{"id":"W2134614409","doi":"10.1002/psp.1991","title":"Neighbours Helping Neighbours in Multi‐ethnic Context","year":2015,"lang":"en","type":"article","venue":"Population Space and Place","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; University of Toronto","funders":"","keywords":"Neighbourhood (mathematics); Ethnic group; Friendship; Social capital; Immigration; Census; Sociology; Population; Interpersonal ties; Social relation; Demographic economics; Social psychology; Geography; Psychology; Demography; Social science; Economics","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":[0.001238447,0.0001438283,0.0003412955,0.0009827042,0.003934792,0.002384974,0.0006189594,0.0004882789,0.005921322],"category_scores_gemma":[0.004113714,0.000133415,0.0001776203,0.001227389,0.001828147,0.001349765,0.003473172,0.0005664817,0.0002929058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508187,"about_ca_system_score_gemma":0.001306432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04263689,"about_ca_topic_score_gemma":0.1105734,"domain_scores_codex":[0.9987283,0.0007544035,0.00005034023,0.0001049197,0.0001797315,0.0001821886],"domain_scores_gemma":[0.9979772,0.0005169575,0.0005583473,0.0001017624,0.0002135054,0.0006322808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001629846,0.000240178,0.6089157,0.0003564082,0.0001560735,0.001383053,0.3205383,0.0002983388,0.000398239,0.01746577,0.004778968,0.04530599],"study_design_scores_gemma":[0.00001142969,0.0000903531,0.4056279,0.0003516304,0.00006329548,0.0005479254,0.5628695,0.0002715963,0.000104647,0.002647508,0.02738018,0.00003402099],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826315,0.001323758,0.0001203143,0.001023218,0.00004290613,0.000007526351,0.00004993839,0.000002856896,0.01479818],"genre_scores_gemma":[0.999016,0.0003256106,0.00004141206,0.00004824953,0.000007738286,0.000002577318,0.000009763386,0.000001142902,0.0005474855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04263689,"threshold_uncertainty_score":0.08477747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1402388050753798,"score_gpt":0.366240423886821,"score_spread":0.2260016188114413,"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."}}