Bibliographic record
Abstract
Individuals from the same population typically show consistent differences in behavioural traits that are frequently associated with differences in contextual plasticity. Yet such a correlation might arise either because some individuals are better able than others to detect environmental changes or because the benefits of being plastic are condition-dependent. To discriminate between these two competing hypotheses, I developed an individual-based model that simulates a population in which individuals of varying fighting ability compete by pairwise interactions using either the fixed hawk (aggressive) or dove (peaceful) strategies or a conditional assessment strategy. As anticipated, the model predicts that only individuals with low (and/or intermediate) fighting ability should use the assessment strategy, giving rise to a negative (or dome-shaped) relationship between aggressiveness and plasticity. The proportion of plastic individuals, however, should be affected not only by the environmental conditions in which individuals live but also by the mechanism that would maintain variation in the traits that determine the benefits of plasticity. In particular, if individual differences in fighting ability may be eroded by natural selection, it predicts that ecological conditions that cause assortative interactions (e.g. high predation risks) would contribute in maintaining variation among individuals in their fighting ability, thereby favouring greater plasticity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".