Bibliographic record
Abstract
A TRAIN RIDE In 1982 I had the pleasure of riding in a train across part of southern Ontario in Canada with the late Bill Hamilton who many think introduced the greatest innovation in the theory of evolution since Darwin – the theory which came to be known as “kin selection” or “inclusive fitness” (1964 a,b). Hamilton had pointed out that selection would act on (and hence calculations of fitness should take into account) not only the effect of our genes on our own behaviour, but also their effect on relatives, because the latter, to varying degrees depending upon the relationship, are carriers of the same genes identical by descent. For example, a gene which influenced one to assure the survival of a little more than two full siblings at the cost of one's own life would be favoured by selection because, on average, it would be transmitted through a relative rather than personally. His insight, model and initially suggested applications went on to give rise to a vast lineage of research on cooperation among relatives in nature. It was this work that stimulated Edward O. Wilson to write his Sociobiology: The New Synthesis (1975) which had caused such a stir among social scientists while I was a graduate student. We were leaving a conference in Kingston Ontario and ended up on the same train. I was getting off at my home, Toronto, while he was going on further.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.303 | 0.150 |
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".