Bibliographic record
Abstract
Clustering humans based on their genetic ancestry is a common practice in human genomics. Genetically similar populations can be seen as statistical constructs that are labeled by population descriptors such as “race,” “ethnicity,” and “genetic ancestry.” Recently, there has been a shift towards replacing the descriptor “race” with “genetic ancestry” because the latter is considered more objective. A descriptor is deemed objective if it adequately captures an underlying feature of the biological world, such as genetic similarities or differences between human sub-populations. However, claims of objectivity do not sufficiently explain the rationale for the choice and use of population descriptors such as “ancestry.” This paper proposes an axiological approach to capture the choice and use of population descriptors in human genomics, by showing that the population descriptor “ancestry” is value-laden and that there is a legitimate role for values in the choice and use of population descriptors in genomics. • Use of population descriptors in genomics has come under scrutiny over the past years. • “Genetic ancestry” is not a neutral descriptor when compared to “race”. • Population descriptors are value laden. • A value-based accounts provides a philosophical contribution to ongoing debates.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.070 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".