‘Coming out’ on the spectrum: autism, identity and disclosure
Bibliographic record
Abstract
Much has been written by queer theorists about the personal and political ramifications of being out of the closet, and connections with experiences of disclosure for those with ‘hidden’ health conditions have been made by researchers studying critical geographies of disabilities and chronic illness. To date, however, the impact of such issues for those on the autism spectrum (AS) has received comparatively little attention. Popular re-presentations of AS suggest disclosure is irrelevant for those assumed so obviously different and unlikely to pass as ‘normal.’ However, AS authors reveal a broad spectrum of experience indicating that concealment and disclosure are complex and selective strategies of information and identity management. Applying discourse analysis to AS autobiographies and personal narratives, this paper explores four sense-making discourse clusters, or repertoires, that emerge from the texts under study: a ‘keeping safe’ repertoire, which addresses protective strategies in disclosure and coming out; a ‘qualified deception’ repertoire, which relates to the complexities of non-disclosure; a ‘like/as resistance’ repertoire, which captures the tendency of AS authors to position their individual and collective experiences of coming out on the spectrum as analogous to the process of coming out for other marginalized groups, most notably gay and Deaf communities; and an ‘education’ repertoire, which contributes to the project of building a community to come out to. Each of these repertoires is situated within the broader literature in social and cultural geography and critical disability studies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".