Bibliographic record
Abstract
Legal, moral, and health-related issues around sexual consent have become prominent in the media in recent years. The public debate surrounding these news stories indicates a large discrepancy in people’s understanding of sexual consent and its legal implications. Motivated by the fact that university students are sexually victimized at rates exceeding the general population, this study explores factors that influence knowledge of legal aspects of sexual consent and confidence in using such knowledge of students/alumni, under 30 years old, from two southern Ontario universities. This quantitative study used an online survey design, and is grounded in a heuristic paradigm, with a feminist perspective. Ten vignette-style questions were developed to evaluate legal sexual consent knowledge. Participants felt relatively confident about their level of knowledge and understanding, and yet their scores on knowledge do not reflect that. Association between variables was examined using bivariate and multiple regression analyses. No factors were found to be statistically significantly associated with level of sexual consent knowledge. A regression model for levels of confidence about sexual consent, accounted for 12.4% of the variance. Implications for research, practice, and policy are discussed, with an emphasis on educational interventions and advocacy opportunities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".