Bibliographic record
Abstract
When it comes to determining whether a condition is a medical one, philosophers such as Leon Kass or Christopher Boorse advance an essentially biological approach. They want us to look to the biological norms of the human species – our genetic, cellular, and organic functioning – for the foundations of what medicine should and should not do. Their understandable concern is that without such a biological anchor, we would have no grounds for refusing someone like Dartmouth Medical School professor Joseph Rosen, who ended a medical conference some years ago by “pounding the table … and announcing that, were he given permission by a medical ethics board, he would try to engineer a person to have wings.” But as many critics have urged, and as I too argue in the Introduction, there is a serious weakness in the biological functioning approach to species normality. Unless we at the same time consult social norms, it tells us very little. Perhaps, as a proposition of biological functioning, our legs aren't even meant to carry us upright, as Dorothy Dinnerstein suggests. Nor would a reliance on natural biological functioning seem to allow medicine to concern itself with the vision sufficient for night driving. But once we admit that medicine's task is to take us to whatever happens to be socially normal, we then have to accept that the social norm itself is always evolving.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".