{"id":"W4233446531","doi":"10.32920/ryerson.14651613","title":"Spatial income inequality in Toronto: a longitudinal study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Neighbourhood (mathematics); Economic inequality; Inequality; Spillover effect; Demographic economics; Economics; Income distribution; Household income; Geography; Economic geography","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":[0.0004884511,0.0002131453,0.0002828085,0.001206975,0.002149154,0.001155806,0.0004915506,0.0003051083,0.002887071],"category_scores_gemma":[0.002086373,0.0002568749,0.0003881652,0.004348412,0.0005768517,0.0007087377,0.001496023,0.000659213,0.0003611527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009527996,"about_ca_system_score_gemma":0.005158573,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9323881,"about_ca_topic_score_gemma":0.9577851,"domain_scores_codex":[0.9995133,0.00006677027,0.00003309206,0.00008695472,0.0001311154,0.0001687842],"domain_scores_gemma":[0.9984393,0.0001172679,0.0004987504,0.0001350152,0.0004482637,0.0003614915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005309323,0.00003994432,0.9846345,0.00002189403,0.00003430684,0.0001709168,0.00936216,0.00010722,0.0001227617,0.0002469115,0.001290572,0.003915869],"study_design_scores_gemma":[0.000001042102,0.00001509591,0.9952656,0.00001525698,0.000007158786,0.00002899118,0.003751125,0.0001190962,0.00001713185,0.00002417529,0.0007509263,0.000004388872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958376,0.0001881829,0.00006580163,0.0001737956,0.000004720574,0.00001331777,0.002231684,0.000003874282,0.001481121],"genre_scores_gemma":[0.9971089,0.0001789436,0.00008328469,0.00003044756,0.000004326392,0.00001956123,0.001604343,0.000002503661,0.0009678337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06761193,"threshold_uncertainty_score":0.1360202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09067605171719852,"score_gpt":0.3972175063081568,"score_spread":0.3065414545909583,"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."}}