Autistic Experiences of Applied Behavior Analysis (ABA): Toward Improved Autistic‐Centered Supports
Bibliographic record
Abstract
ABSTRACT This study (extracted from a larger doctoral‐level dissertation project) adopts a Critical Autism Studies (CAS) framework, and transformative mixed methods design, to examine lived experiences of Applied Behavior analysis (ABA) and to guide improvements in autism services. Autistic people have shared their lived experiences of the ways ABA has caused them harm due to the rigidly applied positive reinforcement and punishment procedures used to make them appear less autistic. Increasingly, ABA practitioners and researchers have responded by attempting to transform their practices to be more accepting of autistic ways of being. The four‐staged methodology (surveys—interviews—analysis—participatory dissemination) responds to Milton's (2014) call for “interactional expertise” between non‐autistic researchers and autistic people. The survey stage yielded 68 completed surveys from autistic people in Canada—22 respondents had received ABA and 46 had not but wished to express their views. Four participants who had received ABA—two with positive experiences and two with negative experiences—participated in semi‐structured interviews and authorship of recommendations. Findings revealed diverse perspectives on ABA practices. Implications for policy and practice highlight the importance of authentically engaging with autistic communities to develop supports that are helpful and not harmful.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| 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".