Autism, Expert Discourses, and Subjectification: A Critical Examination of Applied Behavioural Therapies
Bibliographic record
Abstract
Applied behavioural therapies are widely adopted interventions that have become the standard of healthcare and expert knowledge for autistic people in Canada. These therapies are methods of individualized behavioural modification whereby skills are taught, and socially “undesirable” or “inappropriate” behaviour is regulated according to expert claims that focus on correction, imitation, repetition, reinforcements, and environmental modification. Despite their prevalence, these therapies are highly controversial methods within autism communities, with mostly non-autistic parents and clinicians as their main proponents, and autistic self-activists as their critics. The ethnographic research presented will examine the culture, training, and knowledge practices of behavioural therapy providers in Ontario, to study disciplinary techniques that are used to create expert subjects. In order to operate as a technology for producing optimal results in the autistic subject, working as a behavioural therapist involves multiple techniques such as completing intensive exercises consisting of audible and textual surveillance, recorded sessions, and intra-therapeutic replicability. These techniques and exercises often work to discipline expressions of care in accordance with a “psychocentric” framework for understanding autism and supporting autistic people.
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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 | 0.047 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.022 | 0.089 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".