The Effect of Gender Role Expectations, Sexism, and Rape Myth Acceptance on the Social Perception of Sexual Violence: A Meta-Analysis
Bibliographic record
Abstract
Recent systematic reviews have highlighted the role of certain attitudes in shaping the social perception of sexual violence. This meta-analysis aimed to assess the effect of sexism, gender-role expectations, and rape myth acceptance on the social perception of sexual violence. It also aims to determine whether attitudinal dimensions, participant gender, or the dyadic composition of sexual violence incidents moderate this relationship. A literature search covered published and unpublished manuscripts from January 1, 2010, to December 31, 2023, across PsychArticles, Proquest, and Google Scholar. Manuscripts had to measure at least one attitude and assess participants’ perception of material depicting sexual violence between two adults. Data from 40 studies from 10 different countries, involving 12,283 participants, revealed a significant association between attitudes and the social perception of sexual violence ( r = .425, p < .001). Meta-regression highlighted the significant contributions of greater gender role expectations, hostile sexism, benevolent sexism, and rape myth acceptance to the social perception of sexual violence incidences. Their combined effect correlated with increased victim blame ( r = .558), reduced victim credibility ( r = −.492), decreased perception of incident severity ( r = −.363) and victim trauma ( r = −.390), heightened perceptions of victim pleasure ( r = .417), as well as reduced perpetrator blame ( r = −.288) and sanction severity ( r = −.191). These tendencies were even more pronounced in cases involving same-gender perpetrators and victims. Participant gender was not a significant moderator. These findings underscore the need for prevention efforts to address broader gender biases and power dynamics.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.039 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".