The End of Asperger’s: An Analysis of the Decision to Remove Asperger’s Disorder from the DSM-5
Bibliographic record
Abstract
Since first formally classified in 1994, Asperger’s disorder (AD) has been a highly controversial diagnosis. Some researchers have argued that it is indistinguishable from high-functioning autism; some have maintained that AD warrants its own diagnosis; and a final group of scholars have claimed that AD should not be considered a disorder but rather should be thought of as a normal human difference. The upshot of this controversy was the decision to eliminate the AD diagnosis from the fifth revision of the Diagnostic and Statistical Manual (DSM-5). This paper explores the question of whether or not this removal of the AD diagnosis is warranted first by reviewing the research that has addressed the legitimacy of the AD diagnosis. The second part of the paper explores the idea that creating the AD label may have been—metaphorically speaking—equivalent to opening Pandora’s box, which implies that an enduring imprint has been left behind. Some scholars have provided evidence of such a long-term impact by arguing that the creation of the AD label has given rise to a new way to be a person. This paper ultimately contends that the position taken by the DSM-5 in eliminating the AD diagnosis is sound because AD should never have been considered a disorder in the first place. However, due consideration needs to be given to the enduring impact of the AD label. Without the AD label, individuals will need another way to define themselves. This paper advocates for the invention of a new term (e.g., Aspergian) that can be used to denote non-pathological instances of AD.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.009 |
| 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".