{"id":"W3175677986","doi":"10.3138/utq.90.2.11","title":"Urbanization and Ageing: Ageism, Inequality, and the Future of “Age-Friendly” Cities","year":2021,"lang":"en","type":"article","venue":"University of Toronto Quarterly","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Urbanization; Urbanism; Inequality; Injustice; Context (archaeology); Economic growth; Social inequality; Sociology; Population ageing; Population; Urban planning; Political science; Economic geography; Development economics; Geography; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002161481,0.00004901844,0.0001361755,0.00001048443,0.0003734321,0.00002034709,0.00007521979,0.00003934099,0.00008283082],"category_scores_gemma":[0.00001784533,0.00004481966,0.00003026634,0.00003986073,0.0004609422,0.0002225426,0.00002358098,0.00002811902,2.156181e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002316605,"about_ca_system_score_gemma":0.00004058253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1185727,"about_ca_topic_score_gemma":0.4970688,"domain_scores_codex":[0.9994248,0.0001835088,0.00007656888,0.0000983336,0.0001417465,0.00007505965],"domain_scores_gemma":[0.9995924,0.00007782433,0.00008740798,0.00008061996,0.0001334516,0.00002835219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001915553,0.00001394807,0.003040173,0.00002596371,0.00003308897,0.00001023282,0.9043325,2.311099e-7,0.00001746959,0.08679365,0.001555568,0.004158013],"study_design_scores_gemma":[0.001346165,0.0001383487,0.2469894,0.00003192014,0.0001009877,9.795212e-7,0.6748557,0.00002979946,0.00001303405,0.00567864,0.07067157,0.0001434033],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830809,0.005264622,0.0007673932,0.001469063,0.0001243244,0.00009604038,0.000009195206,0.00001848094,0.009170013],"genre_scores_gemma":[0.9953611,0.001516227,0.0002061519,0.0000210377,0.00008949069,7.111032e-8,0.000003066992,0.000001912678,0.002800904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3784961,"threshold_uncertainty_score":0.8872967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007447729096418629,"score_gpt":0.2179963645354658,"score_spread":0.2105486354390472,"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."}}