{"id":"W3096405725","doi":"10.1080/01419870.2020.1828598","title":"Putonghua vs. minority languages: distribution of language laws, regulations, and documents in mainland China","year":2020,"lang":"en","type":"article","venue":"Ethnic and Racial Studies","topic":"China's Ethnic Minorities and Relations","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Federation for the Humanities and Social Sciences","keywords":"Language policy; Mainland China; China; Minority language; Constitution; Ethnic group; Hierarchy; Linguistic demography; Autonomy; Official language; Linguistics; Multilingualism; Law; Distribution (mathematics); Language planning; Political science; Sociology of language; Natural language; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007950318,0.0001467479,0.0002180108,0.005794011,0.0004748176,0.0008338479,0.0003007835,0.0001643805,0.001759412],"category_scores_gemma":[0.001993469,0.0001133083,0.0001700306,0.01055705,0.000421918,0.000501761,0.000707525,0.0001831782,0.0001925023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001876624,"about_ca_system_score_gemma":0.002711926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1479465,"about_ca_topic_score_gemma":0.2265886,"domain_scores_codex":[0.9993642,0.00007991717,0.0001143049,0.0001022035,0.0001955535,0.000143943],"domain_scores_gemma":[0.996842,0.0006450295,0.001477742,0.0001436498,0.000537655,0.0003539813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000463411,0.00002144235,0.98741,0.00006953069,0.00002552693,0.000149402,0.002079294,0.0001147209,0.000317508,0.0003458172,0.0005121659,0.008908194],"study_design_scores_gemma":[0.000001652878,0.000007914929,0.9969669,0.00001071782,0.000006746293,0.00002719586,0.001706345,0.0001379566,0.00008770246,0.00001882209,0.001025096,0.00000301563],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952456,0.0001999384,0.00002941909,0.00004885801,0.00000206585,0.00001540108,0.003089898,0.000004031213,0.001364738],"genre_scores_gemma":[0.9942577,0.0002823533,0.0001223577,0.00002548283,0.000003187278,0.00002977196,0.004307743,0.000002035072,0.0009692477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1479465,"threshold_uncertainty_score":0.2941707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02998182612360066,"score_gpt":0.362741280395323,"score_spread":0.3327594542717223,"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."}}