Stop Blaming the Victim: A Meta-Analysis on Rape Myths
Bibliographic record
Abstract
Although male rape is being reported more often than before, the majority of rape victims continue to be women. Rape myths-false beliefs used mainly to shift the blame of rape from perpetrators to victims-are also prevalent in today's society and in many ways contribute toward the pervasiveness of rape. Despite this, there has been limited consideration as to how rape prevention programs and policies can address this phenomenon, and there is no updated information on the demographic, attitudinal, or behavioral factors currently associated with rape myths. This research aimed to address this gap by examining the correlates of rape-myths acceptance (RMA) in published studies. A total of 37 studies were reviewed, and their results were combined using meta-analytic techniques. Overall, the findings indicated that men displayed a significantly higher endorsement of RMA than women. RMA was also strongly associated with hostile attitudes and behaviors toward women, thus supporting feminist premise that sexism perpetuates RMA. RMA was also found to be correlated with other "isms," such as racism, heterosexism, classism, and ageism. These findings suggest that rape prevention programs and policies must be broadened to incorporate strategies that also address other oppressive beliefs concurrent with RMA. Indeed, a renewed awareness of how RMA shapes societal perceptions of rape victims, including perceptions of service providers, could also reduce victims' re-victimization and enhance their coping mechanisms.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".