{"id":"W4385753284","doi":"10.1016/j.habitatint.2023.102899","title":"The temporal sequence between gentrification and cycling infrastructure expansions in Montreal, Canada","year":2023,"lang":"en","type":"article","venue":"Habitat International","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Simon Fraser University; University of Saskatchewan; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Gentrification; Cycling; Census; Logistic regression; Equity (law); Green infrastructure; Geography; Economic geography; Demographic economics; Demography; Economics; Economic growth; Political science; Population; Environmental planning; Sociology; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0003372776,0.00004864486,0.00005393548,0.00004036415,0.0003289899,0.00006956765,0.0002194839,0.00003463946,0.00002970536],"category_scores_gemma":[0.0001605112,0.0000386871,0.00001506198,0.0002053081,0.0001191752,0.0001585229,0.00001799471,0.00009929574,0.000003696151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000136691,"about_ca_system_score_gemma":0.0002758146,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.628755,"about_ca_topic_score_gemma":0.9760868,"domain_scores_codex":[0.9991998,0.00004279195,0.0001673077,0.0001386383,0.0003010693,0.0001503631],"domain_scores_gemma":[0.9995399,0.000231807,0.0000493344,0.00007049565,0.00005719399,0.00005128011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002632177,0.000002087925,0.9921363,0.000001127861,0.000004791844,0.000003846812,0.001168773,0.00002737074,0.00002436917,0.0005520768,0.00189736,0.004179291],"study_design_scores_gemma":[0.00007503411,0.000001494048,0.9813532,0.000007584206,0.000001959495,7.71504e-8,0.001730094,0.0002391805,0.00001052832,0.004593387,0.01194298,0.00004443621],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918248,0.00003880717,0.00002843136,0.005743313,0.0003213645,0.00009685731,0.00004881694,0.00002543328,0.001872193],"genre_scores_gemma":[0.9989433,0.00006529034,0.00003970339,0.0000508435,0.0001500886,0.00001053498,0.00007833404,0.00000297216,0.0006589815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3473318,"threshold_uncertainty_score":0.3737172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02911736239076861,"score_gpt":0.3138013119656746,"score_spread":0.2846839495749059,"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."}}