{"id":"W2737645210","doi":"10.3390/socsci7080123","title":"The Geography of Economic Segregation","year":2018,"lang":"en","type":"article","venue":"Social Sciences","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inequality; Race (biology); Economic geography; Economic inequality; Demographic economics; Economics; Ethnic group; Geography; Sociology; Gender studies","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003822048,0.000129201,0.0002396892,0.001715277,0.001245682,0.001538858,0.0002423714,0.0001931793,0.003233214],"category_scores_gemma":[0.003595454,0.0001015046,0.0001398295,0.002360437,0.001985442,0.0009213923,0.002084054,0.0003180981,0.0001899509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110061,"about_ca_system_score_gemma":0.0008153578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02090099,"about_ca_topic_score_gemma":0.02585458,"domain_scores_codex":[0.9992372,0.0002790261,0.00004427833,0.000109063,0.0001643993,0.0001660077],"domain_scores_gemma":[0.9989887,0.0001704118,0.0004486071,0.00007632007,0.0001543473,0.0001616016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006630294,0.00003525379,0.9086209,0.0001094905,0.00008271713,0.0004644954,0.006271576,0.0007616961,0.0007692699,0.0388952,0.001805546,0.04211751],"study_design_scores_gemma":[0.000002925491,0.00002885597,0.9727225,0.00009042279,0.00001995165,0.0003143971,0.008154798,0.0003376764,0.0001351814,0.006343203,0.01183999,0.00001009597],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655137,0.002336001,0.00156005,0.001942714,0.00002814417,0.00001710338,0.0003542035,0.00001272362,0.02823537],"genre_scores_gemma":[0.998923,0.0004759249,0.0001594506,0.00003214361,0.00001418039,0.000003390769,0.00006190994,0.000002806102,0.0003271262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02090099,"threshold_uncertainty_score":0.04155868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04625715071557591,"score_gpt":0.3614736780639814,"score_spread":0.3152165273484055,"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."}}