Young Bisexual People’s Experiences of Sexual Violence: A Mixed-Methods Study
Bibliographic record
Abstract
Bisexual people are at an increased vulnerability for sexual victimization in comparison to heterosexual people, as well as gay and lesbian people. As the majority of first sexual violence experiences happen prior to age 25 for bisexual women, young bisexual people are particularly vulnerable. Despite consistent evidence of this health disparity, little is known about what factors might increase young bisexual people's risk for sexual victimization, or how they access support post-victimization. The current study addresses this gap through a mixed-method investigation of young bisexual people's experiences of sexual violence with a sample of 245 bisexual people age 18-25. Quantitative results indicate that bisexual stigma significantly predicts a greater likelihood of reporting an experience of sexual violence. Qualitative findings support that while not all participants felt bisexual stigma related to their experience of sexual violence, some felt negative bisexual stereotypes were substantial factors. Interview participants found connecting with other survivors, particularly LGBTQ+ and bisexual survivors, to be beneficial. Some participants encountered barriers to accessing support, such as discrimination in schools. Sexual violence researchers should consider bisexual stigma as an important factor, and support services the potential positive impact of bisexual-specific survivor support.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".