Reasonableness Review and the Interdependence of Process and Substance after Vavilov
Bibliographic record
Abstract
This article examines the shared conceptual foundations and practical features of the law of substantive judicial review set out in Canada (Minister of Citizenship and Immigration) v. Vavilov, and the law of procedural review established in Baker v. Canada (Minister of Citizenship and Immigration). The article begins by exploring conflicting notions of legality: one based in legislative authority that prioritizes correctness and tends to separate process from substance, and the other based in a culture of justification that recognizes the interdependency between process and substance. It concludes that the latter –– the “reason oriented” approach –– is the theoretical foundation underpinning both the Vavilov and Baker frameworks. Accordingly, it finds that these two frameworks are highly similar in terms of the contextual factors they prescribe, and that they both rely heavily on the relationship between the procedure used to reach a decision and its substantive outcome. The article concludes by suggesting that reasonableness is already the standard of review both applicable and applied to procedural matters.
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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 | 0.042 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".