{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128579,0.0001622911,0.0001687977,0.0003238137,0.0008902703,0.0002643216,0.0001436965,0.00007265303,0.00004082933],"category_scores_gemma":[0.000298874,0.0001746337,0.00004213049,0.0002932456,0.00008911632,0.0001641269,0.00002338971,0.0001253816,0.000002936803],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001685847,"about_ca_system_score_gemma":0.01141893,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9985843,"about_ca_topic_score_gemma":0.9995975,"domain_scores_codex":[0.9983889,0.00009189495,0.0001865341,0.000277168,0.000220379,0.00083508],"domain_scores_gemma":[0.9988842,0.000396079,0.00003656699,0.0001160046,0.00006158194,0.0005055873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002893938,0.000002001507,0.005989522,0.00008748761,0.00008652081,0.00005850352,0.01906192,0.00003948895,7.795035e-7,0.5787441,0.3948243,0.001102472],"study_design_scores_gemma":[0.00009442103,0.00001568917,0.007292356,0.0001428779,0.00002307633,0.000002625273,0.005429501,0.0001182595,0.000002696157,0.002197849,0.9844691,0.0002115636],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02724017,0.007990654,0.00102855,0.1222227,0.002611951,0.001210011,0.0016939,0.0002957039,0.8357064],"genre_scores_gemma":[0.9690425,0.00009403958,0.00006113709,0.01063517,0.001240228,0.00005859933,0.00003826549,0.0000225109,0.01880755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9418023,"threshold_uncertainty_score":0.9941854,"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."}}