{"id":"W1556717025","doi":"10.7202/029089ar","title":"Toward a More Competent Labour Force: A Training Levy Scheme for Canada","year":2005,"lang":"en","type":"article","venue":"Relations industrielles","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Université Laval","funders":"","keywords":"Scheme (mathematics); Training (meteorology); Productivity; Commission; Work (physics); Economics; Labour economics; Computer science; Economic growth; Engineering; Finance; Mathematics; Geography; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003884909,0.0001404672,0.0002702252,0.0000988067,0.0002122653,0.00005316133,0.0001723967,0.00016862,0.0002675248],"category_scores_gemma":[0.0003061474,0.0001635348,0.00008505767,0.000265899,0.00003189598,0.0001440149,0.00003513249,0.0002403418,0.00001946225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000322698,"about_ca_system_score_gemma":0.0003463468,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02877489,"about_ca_topic_score_gemma":0.0339375,"domain_scores_codex":[0.9987843,0.00001010901,0.000563555,0.000285279,0.00005104312,0.0003056811],"domain_scores_gemma":[0.9992099,0.0001542652,0.0002348239,0.0002359269,0.00006665033,0.00009844801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009688802,0.0000386434,0.02305268,0.000006065829,0.00006898279,0.000001640323,0.0008524628,0.001616056,0.000003884256,0.9668779,0.003279584,0.004192378],"study_design_scores_gemma":[0.001166162,0.00003197591,0.01409489,0.00002713366,0.00001180339,0.000004714209,0.001457553,0.04617982,0.00002217099,0.0356276,0.9009162,0.0004600413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533545,0.0008754962,0.01245112,0.01912341,0.0003341096,0.0004474508,0.001868845,0.00005220791,0.01149282],"genre_scores_gemma":[0.9868147,0.00003271435,0.005208145,0.0005167315,0.0002238461,0.00005466699,0.0001022783,0.00002332435,0.007023622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9312503,"threshold_uncertainty_score":0.9836906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06584500759039057,"score_gpt":0.2422292291263515,"score_spread":0.1763842215359609,"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."}}