{"id":"W4200301764","doi":"10.32920/17049434.v1","title":"Toronto: Planning for Diversity, Inclusion and Urban Resilience","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban and Rural Development Challenges","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metropolitan area; Settlement (finance); Diversity (politics); Psychological resilience; Immigration; Inclusion (mineral); Political science; Work (physics); Politics; Geography; Phenomenon; Resilience (materials science); Economic growth; Economic geography; Sociology; Business; Social science; 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":[0.0009413783,0.0004615287,0.0002013333,0.0007404311,0.01114933,0.005732942,0.0009004849,0.001234355,0.02669895],"category_scores_gemma":[0.001870292,0.0003191905,0.0003225869,0.002086069,0.003839252,0.001863052,0.00469924,0.00165836,0.001910142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03493116,"about_ca_system_score_gemma":0.08368313,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8165812,"about_ca_topic_score_gemma":0.9264904,"domain_scores_codex":[0.9990885,0.0003972773,0.00002124741,0.0000675321,0.0001730087,0.0002524057],"domain_scores_gemma":[0.997916,0.00029941,0.000090671,0.0001325956,0.0003116214,0.00124957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003016555,0.00003884428,0.006332842,0.0003451847,0.00001945303,0.00139872,0.09855289,0.002808245,0.0005509393,0.3133816,0.4727636,0.1037775],"study_design_scores_gemma":[0.000003802077,0.00001261667,0.005618715,0.0002325019,0.000008782039,0.00009756621,0.05895792,0.0005136007,0.0002145643,0.01630459,0.9180187,0.00001659449],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06949007,0.01024732,0.01167452,0.1450154,0.001138644,0.0003589471,0.002626976,0.000532082,0.7589161],"genre_scores_gemma":[0.6963983,0.009393341,0.02308348,0.003338614,0.0001669452,0.0002915662,0.001480552,0.0003549536,0.2654923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1834188,"threshold_uncertainty_score":0.3689979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05616135379160599,"score_gpt":0.3350517771167548,"score_spread":0.2788904233251489,"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."}}