Deference as informed respect: <i>Vavilov’s</i> implications for procedural review of legislative functions
Bibliographic record
Abstract
This article articulates a theoretical and doctrinal basis to support the idea that Canadian courts should abandon their complete abstinence from reviewing the procedural fairness of delegated legislation (known as the ‘legislative exception’). While this is a longstanding demand, Canadian courts have been slow in embracing it until now. Vavilov, a Supreme Court of Canada case decided in 2019, opens the door for the substantive review of delegated legislation, and, hopefully, this will become a consolidated feature in the Canadian system soon. For a Vavilovian robust reasonableness review to be meaningful, however, procedural guarantees need to be in place. To support this claim, we argue that Vavilov builds on David Dyzenhaus’s influential concept of ‘deference as respect’ but rearticulates it as ‘deference as informed respect.’ This means that a deferential stance requires courts to have enough information in order to determine if a decision is justified. Procedural guarantees will provide information and assist courts in conducting a meaningful substantive review. Eliminating the legislative exception in this way will allow courts to address a significant gap in, and increase the coherence of, Canadian administrative law jurisprudence.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| 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".