Imagining Neurodivergent Futures from the Belly of the Identity Machine: Neurodiversity, Biosociality, and Strategic Essentialism
Bibliographic record
Abstract
Several critiques have emerged of the neurodiversity paradigm and of claims made by activists in the Neurodiversity Movement. These critiques include concerns that the Neurodiveristiy movement downplays the differences between Autistic people. In this article, I argue that the neurodiversity paradigm is a strategically adopted response to current realities. Sometimes, it is strategically necessary to appeal to existing narratives about Autism, or to emphasize solidarity within the Autistic community over the autism spectrum's internal diversity. At times, this can lead activists to neglect a more nuanced articulation of the Neurodiversity paradigm, which allows for the diversity of our community while still calling for solidarity in the face of shared experiences of discrimination. I compare this strategy with strategies of strategic essentialism utilized in the Indigenous Rights movement in Canada. I also explore the ways in which discourses of ableism and racism have historically been intertwined. Both Autistic people and Indigenous people represent diverse communities that must grapple with externally imposed identities to access legal rights, and both identities have been denigrated as mentally inferior by non-Autistic and colonial powers. I conclude that it is sometimes necessary to employ these types of strategies to secure needed resources and protections. I call for both scholars and advocates to take a more intersectional approach to understanding how strategic essentialism is being deployed within the Neurodiversity Movement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.126 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| 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".