Enforcement of Foreign Judgments: Where Morguard Stands after Beals
Bibliographic record
Abstract
Many of the questions raised but left unresolved by the Supreme Court of Canada’s innovative decision in Morguard Investments Ltd. v. De Savoye have now been considered by provincial appellate courts, and some consistent answers have emerged. In Beals v. Saldanha, the Supreme Court of Canada does much to confirm our understanding of the law in this area. This article analyzes how some of the main questions posed by Morguard have been answered, considers possible alternative tests for judgment enforcement, and examines the development of the law on defences to enforcement. It also addresses the issue of enforcing foreign punitive and multiple damages awards. Finally, it considers the interplay between these common law developments and various statutes concerning the enforcement of foreign judgments. The Morguard test, as fleshed out in cases like Muscutt v. Courcelles, is sufficiently flexible to accommodate concerns about enforcing international judgments, particularly when the vast majority of those judgments come to Canada from the United States and England. While the current defences to enforcement will likely expand somewhat as a result of handling more incoming judgments, overall they should retain their relatively narrow focus. In a similar vein, special scrutiny of foreign punitive or multiple damages awards should be the exception.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| 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".