One big, and many small reasons that direct selection on offspring number is still open for discussion
Bibliographic record
Abstract
In a recent paper, I proposed that natural selection should act to increase offspring number when diversification bet hedging is favoured. The simple underlying reasoning is that a target diversification strategy is more reliably generated with increasing sample size. The intention of opening a discussion has been realized; recent criticisms of the idea argue that selection does not act to increase offspring number when population size is large or infinite. Here I agree that criticisms have merit; indeed they are largely confined to the caveats discussed in my original paper. The critique, however, implies a verdict of outright rejection of the idea of selection on offspring number, which would be erroneous. Contrary to the assertions of the criticism, then, the importance of selection acting directly on offspring number remains an open question.
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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.025 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".