{"id":"W4391618177","doi":"10.32920/25169546.v1","title":"Gentrifying Toronto: identifying and quantifying neighbourhood change","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Urban Planning and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gentrification; Disinvestment; Economic geography; Neighbourhood (mathematics); Geography; Demographic economics; Regional science; Investment (military); Sociology; Economic growth; Political science; Economics","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":[0.0004232928,0.0002385051,0.000202762,0.00225954,0.001772123,0.001852192,0.0004803297,0.0003328209,0.001739171],"category_scores_gemma":[0.002930805,0.000144381,0.0002899214,0.004571887,0.001213361,0.0007633301,0.002172067,0.000259225,0.0001162862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01195188,"about_ca_system_score_gemma":0.00485208,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8390825,"about_ca_topic_score_gemma":0.933656,"domain_scores_codex":[0.9995964,0.00009093375,0.0000181106,0.00007964438,0.0001386278,0.00007629996],"domain_scores_gemma":[0.9989938,0.0001950612,0.0002911411,0.0001049849,0.0002380328,0.0001768513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001239118,0.00002856789,0.8664708,0.0003454668,0.0001758525,0.0003423996,0.04164219,0.01001782,0.001618482,0.01054333,0.004511883,0.06417931],"study_design_scores_gemma":[0.000003150974,0.00003394016,0.9588253,0.00009851153,0.00004469552,0.00005264722,0.02184203,0.006667585,0.000572815,0.001339707,0.01049469,0.00002507765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830898,0.0005907849,0.003159632,0.0002582539,0.00001520285,0.00006570496,0.003048577,0.00004655693,0.009725439],"genre_scores_gemma":[0.9957664,0.0001956449,0.002123788,0.00001073035,0.000002431566,0.0000236936,0.0009201396,0.000006255219,0.0009509387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1609175,"threshold_uncertainty_score":0.3237303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1858507319028509,"score_gpt":0.3989584492689329,"score_spread":0.2131077173660821,"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."}}