{"id":"W3157199677","doi":"10.59962/9780774862042-009","title":"Montreal: The Changing Drivers of Inequality between Neighbourhoods","year":2020,"lang":"en","type":"book-chapter","venue":"University of British Columbia Press eBooks","topic":"Social Sciences and Governance","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Inequality; Economic geography; Sociology; Demographic economics; Regional science; Geography; Mathematics; Economics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004511214,0.0003375063,0.00024507,0.001497691,0.004160064,0.006354417,0.00121766,0.0009040642,0.03171915],"category_scores_gemma":[0.001901233,0.000320196,0.00027338,0.006328583,0.002347096,0.00203545,0.001598809,0.001113781,0.0009918824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0495198,"about_ca_system_score_gemma":0.03637052,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.991327,"about_ca_topic_score_gemma":0.996062,"domain_scores_codex":[0.9995518,0.00006589505,0.000007429167,0.00005208186,0.0001414323,0.0001813145],"domain_scores_gemma":[0.9995466,0.00005804886,0.00004045853,0.00002527357,0.0001471733,0.0001823872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001006646,0.00004927307,0.04158141,0.0002406413,0.00005297513,0.0004983698,0.01900742,0.001731362,0.0005315582,0.3646019,0.4187402,0.1528641],"study_design_scores_gemma":[0.0000209511,0.0000160819,0.2069506,0.0003120043,0.00002672021,0.00006848506,0.01511661,0.0009568806,0.0001562355,0.01374947,0.7625657,0.00006042028],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1481057,0.03383677,0.00160153,0.09297013,0.001200996,0.0001184506,0.01314534,0.000253176,0.708768],"genre_scores_gemma":[0.6759169,0.01345254,0.001255192,0.001682663,0.0002402268,0.00006807406,0.001672425,0.0001218095,0.3055902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0495198,"threshold_uncertainty_score":0.359293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03309056229206098,"score_gpt":0.2298569170942297,"score_spread":0.1967663548021687,"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."}}