Do Health and Forensic DNA Databases Increase Racial Disparities?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The issue of the digital divide is a growing concern in health and forensic DNA databases, reflecting structural disparities in biomedical research and policing. Over the last decade, the majority of DNA samples in population studies are from individuals of European origin. Individuals from Asian, African, Latino, and aboriginal groups are underrepresented. Forensic DNA databases are growing to mirror racial disparities in arrest practices and incarceration rates. Individuals from African American and Latin1o groups are overrepresented in forensic from health DNA databases. Currently, there is little recognition in national and international public policy circles about the “digital divide” in health and law enforcement databases. To avoid reproducing structural patterns of racial inequality, regulators, policy makers, scientists, and law enforcement officials need to address these disparities by supporting policies and mechanisms designed to better protect individuals and groups through institutional practices, law, and securely encrypted digital codes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it