{"id":"W2142496726","doi":"10.1111/0022-4146.00240","title":"Exploring the Canadian‐U.S. Unemployment and Nonemployment Rate Gaps: Are There Lessons for Both Countries?","year":2001,"lang":"en","type":"article","venue":"Journal of Regional Science","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unemployment; Economics; Unemployment rate; Demographic economics; Labour economics; Population; Economic growth; Demography","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":[0.003926492,0.0004737333,0.001124616,0.004754029,0.004475045,0.004000343,0.001818282,0.001439802,0.006193034],"category_scores_gemma":[0.009703449,0.0002880176,0.00102206,0.01242692,0.001732531,0.003920748,0.002389137,0.001846877,0.0003377502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02928278,"about_ca_system_score_gemma":0.08091263,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9900795,"about_ca_topic_score_gemma":0.9953399,"domain_scores_codex":[0.9979827,0.0001643649,0.00005519562,0.0001609912,0.0004610597,0.00117562],"domain_scores_gemma":[0.9939681,0.00151806,0.0009726615,0.0001555395,0.002431218,0.0009544117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004424364,0.0002114771,0.6733418,0.001326566,0.000507832,0.0005074343,0.0136379,0.002994294,0.0002743473,0.08763393,0.04484705,0.1742751],"study_design_scores_gemma":[0.00003159154,0.00007930314,0.9114263,0.0009172697,0.0002651123,0.00009342081,0.04027372,0.001988185,0.0002534508,0.007681821,0.03689036,0.0000994577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7275586,0.08091763,0.001855283,0.09462246,0.0006136835,0.000121014,0.0168341,0.00009130259,0.07738585],"genre_scores_gemma":[0.9743469,0.01401714,0.001370966,0.002878441,0.0001384065,0.00004135365,0.003245946,0.00003008132,0.003930827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02928278,"threshold_uncertainty_score":0.2124624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2489461325627785,"score_gpt":0.4185881570846214,"score_spread":0.1696420245218429,"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."}}