Silencing the Rainbow: Prevalence of LGBTQ+ Students Who Do Not Report Sexual Violence
Bibliographic record
Abstract
Previous research on sexual violence suggests that there is a higher likelihood of students from LGBTQ+ community experiencing sexual violence and not reporting it. This study investigates various types of sexual violence experienced by the LGBTQ+ students and the prevalence of reporting it. The study further determines why different types of sexual violence are not being reported. This study uses a LGBTQ+ scholarship survey data conducted in 2019. Using Pearson's chi square analysis, the study investigates the relationship between who experienced various kinds of sexual violence and those who do not report it. The study provides descriptive analysis of reasons for not reporting sexual violence across different types of sexual violence. The results show that there is a statistically significant relationship between those who experienced various kinds of sexual violence and those who do not report it. In addition, the study illustrates mistrust in support services and fear of embarrassment as the major reasons resulting in non-reporting behaviors. In conclusion, the study illustrates high prevalence for various types of sexual violence against LGBTQ+ students as well as high underreporting. Study results have implications for health professionals and institutions to focus efforts in making school environments safe and inclusive for LGBTQ+ students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".