Picked the Wrong Cars: A Comment on R. v. Theriault, R. v. Khill, and the Initial Aggressor
Bibliographic record
Abstract
The decision in R. v. Theriault, 2020 ONSC 5725 attracted considerable attention, both for being broadcast over the internet and for Justice Di Luca's controversial ruling, which many viewed as yet another instance of the criminal justice system failing racialized persons. Although I view Di Luca J. as a thoughtful jurist and empathize with those who have sought to shield him from public criticism, this article argues that Justice Di Luca erred in his treatment of self-defence. The fundamental error in Theriault was in the trial judge's failure to recognize that the accused--Michael and Christian Theriault--were the initial aggressors in their confrontation with the victim, Dafonte Miller. In fairness to Di Luca J., the status of the initial aggressor in Canadian self-defence law has long been uncertain, attributable to our unbelievably confusing prior self-defence legislation and the Supreme Court of Canada's decision to collapse the distinction between initial aggressors and all other accused in R. v. McIntosh, [1995] 1 SCR 686. This paper contends that Canada's newly flexible self-defence regime, introduced in 2013, signals Parliament's intent to reassert the distinction between the initial aggressor and other accused. So long as we continue to view self-defence as a justification, implying the moral correctness of the accused's conduct, the defence should be more difficult to access for those who initiate confrontations than for those who do not. In its recent decision in R. v. Khill, 2020 ONCA 151 the Court of Appeal for Ontario recognized that one way to account for the initial aggressor is to emphasize the accused's role in the incident under s. 34(2)(c) of the Criminal Code of Canada. The Court of Appeal's approach in Khill is a sensible and should be adopted by the Supreme Court when it hears the further appeal in that case. Justice Di Luca erred by not applying Khill, although it was binding on him. In all cases of initial aggression, it should be incumbent on the trial judge to inform the jury (or self-instruct) that the common law has historically required an aggressor to retreat prior to using force, and the trier of fact should consider whether it was possible for the accused to have done so. To do otherwise risks unjust outcomes.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.102 | 0.075 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".