Bibliographic record
Abstract
Abstract Personality represents individual behavioural differences that are consistent over time and across situations. In humans and many taxa of nonhuman animals, individuals differ in the way they react to novel and challenging situations, and these differences affect resource acquisition, social interactions, survival and reproduction. Researchers in fields like psychology, behaviour genetics or behavioural ecology have developed different methods to study and measure personality differences, each method being characterised by its own advantages and limitations. The results associated with each method provide different colours to the concept of personality. The main goal of students of animal personality is to understand why individuals differ in their behavioural responses to identical stimuli, and why they often show sets of correlated behaviour traits. Several adaptive theoretical hypotheses offer promising explanations for the maintenance of personality differences in wild populations, although empirical confirmation of their predictions is still needed. Key Concepts: Personality represents individual behavioural differences that are consistent over time and across situations. The concept ‘individual consistency’ only makes sense with the population as a referent. Individual differences define the evolutionary potential of a population. Individual differences within a population, differences between populations and potentially between species, are all expressions of genetic/environmental differences at different levels of organisation. Rating, coding and experimental manipulation, are the three approaches to study personality in animals: each approach has advantages and limitations. Individual behavioural differences are key to understanding many ecological processes and patterns. We distinguish five evolutionary mechanisms that can explain the maintenance of personality differences in wild populations: trade‐offs between life history traits; spatial‐temporal heterogeneity; frequency‐dependent selection; antagonistic selection; correlational selection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".