Unrelated helpers will not fully compensate for costs imposed on breeders when they pay to stay
Bibliographic record
Abstract
Unrelated subordinates may invest in costly help to avoid being evicted from groups (the 'pay-to-stay' hypothesis). However, the effectiveness of eviction to enforce help should depend on its being applied accurately and on the costs it imposes on both dominants and subordinates. The relative cost of being evicted is a function of the population frequency of eviction when population growth is limited by density-dependent factors. We describe a stage-structured pay-to-stay model incorporating density-dependent population growth, costly eviction and occasional errors. Breeders demand some amount of help and evict subordinates that do not provide it. Helpers decide on the amount of help they will provide. The threat of eviction alone is sufficient to enforce helping. However, helping will not be favoured if helpers do not impose costs on breeders. The amount of help provided is less than the cost that subordinates impose upon breeders, when any help is provided. Thus, the net fitness effect of a helper under pay-to-stay alone is negative, even if it is investing in cooperative behaviour. Constraints on dispersal have no effect on the amount of help, although they may influence the tolerance threshold of breeders and group stability, depending on the mechanism of density dependence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".