Experiences of Peer Interaction Amongst Autistic LGBTQ+ Youth in Secondary Schools
Bibliographic record
Abstract
This article highlights the importance of supporting the LGBTQ+ autistic community in the secondary school environment. While there is general agreement that schools need to be safe spaces, there is a dearth of literature understanding how schools can be safe spaces for individuals identifying as autistic and LGBTQ+. Using qualitative data from videoconference (Zoom) interviews with six LGBTQ+ autistic youth in Ontario secondary institutions, this study examined how interactions with peers shape identity, mental health, well-being, and social belonging. Informed by intersectionality theory and by critical perspectives on neurodiversity, this study found that LGBTQ+ autistic youth faced discrimination through microaggressions, peer victimization, and stereotyping, which led to feelings of low levels of safety in their school environments. Participants also had positive, affirming experiences relating to peers who would advocate and recognize LGBTQ+ and autistic identities. Participants spoke about supporting other peers who experienced similar challenges. This research has implications for social work practice, as it points to the importance of developing mentorship opportunities, implementing anti-discriminatory training and policies, respecting self-identification, and engaging in self-reflection to foster increased well-being and safety for LGBTQ+ autistic youth in school settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".