“These Experiences also Relate to my Neurodiverse Identity”: An Intersectional Understanding of Heterosexist Events Among Autistic-LGBTQ+ Individuals
Bibliographic record
Abstract
Background: Given the high likelihood of Autistic individuals also being sexual and gender minorities, it is important to understand how Autistic-LGBTQ+ people understand prejudice events in the context of their intersectional identities. Methods: In this study, we used reflexive thematic analysis guided by an intersectional lens to explore experiences of heterosexism among 49 Autistic-LGBTQ+ adults. Specifically, we explored two issues as follows: (1) the nature of the heterosexist events they experienced and (2) the identities Autistic LGBTQ participants thought were related to a distressing heterosexist event they had experienced. Results: Heterosexist events were characterized by four themes, including rejection, harassment, invalidation, and discrimination. Participants often felt that several identities were salient when considering their heterosexist event. The four themes that described the experience of intersectional stressors for Autistic-LGBTQ+ participants are abbreviated here as follows: (1) assumptions about autism, (2) gender “as a bit of a double blow,” (3) queerness as “abhorrent,” and (4) “too young to know better.” Conclusion: We use intersectionality theory to frame stigma experiences for Autistic people who are also sexual and gender minorities, exploring clinical implications and future directions for research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".