The Posttraumatic Stress Defence in Canada: Reconnoitring the 'Old Lie'
Bibliographic record
Abstract
Posttraumatic Stress Disorder (PTSD) is a hitherto-obscure phenomenon in the criminal law. Law has, for too long, been marching with medicine, but in the rear, and limping a little. Yet, increasing awareness of the Disorder has prompted counsel to plead it in both civil and criminal proceedings. It is, perhaps, unsurprising that this disease, which was once prevalent as shell shock, is the modern scourge of Canada's military obligations abroad. When Canadian Forces (CF) personnel return home, they are often not debriefed, nor provided with adequate mental health services. This neglect sows a ticking time bomb in Canadian homes; when the time is up, the explosion is sometimes violent, and criminal charges are laid. Yet, PTSD is not germane to only soldiers; sexual assault victims, and disaster survivors often suffer from the Disorder. However, because combat veterans often suffer more severe PTSD, and suffer it more prevalently, they will be the focus in this analysis. This paper will demonstrate that Posttraumatic Stress Disorder can form the basis of a successful Defence of Mental Disorder (Not Criminally Responsible, or NCR) in criminal proceedings. The analysis will first define PTSD as a Mental Disorder; second, it will synthesize the Canadian law on the Defence of Mental Disorder; third, it will demonstrate how PTSD can form a basis for a successful NCR defence; fourth, it will address potential problems with the latter assertion; and finally, it will propose some measures to prevent PTSD in the highest risk group - Canadian Forces soldiers - and more effective means of treating them when they do suffer from PTSD. A 2008 report by CBC News indicates that the number of CF soldiers suffering from PTSD has more than tripled since Canada first deployed troops to Afghanistan in 2001. The deployment is now expected to continue to 2011. Veteran Affairs acknowledges that without [treatment] - many [such] veterans have the potential to harm themselves or others. In June 2006, the Manitoba Court of Queen's Bench, acquitted a CF member on this precise defence, in R. v. Borsch. The Court of Appeal recently ordered a new trial, on factual grounds. These decisions, coupled with the fact that the Defence application for leave to appeal to the Supreme Court of Canada is currently pending, demonstrate that this defence is clearly relevant to the discourse in the modern Canadian criminal law
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".