{"id":"W4405980061","doi":"10.1001/amajethics.2025.44","title":"How Should Epidemiologists Respond to Data Genocide?","year":2025,"lang":"en","type":"article","venue":"The AMA Journal of Ethic","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Genocide; Colonialism; Data collection; Criminology; Political science; Data quality; Coronavirus disease 2019 (COVID-19); Public relations; Sociology; Medicine; Law; Social science; Business; Pathology","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":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01258329,0.0001137121,0.0003756603,0.0002263683,0.001255129,0.00002284583,0.001429489,0.0003137261,0.0001859215],"category_scores_gemma":[0.01506227,0.00007088995,0.00007286539,0.0004321632,0.0001225247,0.0002142016,0.0006372235,0.002891174,0.0001075352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001268315,"about_ca_system_score_gemma":0.0009535963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001576874,"about_ca_topic_score_gemma":0.0006587557,"domain_scores_codex":[0.9957424,0.002533437,0.0008348254,0.0001713916,0.0003011444,0.0004167761],"domain_scores_gemma":[0.9924489,0.005310637,0.0006030301,0.0009878658,0.0004705887,0.0001789242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005865767,0.00003744614,0.01653842,0.0003238121,0.0001455796,0.00002362208,0.01055248,0.0001396532,0.0002360402,0.1078529,0.859453,0.00411046],"study_design_scores_gemma":[0.0007714956,0.0002002051,0.1071864,0.0009490325,0.000115839,0.00001145146,0.006224073,0.0001867524,0.00001285599,0.1360443,0.7481647,0.0001329082],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1028206,0.002236517,0.005134606,0.881934,0.002766889,0.0005952044,0.00007992339,0.00002855515,0.004403638],"genre_scores_gemma":[0.9223844,0.0006285838,0.004524103,0.06455577,0.0009358533,0.00001041143,0.00001187703,0.00001404266,0.006934885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8195639,"threshold_uncertainty_score":0.9994092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.70782241303012,"score_gpt":0.6167767111945951,"score_spread":0.09104570183552485,"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."}}