Bibliographic record
Abstract
Turn now from the question “when does cure become enhancement?” to “when does cure become cultural genocide?” Suppose that we eliminate deafness, plain facial features, or obesity. Would we not wipe out the cultural traditions associated with those conditions, from ASL, to “beauty is only skin deep” stories to the comedy of Dawn French? Of course, not all of those harboring any given condition will opt for a cure once one is available. Those worried about cultural genocide, however, acknowledge this. Their concern is that whatever damage a phenotypic cure does to a group's cultural tradition, it will begin far below the level at which every group member takes it. And it will simply grow more serious as more do take it. The question is: How exactly should we conceive that damage? What claim could group members who value their condition on cultural grounds lodge against medicine for developing a cure, and against society for funding and permitting it through the state? Much of the debate concerning cultural genocide, and I'm of course speaking specifically of cultural genocide debate surrounding attempts to draw limits to medicine, pivots on groups' claims to have evolved a culture. But cultural status, especially for the particular groups in question, is a vexing thing to measure. So I suggested in the Introduction that in place of our conventional focus on the meaning of “cultural” in “cultural genocide,” we concentrate instead on the meaning of “genocide.”
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".