Bibliographic record
Abstract
In many animals, the winners of a fight are more likely to win subsequent contests, while the losers tend to lose their following fights. Such winner and loser effects can have a large influence on individual behavior and fitness. Recent studies indicate that winner and loser effects occur in humans as well. Here we provide a narrative review of the relevant similarities and distinctions between nonhumans and humans with the goal of assessing the causes and consequences of winner and loser effects in humans. In both nonhumans and humans, winner and loser effects probably guide individuals to behave according to their apparent social rank, with winners adopting assertive postures and losers becoming submissive. Physical formidability is the dominant dimension determining social rank in nonhuman species. In adult humans, physical formidability plays a lesser role, while social conventions, physical attractiveness, competence in complex skills, and social competence are more important for social rank. Recent data indicate that human winner and loser effects may influence behavior and social rank in nonaggressive contexts. We suggest future lines of research that will help us better understand how and why winner and loser effects shape human cognition, mood, and behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".