Winner and Loser Effects and Social Rank In Humans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it