The Role of Rape Myth Acceptance, Situational Context, and Gender in Individual’s Perceptions of Image-Based Sexual Abuse Victims and Perpetrators
Bibliographic record
Abstract
Image-based sexual abuse (IBSA), defined as the non-consensual creation, use, and/or distribution of sexually explicit photos, is an under-researched yet common form of violence against women. Victims of this form of violence are often blamed for the abuse they endure, which influences their likelihood to seek help and recover. While in-person sex work stigma is known to increase the likelihood of negative reactions to victims, it is unknown whether women who share their own sexual images online for money are viewed in similar ways. The current study used an experimental vignette design to understand the influence of the context of IBSA, specifically related to how the original image was produced, and gender, on individuals' attributions of blame to a female victim and male perpetrator of IBSA and their empathy for the victim, while controlling for rape myth endorsement. Results showed that participants placed more blame on the victim, less blame on the perpetrator, and displayed less empathy toward the victim when she took the explicit photo herself compared to a victim whose photo was taken by someone else. Moreover, participants blamed a perpetrator of IBSA less when he had paid for access to the explicit photo on a subscription-based website and displayed lower empathy for a victim of IBSA who earned a monetary reward for their explicit photo. On average, women reported more empathy for victims of IBSA compared to men, and individuals of all genders who endorsed rape myths to a greater degree placed more blame on victims of IBSA. This study is the first step in understanding the ways in which the context of image production affects the views of victims and perpetrators of IBSA and provides important information for prevention and education efforts.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".