Comparing Factors Shaping Sexual Violence Perpetration for Sexual and Gender Minority Youth and Cisgender Heterosexual Youth
Bibliographic record
Abstract
Sexual and gender minority (SGM) youth experience higher rates of sexual violence victimization than their cisgender heterosexual counterparts. Very little is known about how the minority status of SGM youth contextualizes their victimization and perpetration experiences. In one-on-one interviews with 39 SGM youth and 11 cisgender heterosexuals (non-SGM) youth, we compared the contextual factors shaping sexual violence victimization and perpetration between the two groups using a qualitative descriptive approach. Interviews highlighted how SGM youth continue to experience extensive discrimination that negatively impacts all aspects of their lives, while non-SGM youth do not discuss having to navigate stigma and discrimination in their lives. SGM youth pointed to a lack of understanding of sexual violence within the SGM community. Both groups believed that SGM perpetration was unlikely: while most SGM and non-SGM youth agreed that sexual violence between youth was a problem, same-gender perpetration was seldom discussed. Unlike their non-SGM counterparts, SGM youth felt that they were targeted because of their sexual and gender identity. SGM youth also felt that they were more vulnerable to sexual violence because of how they physically looked, particularly if their gender expression did not match cis-normative expectations. SGM youth reported facing unique pressures when seeking support as a victim, particularly a fear of being outed or stigmatized as part of the process. They also conveyed that SGM people worried about being treated unfairly if they reported sexual violence to authorities. Findings suggest that stigma and concerns of discrimination are unique aspects of sexual violence for SGM compared to non-SGM youth. All youth need to have access to sexual violence prevention education that includes SGM and non-SGM youth as both victims and perpetrators to begin addressing these noted disparities in experiences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".