Women’s perceptions of, and emotional responses to, sexual violence depicted in film or series
Bibliographic record
Abstract
Sexual violence, particularly against women, is alarmingly common. Many survivors experience post-traumatic stress ( Cortina & Kubiak, 2006 ); thus, reminders of the trauma could cause flashbacks, dissociative symptoms, and intense fear ( American Psychiatric Association, 2013 ). Given that women consume media that regularly depicts sexual violence, which could cause distress, the current study examined women’s perceptions of, and emotional responses to, scenes of sexual violence. It was predicted that women would perceive scenes of sexual violence negatively and that would be particularly true for women with a sexual violence history, those who reported post-traumatic stress disorder symptoms related to sexual violence history, and those who use negative coping strategies in response to stressors. Participants were women ( n = 229) who completed an online survey. More than half (52%) of participants reported that they had experienced sexual violence. Participants generally reported negative perceptions of scenes of sexual violence, with a majority viewing them as too graphic, used for shock value/titillation, and unnecessary to the plot. Women with a history of sexual violence reported greater avoidance of media that might contain sexual violence and greater negative affect in response to scenes of sexual violence; however, women who exceeded the post-traumatic stress disorder screen cut-off did not report greater avoidance and negative affect than those who did not exceed the cut-off. Finally, those who reported a greater tendency to cope with stressors using problem avoidance reported more avoidance of, and negative affect in response to, scenes of sexual violence. Exploratory analyses, limitations, and future directions are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".