Admixture mapping: from paradigms of race and ethnicity to population history
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
Admixture mapping is a whole genome association strategy that takes advantage of population history-or genetic ancestry-to map genes for complex diseases. However, because it uses racial/ethnic groupings to examine differential disease risk, admixture mapping raises ethical and social concerns. While there has been much theoretical commentary regarding the ethical and social implications of population-based genetic research, empirical data from stakeholders most closely involved with these studies is limited. One of the first admixture mapping studies carried out was a scan for Multiple Sclerosis (MS) risk factors in an African-American population. Applying qualitative research methods, we used this example to explore developing views, experiences and perceptions of the ethical and social implications of admixture mapping and other population-based research-their value, risks and benefits, and the future prospects of the field. Additionally, we sought to understand how social and ethical risks might be mitigated, and the benefits of this research optimized. We draw on in-depth, one-on-one interviews with leading population geneticists, genome scientists, bioethicists, and African-Americans with MS. Here we present our findings from this unique group of key informants and stakeholders.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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