Culturally Diverse Students’ Perspectives on Sexual Violence Policies: Recommendations for Culturally Sensitive Approaches to Prevention in Higher Education
Bibliographic record
Abstract
Culturally sensitive approaches in sexual violence prevention (SVP) refer to the proactive measures and strategies designed to address unique cultural circumstances impacting SVP. It focuses on fostering a culture of consent, respect, and equity and creating a safe and supportive environment for all individuals regardless of your identity. Increasing cultural diversity on university campuses poses unique challenges in preventing sexual violence (SV). Cultural diversity brings different perspectives, norms, and values regarding sex, sexuality, and gender roles. It can contribute to varying understandings of consent, differing attitudes toward SV, and diverse victimization experiences. These differences can create barriers to effectively addressing and preventing SV. The multiphase Culture and Perspectives on Sexual Assault Policy study, conducted at four universities in Eastern Canada, employed a qualitative research design involving focus groups with culturally diverse student participants. The findings revealed a strong desire for more education on sex, sexuality, SVP, and the intersections of culture. Additionally, the findings emphasize the importance of education and comprehensive prevention efforts that consider cultural differences, challenge gender normativity, debunk rape myths, and address the shame and secrecy associated with experiencing SV. These insights have significant implications for promoting a sense of community ownership, increasing the effectiveness and sustainability of prevention efforts, and helping to create a campus environment where all students feel safe, supported, and valued.
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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".