A Quantitative Intersectional Exploration of Sexual Violence and Mental Health among Bi + People: Looking within and across Race and Gender
Bibliographic record
Abstract
Young bisexual people report disparities related to mental health and sexual violence compared to their heterosexual and gay/lesbian peers. However, the majority of research in these areas does not employ an intersectional design, despite evidence that health outcomes vary by race and gender within bi + populations. The goal of this paper is to provide an intersectionally-informed exploration of the prevalence of sexual violence among a diverse sample of 112 bi + people age 18-26, as well as descriptive data on stigma, mental health, and social support. Most (82%) of participants reported at least once experience of sexual violence since the age of 16. Sexual violence was positively associated with sexual stigma, anxiety, depression, and suicidality. Nonbinary participants reported greater prevalence of violence, exposure to stigma, and worse mental health outcomes relative to cisgender participants. Nonbinary BIPOC participants reported higher levels of anxiety and depression than cisgender BIPOC participants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".