The post‐Human Genome Project mindset: race, reliability, and health care
Bibliographic record
Abstract
The following essay reports on the first session of a 2-day workshop on genetic diversity and science communication, organized by the Institute of Genetics. I argue that the four talks in this session reflected two different facets of a 'post-Human Genome Project (HGP)' view of human genetics. The first is characterized by an increasing interest in genetic differences. Two speakers - Troy Duster and Jasber Singh - expressed skepticism about one aspect of this trend: an emphasis on race in medicine and genetics. The other two speakers - Kenneth Weiss and Gustavo Turecki - spoke to a second facet of the post-HGP view: a recognition of the difficulty in translating genetic discovery into medical or public health applications. Though both sets of talks were highly critical of current trends in genetic research, they pulled in opposite directions: one warned about the role of genetics in stabilizing racial categories, while the other lamented the failure of any genetic claims or categories to stabilize at all. I argue that the use of racial categories in medicine seems likely to encounter scientific, medical, and social challenges.
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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 | 0.052 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".