{"id":"W1999063682","doi":"10.1111/j.1541-0064.2001.tb01174.x","title":"The changing face of Canada: the uneven geographies of population and social change","year":2001,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; University of Toronto","funders":"","keywords":"Population; Diversity (politics); Metropolitan area; Immigration; Population growth; Face (sociological concept); Economic geography; Sociology; Welfare state; Politics; Social change; Population ageing; Development economics; Political science; Economic growth; Geography; Economics; Social science","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":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0008476306,0.0002598596,0.0003528259,0.001444848,0.005389973,0.00008430781,0.0004896733,0.000116046,0.00001313432],"category_scores_gemma":[0.0001493674,0.0001945285,0.0001787675,0.004528526,0.002945775,0.000217318,0.00005272289,0.0001542806,1.241122e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008085586,"about_ca_system_score_gemma":0.0003408404,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9993248,"about_ca_topic_score_gemma":0.9999954,"domain_scores_codex":[0.997511,0.000200532,0.0004252696,0.0002857279,0.0004591763,0.001118334],"domain_scores_gemma":[0.9984531,0.0003410071,0.0003285644,0.0002799141,0.0002987159,0.0002986311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001880191,0.000009863688,0.5415854,0.00005092003,0.0002849288,0.00001047018,0.3586772,0.000004236522,0.000001974022,0.08111399,0.007143727,0.01109842],"study_design_scores_gemma":[0.0001231169,0.0000272093,0.7567945,0.00002858394,0.00006575287,0.000003510431,0.09774709,0.000006720633,0.000002310325,0.002037061,0.142926,0.0002380967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779888,0.008830669,0.000001287515,0.01122595,0.000541286,0.0005312787,0.0001146057,0.00002955483,0.000736566],"genre_scores_gemma":[0.9874583,0.01149157,0.00000833863,0.0002716082,0.0002998131,0.00007574305,0.00002218171,0.0000205454,0.0003518657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2609302,"threshold_uncertainty_score":0.9997677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130171027812391,"score_gpt":0.2149780043369398,"score_spread":0.2019609015557007,"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."}}