Texts and Effects: Interview Findings on Neurodiversity and Representation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».