{"id":"W2013108856","doi":"10.1177/0049124105280198","title":"Mapping Social Distance","year":2005,"lang":"en","type":"article","venue":"Sociological Methods & Research","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multidimensional scaling; Ethnic group; Census; Metropolitan area; Immigration; Diversity (politics); Geography; Social distance; Sociology; Social group; Racial diversity; Census tract; Cultural diversity; Geographical distance; Economic geography; Regional science; Demography; Demographic economics; Social science; Statistics; Mathematics; Anthropology; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005583155,0.0003970628,0.0002996772,0.007297107,0.001061586,0.002003712,0.0005048332,0.0004684281,0.007176226],"category_scores_gemma":[0.00609723,0.000137584,0.0004381109,0.006187335,0.0004872404,0.001379953,0.002301313,0.0003785917,0.00172455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006141687,"about_ca_system_score_gemma":0.0005755317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008951135,"about_ca_topic_score_gemma":0.009242971,"domain_scores_codex":[0.9988495,0.0003438917,0.00006434631,0.0002827484,0.0003555387,0.0001040422],"domain_scores_gemma":[0.998381,0.0007071233,0.0002249989,0.0002301185,0.0003562575,0.000100555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001995543,0.0001903474,0.2537895,0.0006454251,0.0002252469,0.0004521729,0.01397368,0.01232159,0.006245315,0.05452901,0.01076645,0.6466617],"study_design_scores_gemma":[0.00004888682,0.000420279,0.536755,0.0004110018,0.0001558089,0.001725487,0.05260046,0.09654545,0.009727921,0.1262121,0.1751808,0.0002168629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7456446,0.001262352,0.1692599,0.0005626908,0.0001350252,0.0003427188,0.009966224,0.0008560746,0.0719705],"genre_scores_gemma":[0.9355955,0.0003915147,0.05643403,0.00002758813,0.00001914454,0.0001806971,0.003002002,0.00008006662,0.004269463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008951135,"threshold_uncertainty_score":0.02400684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.496170104316695,"score_gpt":0.6084512661669489,"score_spread":0.1122811618502539,"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."}}