In Pursuit of Truth and Care: Discourses of Autism Spectrum Disorder Diagnosis Among Psychologists in Ontario
Bibliographic record
Abstract
In the last half-century, members of the medical community have sought to establish a more valid method of diagnosing autism spectrum disorder (ASD). I argue that these methods, which have come to be predominantly rooted in a reductionist biomedical framework, obscure how ASD and its related experiences are necessarily mediated by social circumstance. Applying a social constructionist lens to the issue of ASD diagnosis, this thesis elucidates the discursive construction of ASD and its diagnosis in eight semi-structured interviews with Ontario-based psychologists who diagnose ASD. I demonstrate that psychologists’ talk about ASD diagnosis shifts around the notion of clinical impairment. Diagnosis is, on the one hand, a hypothetico-deductive process that is concerned with the accurate determination of an innate and discrete ASD and, on the other, an act of care concerned in which ASD is a diffuse and pragmatic label. I argue that while the variability in psychologists’ talk legitimizes diagnosis at the limits of biomedical discourse, ASD is ultimately constructed as a deficit.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.033 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".