{"id":"W2161480676","doi":"10.3386/w11813","title":"Racial Sorting and Neighborhood Quality","year":2005,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; National Science Foundation","keywords":"Sorting; Quality (philosophy); Computer science; Geography; Algorithm; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0006196145,0.000103382,0.0002230954,0.0005218681,0.0008995167,0.0009869062,0.0002163829,0.0004035156,0.00691059],"category_scores_gemma":[0.003560221,0.00008664213,0.0001906866,0.001027413,0.0005083636,0.0005195541,0.0007659241,0.0002692411,0.0003608216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009493609,"about_ca_system_score_gemma":0.0004507199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02774745,"about_ca_topic_score_gemma":0.04923346,"domain_scores_codex":[0.9995728,0.0001566041,0.00002132703,0.00007352155,0.00006769784,0.0001080996],"domain_scores_gemma":[0.9985507,0.0003342374,0.0006332744,0.0001260167,0.0001536064,0.0002021973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001627774,0.0002070348,0.9273603,0.00004137866,0.0001229106,0.00007665032,0.001372979,0.006112664,0.0004346638,0.03263303,0.002772211,0.02870333],"study_design_scores_gemma":[0.00003202244,0.0001085682,0.9573887,0.00004663937,0.00006427296,0.00007359689,0.002656664,0.009410498,0.0002272743,0.02205422,0.007916356,0.00002119948],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986331,0.0003171166,0.001430411,0.0006319561,0.00001384044,0.00001885442,0.0005080146,0.00001304218,0.01073574],"genre_scores_gemma":[0.9987367,0.00007828354,0.000257325,0.0000423453,0.000003983487,0.000004589375,0.0001280813,0.000001699742,0.000747088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02774745,"threshold_uncertainty_score":0.05517191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5988421407198125,"score_gpt":0.617048557711178,"score_spread":0.01820641699136549,"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."}}