Bibliographic record
Abstract
Just over a decade ago, with the introduction of a unified approach to substantive review, the Supreme Court of Canada significantly modified administrative law. More recently, the law on substantive review has been transformed once again through Dunsmuir v. New Brunswick, with some further elaboration in Canada (Minister of Citizenship and Immigration) v. Khosa. This article examines the latest changes in the jurisprudence. By way of background, Part I provides a brief overview of the unified approach inaugurated by Pushpanathan v. Canada (Minister of Citizenship and Immigration) and Baker v. Canada (Minister of Citizenship and Immigration). Part II describes the new framework for appeals and judicial review set out in Dunsmuir, as well as the amplification provided by Khosa. Part III considers the extent to which the majority of the court in Dunsmuir explicitly saw itself as charting a new path. Part IV suggests that at least two features of the new approach actually reflect a partial, nuanced return to aspects of the pre-Pushpanathan and Baker jurisprudence. Part V raises the question of whether, besides reworking the law on substantive review, Dunsmuir may also signal a closer link between substantive and procedural review.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.051 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".