“I Felt Powerful and Confident”: Women’s Use of What They Learned in Feminist Sexual Assault Resistance Education
Bibliographic record
Abstract
Research on women's response and resistance to sexual assault risk has informed the development of interventions to improve women's ability to effectively resist sexual assault. However, little is known about how women anticipate, navigate, and respond to risk following participation in sexual assault risk reduction/resistance education programs. In this study, we examined the information and skills used by university women who had recently completed the effective Enhanced Assess, Acknowledge, Act (EAAA) sexual assault resistance program. We analyzed responses from 445 women using descriptive statistics and content and thematic analysis. Just under half (42%) of women used at least one EAAA strategy in the following 2 years. Most women reported that their efforts were successful in stopping an attack. Women's responses included strategies both to preempt sexual assault threat (e.g., avoiding men who display danger cues, communicating assertively about wanted and unwanted sex) and to interrupt or avoid an imminent threat (e.g., yelling, hitting, and kicking). Women's use of resistance strategies worked to subvert gendered social norms and socialization. The results suggest that counter to criticisms that risk reduction/resistance programs blame women or make them responsible for stopping men's violence, women who took EAAA typically positioned themselves as agentic and empowered in their resistance.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".