{"id":"W2161362441","doi":"","title":"Neighbourhood Inequality in Canadian Cities","year":2000,"lang":"en","type":"article","venue":"Analytical Studies Branch Research Paper Series","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Neighbourhood (mathematics); Economic inequality; Inequality; Demographic economics; Earnings; Income inequality metrics; Economics; Census tract; Social inequality; Labour economics; Geography; Socioeconomic status; Sociology; Population; 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":[0.000501148,0.0002881784,0.0004702091,0.004745211,0.003911131,0.002016059,0.0009229968,0.0003452172,0.004984478],"category_scores_gemma":[0.003392286,0.0002122821,0.0006968102,0.01521,0.0006473698,0.0006670738,0.001791839,0.0005053392,0.0002975792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02955578,"about_ca_system_score_gemma":0.01894534,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947402,"about_ca_topic_score_gemma":0.9973727,"domain_scores_codex":[0.9988809,0.00007065341,0.00004358363,0.000163134,0.0004227473,0.0004189603],"domain_scores_gemma":[0.9983085,0.0001015357,0.0003322852,0.00007941935,0.0008387944,0.0003394402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008340333,0.00002670835,0.9562331,0.0001387723,0.0001191334,0.00018742,0.003749928,0.001772655,0.0001187794,0.003906928,0.009576607,0.02408657],"study_design_scores_gemma":[0.000002421465,0.000003761413,0.9935608,0.00003495784,0.00001338824,0.00002389927,0.001450225,0.0004455247,0.00001765482,0.000181415,0.004254432,0.00001154623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9324849,0.003095469,0.0005453379,0.001280176,0.00005716654,0.00008159519,0.02861576,0.00004627055,0.03379348],"genre_scores_gemma":[0.9914904,0.0007541317,0.000327755,0.0000649175,0.000007943508,0.00003282657,0.005564468,0.000008586097,0.001748946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02955578,"threshold_uncertainty_score":0.2144432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1639911430226249,"score_gpt":0.4447958811205407,"score_spread":0.2808047380979158,"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."}}