Understanding sexual consent and nonconsensual sexual experiences in undergraduate women: The role of identification and rape myth acceptance
Bibliographic record
Abstract
The current study examined how nonconsensual sexual experiences (NSE) and the self-identification of such experiences as sexual assault play a role in the relationship between rape myth acceptance and sexual consent attitudes. Undergraduate women ( N = 296) completed measures on sexual consent attitudes, their sexual experiences, and rape myth acceptance. Participants were categorized into three groups: those with no NSEs, those with NSEs who identify them as sexual assault (identifiers), and those with NSEs who do not identify them as sexual assault (non-identifiers). Multiple regression analyses to test the moderating effects of group membership on the relationship between rape myth acceptance and sexual consent attitudes were conducted. Results indicated that non-identifiers reported less positive attitudes toward establishing consent and more indirect behavioural approaches to consent than both identifiers and those with no NSE histories. Greater rape myth acceptance was significantly related to a lack of perceived behavioural control and less positive attitudes toward establishing consent in identifiers and those with no NSEs, as well as less awareness and discussion around consent in those with no NSEs. Conversely, rape myth acceptance was not significantly associated with any consent attitudes in non-identifiers. The findings suggest that NSE identification, or a lack of identification of NSEs as sexual assault, is significantly related to sexual consent attitudes that are independent of rape myth acceptance. These findings are discussed in terms of sexual violence education and prevention and future research considerations.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".