{"id":"W2117409225","doi":"","title":"REGIONAL DISPARITIES IN CANADA: INTERPROVINCIAL OR URBAN/RURAL?","year":2011,"lang":"en","type":"article","venue":"Region et Developpement","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Census; Rurality; Lagging; Geography; Rural area; Urbanity; Economic growth; Demographic economics; Regional science; Economic geography; Socioeconomics; Political science; Economics; Demography; Economy; Population; Sociology","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":[0.0006434844,0.0001752351,0.000357305,0.001817508,0.002682721,0.001589321,0.0004337791,0.0002870864,0.003978072],"category_scores_gemma":[0.002469701,0.00007668205,0.0003734377,0.004865868,0.001621227,0.0005964095,0.001402279,0.0007566928,0.0001097324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01962102,"about_ca_system_score_gemma":0.03213753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9866806,"about_ca_topic_score_gemma":0.9939731,"domain_scores_codex":[0.9989319,0.00009632835,0.00002788992,0.0001118037,0.0003228964,0.000509113],"domain_scores_gemma":[0.9988545,0.0002028426,0.0002510715,0.00005090713,0.0003920594,0.0002485225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001381864,0.00004438784,0.7098598,0.000367369,0.0002026561,0.0004319677,0.008387784,0.00364198,0.0006538031,0.1489187,0.01642962,0.1109238],"study_design_scores_gemma":[0.00001303164,0.00002635588,0.9295402,0.0002699308,0.0001351154,0.0001411205,0.01842405,0.002325897,0.0003510239,0.01334889,0.03537364,0.00005079401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.859235,0.008356933,0.004747452,0.02242981,0.0001381131,0.00009955405,0.005839638,0.00005725097,0.09909631],"genre_scores_gemma":[0.9939743,0.001592492,0.0008730779,0.0005358615,0.00001968824,0.000009148604,0.0004088673,0.000007225445,0.002579446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01962102,"threshold_uncertainty_score":0.1423611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07962281304341491,"score_gpt":0.212331858508872,"score_spread":0.1327090454654571,"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."}}