Errors of Fact and Law: Race, Space, and Hockey in <i>Christie v York</i>
Bibliographic record
Abstract
Christie v York has pride of place among decisions wrongly decided in Canadian legal history. Fred Christie and two friends were on their way to a hockey game when they entered the York Tavern at the Montreal Forum, seeking a beer before the game, and the York denied them service on the grounds of race. Or so the facts tell us. As it turns out, a significant error has long been woven into the story of Christie v York. What were Christie and his friends doing that night at the Forum? This article reveals that they were not attending a hockey game. The real facts, long hidden from view, involve a hot summer night, the Canadian Olympic boxing trials, Joe Louis, American race riots, and a local black boxer. And yet, even in error, hockey matters in the case, especially in Justice Henry Davis’s famous dissent. Recasting Christie as a case that turns on the judicial construction of facts, this article highlights the importance of real space and circumstance in creating notions of identity, belonging, and equality. While Christie’s errors of law have been the principal source of interest among legal scholars to date, this article argues that Christie’s facts, both real and imagined, provide a far richer contribution to the legal history of the complex relationship among race and space and 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.062 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".