Bibliographic record
Abstract
Naturalists have long provided the general backcloth against which issues of health care and public health are constructed. In seeking the general case through studies of human and non-human species, naturalists provide a context in which specific arguments about human states of disease and health (and responsibility for them) are set. Think, for example, of Stephen J Gould and his Mismeasure of Man;1 and for a different perspective consider EO Wilson,2 called by some admirers ‘Darwin’s natural heir’.3 Indeed, consider not the heirs but Charles Darwin himself. Famously, his evolutionary theory insisted upon competition for eternally scarce resources between individuals of the same species (as well as between species) as the critical engine of evolutionary advancement. His enduring legacy helped to shape scientific and social thinking into the 21st century. At one scale it gave us Richard Dawkin’s The Selfish Gene,4 genetics as a kind of competitive zero-sum game. At another, the assumption of selfish competition enforces a principle underlying laissez faire economic theories in which competition between individuals is assumed to be inevitable, natural and efficient.
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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.047 | 0.044 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".