383,0.00006687859,0.00008021192,0.017844,0.00003262497,0.06408338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03433138,"threshold_uncertainty_score":0.2490928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05739179871756978,"score_gpt":0.2864929327839606,"score_spread":0.2291011340663908,"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."}}