Inclusion Must Be Global, Decolonized, Culturally and Linguistically Diverse, and Anti‐Normative
Bibliographic record
Abstract
ABSTRACT In this concluding commentary for our special issue, Neurodiversity‐Affirming Intersectional Approaches that Target Public Policy: Moving the Focus from Changing Individuals to Changing Systems of Power , we seek to ameliorate the pervasive omission of nonspeaking Autistic people and those outside the Global North from research, services, and policy. In our special issue, we tried to nurture the often‐neglected intersectional roots of the neurodiversity movement by amplifying perspectives of multiply marginalized Neurodivergent people. However, nonspeaking people remain underrepresented in our special issue. Therefore, we assembled people with diverse connections to the autism constellation, including nonspeaking and minimally speaking people and people from the Global South, to write this concluding piece. Together, we generated neurodiversity‐affirming policies and organized them according to these themes arising from articles in our special issue: justice, representation, and systems change. To foster justice, we call for full access to individualized, holistic communication supports, cross‐disability alliances, and decolonial approaches. To improve representation, we recommend melding Universal Design, Open Scholarship, and indigenous frameworks to support Neurodivergent representation in all aspects of research and advocacy, particularly leadership. To promote systems change, we call for accessible multimodal resources and valid assessments. Across all themes we stress tech equity, transparency, and community oversight. Accessible and detailed summaries of our policy recommendations that administrators, editors, clinicians, educators, researchers, and advocates can adopt now to make inclusion the default are provided in tables (please share widely). For the most marginalized, inclusion will not come from incremental adjustments but from radical solutions and systemic overhauls.
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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 | 0.046 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.054 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.033 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".