Bibliographic record
Abstract
The future of evolutionary medicine: sparking innovation in biomedicine and public healthThe concept of evolutionary medicine was created about 40 years ago at the University of Michigan's Museum of Zoology when the psychiatrist Randy Nesse walked into the office of Bill Hamilton with a new theory about senescence that was quite wrong.The field thus had a precarious early childhood but was soon guided into strapping adolescence by two works: the 1991 treatise 'Dawn of Darwinian Medicine' by George Williams and Nesse and the 1993 book 'Evolution of Infectious Disease' by Paul Ewald.A string of landmark successes followed: a brilliant monograph on 'Why We Get Sick' by Nesse and Williams, an international society, a thriving Oxford journal, a 'Club EvMed' seminar series led by Charlie Nunn, and a dedicated textbook co-authored by Steve Stearns.It was a paradigm of cross-disciplinary insights, with Randy Nesse tirelessly rallying the troops and Darwin himself taking point.What could possibly go amiss?
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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".