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.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".