Speak out: Lessons on how to support Canadian 2SLGBTQI youths who face gender-based violence
Bibliographic record
Abstract
2SLGBTQI youth are at disproportionately high risk of experiencing gender-based violence compared to their cis-heterosexual peers, although there is a gap in research explaining why as well as what this violence looks like. Part of the explanation relates to ongoing homophobia and transphobia; however, more research is needed to understand 2SLGBTQI youths’ feelings of safety within their communities, their experiences of violence with partners, and their help-seeking behaviours. Given the limited Canadian research, the Speak Out project was undertaken. The Speak Out project is a multi-phase project with Phase 1 encompassing a survey of youth across Canada about their experiences of gender-based violence. From across Canada, 292 youths were recruited and asked to complete a survey on gender-based violence. The survey covered four domains related to violence (physical violence, emotional abuse/control, sexual violence, virtual violence) participants experienced, experiences with help-seeking, and connections to the 2SLGBTQI community. Most youths had connections to the broader 2SLGBTQI community and were open about their identities, but many reported being discriminated against based on their gender expression (50%) and sexual orientation (43%). Of the participants who answered questions related to physical, emotional, and sexual abuse, very few reported any incidents. More youths reported virtual violence via text messages (34%), making them afraid to ignore phone calls or other contact (18%), and surveillance of their social media (18%). These results contribute valuable knowledge on the experiences of Canadian 2SLGBTQI youths and have important implications for both education and frontline service provision.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".