Texts and Effects: Interview Findings on Neurodiversity and Representation
Bibliographic record
Abstract
What texts do people use to think about, understand, and build upon the language and concept of ‘neurodiversity’? This article presents findings from an institutional ethnography study in which participants in Ontario, Canada each selected and discussed a text – any written, visual, or recorded source – that had influenced their understanding of neurodiversity. This analysis focuses on findings from interviews with the 44 neurodivergent participants. We found that participants were sometimes very connected to discussions on neurodiversity and sometimes new to or isolated from them. Our findings highlight the impact of influential texts such as the movie Rain Man and the book NeuroTribes , and confirm the structural dominance of the Diagnostic and Statistical Manual of Mental Disorders . While participants expressed varied opinions about specific items (e.g. television shows), all participants described a limited representational landscape. Participants talked about how neurodivergent people have unequal access to status and expertise; texts can be one way to mobilize the ‘expert’ status of others, or to assert the expertise of neurodivergent people. In hearing from neurodivergent people about how texts contribute their understanding of neurodiversity, researchers can learn about the everyday social impact of representation across institutional contexts. Lay abstract What books, shows, websites, movies, and other texts do people think about when they are talking about neurodiversity? We interviewed 44 neurodivergent people in Ontario, Canada, to find out what they thought about neurodiversity. Interviews were done in person or online through Zoom. The people who were interviewed chose a “text” that they thought was related to neurodiversity and explained what they thought and felt about it. Texts could be anything that is recorded: books, movies, shows, music, webcomics, and more. We examined what people selected and what was important to them about their selections. We found that some people were really connected to ideas and language about neurodiversity and had lots of places and people where they could discuss it, while others did not know many others who talked about neurodiversity. We learned that a few texts affected a lot of other beliefs and experiences - we called these “boss texts”. People also shared a lot of different feelings about how different texts (such as shows or books), but a problem everyone shared is that there are few representations of neurodivergent people that everyone could feel okay about. People also talked about how some texts could be taken more seriously than others based on who created them, and it could take a lot of work to find texts that were helpful. They also discussed how important it is to have texts created by neurodivergent people. Some people even decided to create their own texts. In asking participants about texts, researchers are able to hear different perspectives about neurodiversity from people whose knowledge is often ignored.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".