{"id":"W2792105533","doi":"10.1177/0042098017745235","title":"The knowledge economy city: Gentrification, studentification and youthification, and their connections to universities","year":2018,"lang":"en","type":"article","venue":"Urban Studies","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Research Centre for the Humanities; University of Pennsylvania","keywords":"Gentrification; Census; Urbanism; Context (archaeology); Economic geography; Work (physics); Confidentiality; Urban economics; Spillover effect; Geography; Sociology; Regional science; Economic growth; Political science; Economics; Population; Demography; Civil engineering; Architecture; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0006876214,0.0001607959,0.0003163618,0.002198327,0.003542689,0.003903051,0.0007253005,0.0003205341,0.002202231],"category_scores_gemma":[0.002111305,0.0001206878,0.0002971094,0.003989608,0.004575317,0.0008425596,0.004301162,0.0005428068,0.00008859568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01008576,"about_ca_system_score_gemma":0.01174632,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8221218,"about_ca_topic_score_gemma":0.9179898,"domain_scores_codex":[0.9991411,0.0001747387,0.0000213994,0.00008944871,0.0001542253,0.0004190482],"domain_scores_gemma":[0.9985209,0.0002357858,0.0003970532,0.00008806823,0.0002919067,0.0004662712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006492747,0.00003631197,0.9368282,0.00003418045,0.00003542604,0.0001387049,0.03660421,0.0004163181,0.0002114084,0.01252812,0.0005842485,0.01251782],"study_design_scores_gemma":[0.000002093773,0.00001690642,0.900063,0.00004534207,0.00001986361,0.00004826031,0.09459122,0.0005176188,0.00009673326,0.001046255,0.003537775,0.00001500407],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994996,0.0001565223,0.0001329888,0.0002548498,0.000003670114,0.000006208484,0.0001043804,0.000002141367,0.004343224],"genre_scores_gemma":[0.9995633,0.00008395911,0.00005531035,0.000009283084,0.000001883459,0.000002848525,0.00004471312,0.000001186751,0.0002376101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8221218,"threshold_uncertainty_score":0.3578515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06728526595104951,"score_gpt":0.3373195934159107,"score_spread":0.2700343274648612,"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."}}