Taking charge and stepping in: Individuals who punish are rewarded with prestige and dominance
Bibliographic record
Abstract
Abstract A hallmark of human societies is the scale at which we cooperate with many others, even when they are not closely genetically related to us. One proposed mechanism that helps explain why we cooperate is punishment; cooperation may pay and proliferate if those who free ride on the cooperation of others are punished. Yet this ‘solution’ raises another puzzle of its own: Who will bear the costs of punishing? While the deterrence of free‐riders via punishment serves collective interests, presumably any single individual—who has no direct incentive to punish—is better off letting others pay the costs of punishment. However, emerging theory and evidence indicate that, while punishment may at times be a costly act, certain individuals are better able to ‘afford’ to pay the price of punishment and are often consequentially rewarded with fitness‐enhancing reputation benefits. Synthesizing across these latest lines of research, we propose a novel framework that considers how high status individuals—that is, individuals with greater prestige or dominance—enjoy lower punishment costs. These individuals are thus more willing to punish, and through their punitive action can in turn reap reputational rewards by further gaining more prestige or dominance. These reputational gains, which work in concert to promote the social success of punishers, recoup the costs of punishing. Together, these lines of work suggest that while punishment is often assumed to be altruistic, it need not always depend on altruism, and motivations to punish may at times be self‐interested and driven (whether consciously or unconsciously) by reputational benefits.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".