{"id":"W4402658216","doi":"10.24908/cpp-apc.v2024i2.17254","title":"Making room for everyone","year":2024,"lang":"en","type":"article","venue":"Canadian Planning and Policy / Aménagement et politique au Canada","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Business; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002037547,0.0006789128,0.0005934301,0.0007132931,0.01474107,0.007688706,0.001773929,0.001732499,0.169924],"category_scores_gemma":[0.007732739,0.0002661364,0.0005098109,0.0006394095,0.004748255,0.00690807,0.0131246,0.004754983,0.07167631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005230744,"about_ca_system_score_gemma":0.01844483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09901968,"about_ca_topic_score_gemma":0.2530816,"domain_scores_codex":[0.9966699,0.0006385829,0.00005906188,0.0004526314,0.001167146,0.001012711],"domain_scores_gemma":[0.9936796,0.0002011553,0.0001500358,0.0004337784,0.00139298,0.004142419],"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":[0.00001407218,0.00002802214,0.001332272,0.00006654869,0.000008272767,0.0002248935,0.01093058,0.00002063596,0.0003003668,0.01460047,0.898431,0.07404295],"study_design_scores_gemma":[0.000001328423,0.00000520246,0.000468909,0.00006096168,0.000002170479,0.00007604081,0.005988929,0.000006020674,0.00002414202,0.0009393696,0.9924216,0.000005292718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01029538,0.004996642,0.003797357,0.1500013,0.01345274,0.0002294334,0.0009107735,0.001130994,0.8151854],"genre_scores_gemma":[0.09049599,0.003848714,0.003376379,0.0509487,0.001398788,0.0001444552,0.000794901,0.0007849623,0.8482071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9009803,"threshold_uncertainty_score":0.5684525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509411865746572,"score_gpt":0.3668198720509511,"score_spread":0.3117257533934853,"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."}}