Intrasexual Competition as a Predictor of Women’s Judgments of Revenge Pornography Offending
Bibliographic record
Abstract
Recent legislative developments have led to a marked increase in the empirical investigation of motivations and judgments of so-called acts of “revenge pornography” offending. In two independently sampled studies, we used moderation analyses to investigate whether higher levels of intrasexual competition predicted more lenient judgments of revenge pornography offenses as a function of sex (Study 1, N = 241), and whether such relationships would be further moderated by physical attractiveness (Study 2, N = 402). Potential covariates of callous-unemotional traits, empathy, and victimization history were controlled for. Opposing our hypotheses, we consistently observed a trend for higher levels of intrasexual competition being associated with more lenient judgments of revenge pornography offenses involving male victims by female participants. The results are discussed in terms of intrasexual competition potentially sharing variance with unobserved constructs in the wider sexological literature, and of the key relevance of these findings for future empirical investigation into judgments of nonconsensual image–based offending.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".