{"id":"W4200631953","doi":"","title":"NATIONALISM AS A MECHANISM: ANALYSIS OF XINJIANG COTTON BAN INCIDENT","year":2021,"lang":"en","type":"article","venue":"","topic":"Global Political and Economic Relations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nationalism; Mechanism (biology); Political science; Ancient history; Geography; History; Law; Philosophy; Politics; Epistemology","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":[0.001709411,0.0002415469,0.0002479486,0.002906142,0.003726131,0.003383852,0.0007752218,0.0009446227,0.005555314],"category_scores_gemma":[0.002839833,0.0002349825,0.000332147,0.002656394,0.001953183,0.0019285,0.001829641,0.001893261,0.0004593922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003692749,"about_ca_system_score_gemma":0.002639515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02273306,"about_ca_topic_score_gemma":0.02815485,"domain_scores_codex":[0.9985984,0.0003212641,0.00008663487,0.0001566312,0.0003597552,0.0004772831],"domain_scores_gemma":[0.9972569,0.001024777,0.0009600155,0.0001004327,0.000323998,0.0003339103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002298899,0.0006334329,0.6479201,0.0005693072,0.0001013593,0.008197735,0.2447907,0.0006963026,0.001718203,0.03783483,0.0105992,0.04670895],"study_design_scores_gemma":[0.000008609726,0.00006800052,0.524341,0.0002940549,0.00003935125,0.0005539484,0.4497551,0.00123173,0.0005749636,0.001946133,0.02114808,0.00003904338],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765856,0.0003718304,0.0004126258,0.001378861,0.00005721417,0.00007868967,0.0001758573,0.00001132359,0.02092805],"genre_scores_gemma":[0.9947148,0.0004449655,0.0002067753,0.0001811169,0.00003038758,0.00004467184,0.000211524,0.000007316886,0.004158427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02273306,"threshold_uncertainty_score":0.04520148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02078163788868144,"score_gpt":0.3225531998663233,"score_spread":0.3017715619776419,"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."}}