Nothing About Us Without Us: Identifying Principles of Justice For Emancipatory Participatory Research in the Context of Neurodiversity
Bibliographic record
Abstract
The neurodiversity movement has long advocated for "Nothing about us without us" or the necessity of including neurominoritized people, such as Autistics, in the production of public policies, social discourses, academic knowledge, and scientific research about neurominoritized profiles, including autism. Similarly, the scientific and academic communities are increasingly recognizing the importance for participatory research to be not only ethical but also emancipatory. Yet the call for "Nothing about us without us" is still too often ignored, inaccurately understood, or imperfectly applied, in ways that can be jarring and disrespectful at best, and violent and traumatic at worst. Drawing on my experience as an Autistic woman, academic, and self-advocate who has participated in studies on autism, I develop a proposal for how the principle of "Nothing about us without us," understood as reclaiming epistemic authority and agency, might best be implemented in emancipatory research with Autistic adults. Specifically, I turn to two frameworks that have so far been developed independently of each other, yet that prove to be particularly fruitful when used together in this context: namely, the frameworks of design justice and of epistemic injustice. Drawing on both frameworks, I identify four principles of justice so that participatory autism research can be conducted in both an ethical and an emancipatory manner that heeds the neurodiversity movement's call for "Nothing about us without us" - namely, the principles of thorough involvement, of nonnormative communication, of trust-building, and of accountability.
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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 | 0.215 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.034 | 0.214 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".