Aut-ors of our Experience: Interrogating Intersections of Autistic Identity
Bibliographic record
Abstract
Narratives of the Autistic experience are often told, interpreted, and assigned value by people who are not Autistic, allowing dominant cultural understandings of Autism to pervade without substantial inquiry. In academia, a space in which there is little room for Autistic people in the first place, the power of these dominant ideologies is used to minimize our voices, dismiss our concerns, and devalue our insights. Drawing from Spry’s definition of auto-ethnography and using the works of Derrida and Ronell as aesthetic inspiration, we share and interpret our lived experiences to reclaim our Autistic academic identity. We deliberately disrupt conventions of scholarly writing and storytelling to demonstrate that Autistic narratives should and do interrupt, challenge, or even completely undermine academic normativity. We deploy this cripping-up of our experiences to interrogate how Autistic identity is constructed and negotiated in academia. In doing so, we explore avenues to integrate and celebrate Autism in academic spaces so that scholarship in disability studies, critical autism studies, and gender studies can be enhanced.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.038 | 0.070 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".