Bibliographic record
Abstract
This article presents a legal history and counter-narrative of the Supreme Court of Canada’s unanimous 1977 decision in Smithers v The Queen. Smithers is a criminal law case that focused largely on the issue of causation and is likely taught in most if not all Canadian law faculties annually. The case arose out of a fight following a midget league hockey game where one of the combatants died. In constructing its brief narrative of the facts, the Court drastically understated the racial dynamics that were in play during the game which prompted Paul Smithers, a Black and white biracial teenager to confront Barrie Cobby, who was white, and his primary racial antagonist. In framing its narrative, the Court caricatured Smithers as a Black aggressor preying on Cobby. Drawing from critical race theory, this article advances a detailed counter-narrative challenging the Court’s official account which ignored Paul Smithers’s experiences and interpretation of events leading to Cobby’s death. The article relies on primary sources such as the original trial transcripts including Smithers’s testimony and those of defence witnesses. It also draws on the parties’ factums and newspaper articles published contemporaneously with the original trial, in addition to those published as the case was being appealed. Such news articles include interviews with key witnesses like Smithers and other individuals, which provide added insights on what transpired. Despite the racialized dynamics located within the decision, it has been overlooked in various Canadian legal histories centered on race. Thus, the article seeks to fill a gap in the scholarly literature on race and Canadian legal history. Through a broader historical account offered in this study, one learns not only about the intentional omissions in the Court’s narrative in a racially polarized case, but that its construction of events and of the accused effectively and implicitly advanced a white supremacist account of what took place. In addition to scrutinizing the Court’s narrative, this study examines how Crown prosecutors minimized the role of racism within the case and its impact on Smithers. Lastly, this article emphasizes how racial bias may have played a role in one of the juror’s decision-making, rendering Smithers’s conviction suspect.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.042 | 0.023 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".