{"id":"W4254252556","doi":"10.32920/ryerson.14640003","title":"Toronto: planning for diversity, inclusion and urban resilience","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metropolitan area; Diversity (politics); Settlement (finance); Immigration; Psychological resilience; Inclusion (mineral); Geography; Work (physics); Political science; Economic growth; Economic geography; Global city; Resilience (materials science); Development economics; Sociology; Business; Gender studies; Engineering","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":[0.001212573,0.0005760858,0.0002588338,0.000865981,0.007463581,0.004741244,0.001081,0.001226011,0.04853017],"category_scores_gemma":[0.002568368,0.0003297278,0.0003699824,0.002370217,0.003418194,0.002047951,0.005458182,0.001713358,0.004724951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03290097,"about_ca_system_score_gemma":0.06981293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7943502,"about_ca_topic_score_gemma":0.9045835,"domain_scores_codex":[0.9988254,0.0005146707,0.00002893548,0.00008875655,0.0002702706,0.0002719431],"domain_scores_gemma":[0.9974436,0.0003131988,0.00009040786,0.0001847884,0.0005264229,0.001441759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002530124,0.0000179145,0.00275529,0.0002908083,0.00001229688,0.0004763576,0.02613956,0.001196607,0.0002068476,0.1255961,0.7538173,0.08946575],"study_design_scores_gemma":[0.000003125458,0.000007802189,0.002522932,0.0002967,0.000005817275,0.00005976457,0.01418647,0.0002162377,0.00009636186,0.008382761,0.9742109,0.00001114739],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02014413,0.01892635,0.01159076,0.170942,0.00192268,0.0003215635,0.007294293,0.0009542765,0.767904],"genre_scores_gemma":[0.5171371,0.0239599,0.03333,0.007392501,0.0005001627,0.0005491947,0.006886255,0.001016832,0.4092281],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2056498,"threshold_uncertainty_score":0.4137218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03816966572112383,"score_gpt":0.3431166236426073,"score_spread":0.3049469579214835,"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."}}