How Should Epidemiologists Respond to Data Genocide?
Bibliographic record
Abstract
Data quality for and about American Indian/Alaska Native (AI/AN) people is undermined by deeply entrenched, colonial practices that have become standard in US federal data systems.This article draws on cases of maternal mortality and COVID-19 to demonstrate the ethical and clinical need for inclusive, diverse, and accurate data when researching AI/AN health trends.This article further argues that epidemiologists specifically must challenge implicit bias, question methods and practices, and recognize colonial, racist reporting practices about AI/AN people that have long undermined data collection, analytical, and dissemination practices that are fundamental to epidemiological research.The American Medical Association designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 Credit™ available through the AMA Ed Hub TM .Physicians should claim only the credit commensurate with the extent of their participation in the activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.492 | 0.730 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.029 | 0.042 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.063 | 0.077 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".