Asperger’s Disorder, Criminal Responsibility and Criminal Culpability
Bibliographic record
Abstract
Asperger’s syndrome was only formally accepted into the ICD and DSM classifications of psychiatric disorders in the 1990s. It has been written about extensively in the scholarly literature for two decades, but diagnostic tools are continuing to evolve, as well as understanding of its genetic component and its brain development features. In the criminal law context it poses difficult issues at trial and at sentencing. Contextualising Asperger’s disorder within current knowledge about autism spectrum disorders, this article identifies relevant court decisions internationally, and particularly scrutinises selected decisions in the United Kingdom (Sultan v. The Queen [2008] EWCA Crim 6), Victoria, Australia (Parish v. DPP [2007] VSC 494), and Nova Scotia, Canada (R v. Kagan (2007) 261 NSR (2d) 285; (2008) 261 NSR (2d) 168). It argues that Asperger’s disorder needs to be distinguished by the courts from other disorders, such as personality disorders and intellectual disability, and should be recognised as having the potential to affect in important, albeit subtle, ways defendants’ thinking and understanding, as well as their emotional responses to situations that are to them traumatic. This makes Asperger’s disorder relevant to a number of threshold issues in relation to criminal responsibility as well as to criminal culpability.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 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".