‘Tumblr didn’t really give me sex ed, it more gave me like, Queer ed’: how 2SLGBTQ+ people with developmental disabilities use social media for information
Bibliographic record
Abstract
Two-Spirit, lesbian, gay, bisexual, transgender, queer, and diverse gender and sexual identifying (2SLGBTQ+) individuals with developmental disabilities often face unique challenges in accessing comprehensive sexuality education and finding supportive communities. Traditional educational settings and other offline environments may not adequately address their specific needs, limiting knowledge and understanding of sexuality and gender, affirmation of sexual and gender diversity, and identity development opportunities. We interviewed twelve 2SLGBTQ+ adults with developmental disabilities in Alberta, Canada, to explore their use of social media for learning about sex, gender identity, and sexuality, navigating identities, and finding supportive communities. Findings revealed that participants used social media platforms to compensate for a lack of comprehensive sexuality education, gaining insights into sexuality and gender identity through 2SLGBTQ±focused content. Online spaces facilitated learning about gender pronouns, gender identity, sexual orientations, and identity development. Social media platforms are essential for finding and building supportive communities, and providing a sense of belonging and affirmation often missing in offline environments. Despite accessibility and digital literacy challenges, these platforms offer substantial benefits to 2SLGBTQ+ individuals with developmental disabilities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".