Kin selection models for the co-evolution of the sex ratio and sex-specific dispersal
Bibliographic record
Abstract
We investigate the co-evolutionary relationship between sex-ratio bias and sex-specific dispersal behaviour using an inclusive fitness approach. We consider two models: (i) DDM, in which dispersal of both sexes occurs before mating; and (ii) DMD, in which male dispersal precedes mating and female dispersal follows mating. Under DDM, at equilibrium, there is no bias in either the sex ratio or the sex-specific dispersal rates unless the sex-specific dispersal costs are different. However, under DMD, and at equilibrium, equal sex-specific dispersal costs imply a female bias in the sex ratio and a female dispersal rate at least as great as that of males. The present work highlights the role of sex differences – in either dispersal costs or the timing of dispersal – to the co-evolution of the sex ratio and dispersal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".