Simplification or Semantics? Evaluating Vavilov's Impact on Standard of Review
Bibliographic record
Abstract
The Supreme Court of Canada’s pivotal decision in Canada (Minister of Citizenship and Immigration) v. Vavilov introduced a categorical approach to standard of review analysis, aiming to simplify the existing framework. This article traces the evolution of standard of review analysis and outlines previous empirical studies that examine Vavilov’s effect on this analysis. The article describes a new empirical study that employs a current large language model to measure various variables pertaining to Federal Court and Federal Court of Appeal decisions, such as length of standard of review analysis and party agreement on standard of review. The findings confirm that Vavilov has simplified the standard of review analysis, but perhaps that this simplification may have resulted from an evolving approach that began in the years preceding Vavilov.
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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 | 0.310 | 0.714 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".