Struggling Towards Coherence in Canadian Administrative Law? Recent Cases on Standard of Review and Reasonableness
Bibliographic record
Abstract
Although the Supreme Court of Canada’s seminal decision in Dunsmuir v. New Brunswick has now been cited more than 10,000 times by Canadian courts and administrative tribunals, many of its key features remain obscure. In this article, the author analyzes recent cases decided under the Dunsmuir framework with a view to determining where Canadian courts might usefully go next. The author’s argument is that the two important principles said to underlie the Dunsmuir framework—the rule of law and democracy—can provide guidance to courts in simplifying and clarifying judicial review of administrative action. In Part I, the author explains how the relationship between Dunsmuir’s categorical approach and the contextual approach that it replaced is uncertain and causes significant confusion, and explores the potential utility of the two underlying principles in simplifying the law. The application of the reasonableness standard of review is the focus of Part II, in which the author criticizes the general approach to reasonableness review in Canada, but suggests that the rule of law and democracy may assist in clarifying the law, by setting the boundaries of the “range” of reasonable outcomes and structuring the analytical framework for identifying unreasonable administrative decisions. Finally, the author draws the strands of Parts I and II together by arguing for the adoption of a unified, context-sensitive reasonableness standard, underpinned by the rule of law and democracy, with the aim of providing clarity and simplicity to Canadian administrative law in a manner faithful to the Supreme Court of Canada’s decision in Dunsmuir.
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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 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.037 | 0.035 |
| Scholarly communication | 0.026 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".