I am that name? naming neurotypical imaginaries of the sole autist in autistic/autism fiction
Bibliographic record
Abstract
This paper explores neurodivergent readings of different fiction and non-fiction novels with explicitly “diagnosed” “autistic characters”, or what McGrath (2017) has referred to as ‘named’ representations of autism, and where “autism” is a central part of the plot. We discuss the impact of neurotypical naming and neurotypical texts; what explicit referencing to “autism” and “autistic characters” do in the case of fiction and non-fiction novels aimed at a predominantly neurotypical audience. We reflect upon what it means (and how it feels) to use an already established name (such as autism), and acknowledge its different routes and meanings associated with it. Our aim is not only to do a critical reading and discussion of neurotypical texts about autism and autistic characters, but also to find a collective reading practice where our experiences as readers are valued as research material, providing insights on how we occupy spaces, experience emotions, and inhabit the world. This is a way to challenge neurotypicality of the taken for granted “us” and “our” gaze in neurotypical texts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".