{"id":"W4280634533","doi":"10.1177/00207020221097991","title":"Future Responses to Managing Muslim Ethnic Minorities in China: Lessons Learned from Global Approaches to Improving Inter-Ethnic Relations","year":2022,"lang":"en","type":"article","venue":"International Journal Canada s Journal of Global Policy Analysis","topic":"China's Ethnic Minorities and Relations","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Ethnic group; Factoring; China; Unrest; State (computer science); Political science; Prejudice (legal term); Development economics; Socioeconomic status; Sociology; Gender studies; Law; Politics; Economics","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":[0.006390824,0.0006110994,0.0003606339,0.0007697435,0.004638168,0.003762144,0.001370988,0.002267014,0.004938798],"category_scores_gemma":[0.003465109,0.0001690346,0.0005544656,0.0009969472,0.004002977,0.003372113,0.006424692,0.003067738,0.000222222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006118816,"about_ca_system_score_gemma":0.02777822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02987188,"about_ca_topic_score_gemma":0.05836445,"domain_scores_codex":[0.9977303,0.001005428,0.00008712986,0.0001367643,0.0002490842,0.0007912319],"domain_scores_gemma":[0.9971891,0.0005082068,0.0002104448,0.0001438463,0.0003598729,0.001588562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002521304,0.001047454,0.1041984,0.002807435,0.0001932442,0.002633068,0.1607376,0.004450974,0.007213883,0.1387361,0.07318049,0.5045491],"study_design_scores_gemma":[0.000126893,0.0008678247,0.144292,0.002987459,0.0001638812,0.000431407,0.4184639,0.003846385,0.004001393,0.0681283,0.3565123,0.0001780807],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.365579,0.0152771,0.004944714,0.5469841,0.002037638,0.0003606074,0.0002155696,0.0001813235,0.06441996],"genre_scores_gemma":[0.9480786,0.01284648,0.004543112,0.02416503,0.0004456938,0.0002523868,0.0001473717,0.00002973833,0.009491668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02987188,"threshold_uncertainty_score":0.05939603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08744650786503756,"score_gpt":0.3659089232639027,"score_spread":0.2784624153988651,"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."}}