Costly retaliation is promoted by threats to resources in women and threats to status in men
Bibliographic record
Abstract
What motivates people to act against their own self-interest? In men, what seems to be irrational decision-making in the short-term may be explained by other long-term benefits; thus retaliation may not be motivated by tangible costs, but instead intangible psychological variables (e.g., status threats). In contrast, there is evidence that women are more sensitive to tangible costs than are men. In Experiment 1, using the Point Subtraction Aggression Paradigm (PSAP), we tested the prediction that in men, the frequency of provocation, and not the monetary loss (tangible cost), would be associated with retaliation, whereas women would be sensitive to the tangible costs. In keeping with the prediction, women (n = 80) who incurred greater tangible costs (irrespective of frequency) retaliated with more costly punishment, whereas men (n = 90) who were provoked more frequently (irrespective of tangible costs) retaliated with more costly punishment. In Experiment 2, we directly investigated whether women were more sensitive to threats to resources and men were more sensitive to threats to status, as suggested by the results of Experiment 1. Women's (n = 53) retaliation was greater when they reported it to be a means to protect their resources, and men's (n = 35) retaliation was greater when they reported it to be a means to protect their status. Thus, these results identify psychological variables that guide retaliation that is costly to the actor. Consistent with evolutionary perspectives, concerns about status appear to drive costly retaliatory behavior more so in men than in women.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".