{"id":"W4416905555","doi":"10.1177/00420980251385783","title":"Entering and leaving housing assistance: Neighborhood trajectories of housing voucher recipients in the United States","year":2025,"lang":"en","type":"article","venue":"Urban Studies","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Voucher; Microdata (statistics); Poverty; Census; Context (archaeology); Renting; Microsimulation; Public housing; Rental housing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0008043339,0.0001739136,0.0003663865,0.0002566057,0.0009397665,0.00009604718,0.0001814877,0.00005678722,0.000004139496],"category_scores_gemma":[0.0007084715,0.0001323845,0.00005204363,0.001169781,0.0006774885,0.0001965503,0.000110715,0.000150685,4.863873e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001346858,"about_ca_system_score_gemma":0.00005460083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001364628,"about_ca_topic_score_gemma":0.004908112,"domain_scores_codex":[0.9984109,0.0002906723,0.0003741255,0.000254256,0.0003370187,0.0003330753],"domain_scores_gemma":[0.9985023,0.0009466062,0.0001499878,0.0001484366,0.0002280793,0.00002462443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003013399,0.00005583344,0.7741437,0.00007132164,0.0001953369,0.000003510846,0.2143183,0.00002139323,0.00003427763,0.007014946,0.001828145,0.002283028],"study_design_scores_gemma":[0.001114194,0.00007683429,0.3326933,0.001038822,0.0001770682,3.651583e-7,0.6293731,0.0001625117,0.0001933087,0.00675811,0.02799275,0.0004195951],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738172,0.0167848,0.0002647266,0.001280686,0.0005291784,0.0002638385,0.000007642159,0.0000634049,0.006988483],"genre_scores_gemma":[0.9951538,0.003858403,0.00009409394,0.0002491229,0.00009483309,0.00002163584,0.000002477364,0.000009412526,0.0005162434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4414504,"threshold_uncertainty_score":0.7228019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082726354842286,"score_gpt":0.3422295420611098,"score_spread":0.2914022785126869,"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."}}