Competitive Disadvantage Makes Attitudes towards Rape Less Negative
Bibliographic record
Abstract
Evolutionary theorists have argued that perceived competitive disadvantage may lead to more positive evaluation of, and greater likelihood of engaging in, risky and antisocial behavior. However, experimental studies have not yet examined the effects of competitive disadvantage on perceptions of rape. In the current study, we created a manipulation of perceived competitive status to test its effects on beliefs about rape. In one condition, participants were made to feel disadvantaged relative to male peers in terms of financial, physical, and intellectual power, whereas in the other condition they were made to feel advantaged. Participants were 120 heterosexual male undergraduate students. The manipulation was effective; compared to participants in the advantage condition, those in the disadvantage condition rated themselves as significantly worse off financially, shorter, in worse physical shape, and as having lower course marks than the average male student at the university. Compared to perceived competitive advantage, perceived disadvantage led to less negative attitudes towards rape. However, perceived competitive status did not significantly affect justifications and excuses for rape. Future studies using similar experimental manipulations can complement correlational studies and may contribute to greater clarity, precision, and sophistication of research and theory on the role of competitive disadvantage in rape.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".