{"id":"W2997515780","doi":"10.1093/shm/hkz115","title":"Managing Colonial Diets: Wartime Nutritional Science on the Korean Population, 1937–1945","year":2019,"lang":"en","type":"article","venue":"Social History of Medicine","topic":"Japanese History and Culture","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Backwardness; Colonialism; Scholarship; State (computer science); Empire; Population; Political science; Biopower; Intervention (counseling); Sociology of scientific knowledge; Nutritional science; Economic growth; Social science; Political economy; Economic history; Sociology; Environmental health; Medicine; History; Law; Biology; Economics; Food science; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001577576,0.00009740896,0.000200963,0.00009280967,0.001068345,0.000004907862,0.0003860538,0.00008337985,0.001959025],"category_scores_gemma":[0.0002778888,0.00007225935,0.00006478094,0.0003330771,0.003542406,0.0001534357,0.00002185177,0.0001925244,0.0000816217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423172,"about_ca_system_score_gemma":0.0003847582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001533299,"about_ca_topic_score_gemma":0.0004207695,"domain_scores_codex":[0.9980875,0.0001781235,0.0002071101,0.0002203134,0.00106431,0.0002425983],"domain_scores_gemma":[0.9993212,0.0001174806,0.0001650952,0.0001393957,0.0001775227,0.00007934582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001233831,0.0001124635,0.001327071,0.00003651532,0.00001660101,0.000003334528,0.2309669,0.000003869242,0.003025262,0.5209253,0.2419764,0.001482925],"study_design_scores_gemma":[0.0007340421,0.0001812059,0.01417826,0.0001112895,0.00002848566,7.801112e-7,0.01110689,0.00001125916,0.00001069403,0.01525991,0.9582082,0.0001689982],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4111862,0.0009285026,0.000002956693,0.05810317,0.005153926,0.0005556985,0.000007571877,0.00009415051,0.5239679],"genre_scores_gemma":[0.9782235,0.00001462943,0.00001015932,0.001467391,0.001237685,0.000008761333,0.000009250552,0.000007253674,0.01902138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7162318,"threshold_uncertainty_score":0.9991694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293528555186997,"score_gpt":0.2773809121876377,"score_spread":0.2544456266357678,"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."}}