Deference Deference Shall You Do, For This Is Arbitral Review: Why Canadian Courts Should Exercise Maximal Deference When Reviewing Commercial Arbitral Awards for Legal Errors
Bibliographic record
Abstract
Canadian judges sitting in judicial review of administrative action regularly grit their teeth and declare “reasonable” decisions they might disagree with. Deference has come to define the Canadian approach to administrative law, requiring judges to abstain from substituting their views on the law for those of putatively expert tribunals. In Sattva Capital Corp. v Creston Moly Corp, the Supreme Court of Canada recognized that private commercial arbitration tribunals are due similar deference, albeit for somewhat different reasons. As the Court did in Sattva, one might draw parallels between administrative tribunals and arbitral tribunals, most notably the presumptive expertise attributable to the decision-maker. But important differences also exist. This paper contends that while the Supreme Court’s standard of review framework for appeals of commercial arbitral awards on questions of law set out in Sattva is largely apt, it contains a flaw rooted in overreliance on administrative law principles. Specifically, the Author argues the Court erred in retaining wholesale two categories of questions calling for non-deferential correctness review inspired by the Court’s decision in Dunsmuir v New Brunswick: 1) constitutional questions; and 2) questions of central importance to the legal system and outside the decision-maker’s expertise (“central importance/ outside expertise questions”).
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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 | 0.041 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".