Emotions and the Veil of Voluntarism: The Loss of Judgment in Canadian Criminal Defences
Bibliographic record
Abstract
In this perspective piece, the author attacks the notion of "moral involuntariness" in the Supreme Court of Canada's judgment in R. v. Ruzic. He asserts that the voluntarist account of criminal liability is purely descriptive. Through the embrace of a mechanistic understanding of human agency, it forestalls judgment and veils the normative foundation of criminal law. The author asserts the need for a more normative approach, one which seeks to evaluate the moral blameworthiness of an act. In the case of duress, the author suggests that it is not enough to simply state that a person's will is constrained because he or she is acting under the influence of emotion. An evaluative account of emotions would suggest that emotions involve thought on the part of the actor, and that emotions can be mistaken. Therefore, the moral bases of emotions can and should be evaluated. The law could have considerable conservative inertia under a legal regime which allowed certain attitudes to go unexamined. For instance, the sources of a particular "emotional" reaction might be rooted in a subordinating, retrograde vision of society that placed a low value on certain classes of persons. Hence, the voluntarist account may allow morally suspect social norms and their regressive effects to persist in the criminal law. Through these and other lines of inquiry, the author leads us to question some of the underpinnings of criminal law thinking, and calls for the reintroduction of meaningful and open judgment into the law of criminal defences.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".