The Standard of Review and The Duty to Consult and Accommodate Indigenous Peoples: What is the Impact of Vavilov?
Bibliographic record
Abstract
Following the Supreme Court of Canada’s landmark decision in Vavilov, an especially relevant issue in Canadian jurisprudence is how courts have applied Vavilov’s new standard of review framework. This article seeks to answer how the Vavilov framework affects decision-making regarding the duty to consult and accommodate. While Vavilov establishes a general presumption of reasonableness review for administrative decisions, it also carves out several exceptions to that presumption where the standard of correctness applies. The exception for section 35 Aboriginal and treaty rights under the Constitution Act, 1982 is relevant to the discussion in this paper, including what that exception means for cases involving the duty to consult and accommodate. Most cases involving duty to consult and accommodate questions regarding “trigger’ and “scope” have been reviewed on a correctness standard, while all other issues have been reviewed on a reasonableness standard. The authors argue that the logic in Vavilov suggests that a broader range of issues should be subject to the correctness standard than is currently the practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.010 |
| 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".