Autism, Neurodiversity and the Welfare State: The Challenges of Accommodating Neurological Difference
Bibliographic record
Abstract
Abstract. In the last decade, autism has become one of the most hotly contested health policy issues in North America and beyond. From debates about the role of vaccines to the efficacy of therapeutic interventions, a range of civil society actors has been advocating for policy and societal change in the field, with mixed success. In Canada, this culminated in 2004 with a much-publicized Supreme Court decision— Auton v. British Columbia —that pitted parents of autistic children against the BC government, which was unwilling to cover the costs of behavioural treatment for autistic children. In contrast to parent-led advocacy groups, there has been a flurry of civil society activity waged by autistic self-advocates who decry the focus on curing autistic people and press instead for the recognition of neurological difference. Drawing on interviews with advocates in Canada and the US, this article highlights these contending perspectives and argues that both pose fundamental challenges to how we view the redistributive aims of the welfare state in Canada and beyond. Résumé. Au cours de la dernière décennie, l'autisme est devenu l'un des enjeux les plus controversés dans le domaine de la santé au Canada et à l'étranger. Que ce soit lors de débats sur le rôle des vaccins ou encore sur l'efficacité des interventions thérapeutiques, plusieurs acteurs de la société civile ont milité, avec un succès mitigé, en faveur de changements dans politiques et sociaux par rapport à l'autisme. Au Canada, cet activisme résultera en une décision fort controversée de la Cour Suprême en 2004, Auton v. Colombie-Brittanique , portant sur un conflit entre les parents d'enfants autistes et le gouvernement de la Colombie-Britannique, qui refusait de payer le coût des traitements pour les enfants autistes. En parallèle au militantisme des parents d'enfants autistes, des individus autistes se sont aussi mobilisés pour dénoncer cette fois l'objectif même de guérir les personnes autistes. Ces derniers exigent plutôt que soient reconnues leurs différences et, de manière plus large, le principe de la diversité neurologique. Se basant sur des entrevues avec des militants et des militantes, cet article présente ces différentes perspectives et démontre qu'elles remettent en question la façon dont nous conceptualisons le modèle de redistribution associé à l'État-providence.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.065 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".