Bibliographic record
Abstract
The theory-of-mind-deficit explanation of autism proposes that autistics lack a theory of mind, that autism comprises a theory-of-mind deficit (strong version); or, that autistics often have difficulty with theory-of-mind abilities (weak version). A growing body of critical research demonstrates how these explanations of autistic behavior fail—both empirically and theoretically. The strong version lacks explanatory adequacy, while the weak version is undermined by methodological and empirical flaws in theory-of-mind research. Together, these issues suggest that the “science” of theory of mind in the context of autism is, at best, bad science. Nonetheless, researchers continue to pursue this line of inquiry in autism studies—often moving the goalposts or offering ad hoc rationalizations to preserve the theoretical framework. This article critically examines the theory-of-mind-deficit explanation of autism, focusing particularly on the widely-held view that autistics exhibit difficulties with theory of mind—i.e., the weak version of the theory-of-mind-deficit explanation. Drawing from the philosophy of science, I argue that ongoing adherence to this view exhibits all the hallmarks of a degenerating research programme. Hence, the fact that scientists have not abandoned this hypothesis entails that the research programme is pseudoscientific.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.041 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".