Sexual Violence and Women’s Education: Examining Academic Performance and Persistence
Bibliographic record
Abstract
Sexual violence continues to be a serious problem on university campuses. While the negative psychological and health effects of sexual violence are well known, it is only recently that attention has focused on how these behaviours impact education, particularly women’s education. This study contributes to this area and examined the impact of types of sexual violence on behavioural and attitudinal indicators of academic performance and persistence among students reporting sexual violence. Undergraduate women attending university in Ontario, Canada (N= 934) responded to survey measures of academic performance, attitudes towards education and sexual violence experiences. The results indicate that sexual violence has a deleterious impact on women’s academic performance including and beyond grades. Women students who experienced sexual violence reported more delays and failures on assignments, courses and exams, were more likely to endorse attendance problems and thoughts of dropping out or quitting than students not reporting sexual violence. Type of sexual violence experienced was also related to academic performance. The results are discussed in terms of the need to understand components of academic performance as well as factors that may contribute to outcomes for students. Findings have implications for intervention and policy development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".