Bibliographic record
Abstract
In an upcoming set of cases, the Supreme Court of Canada will review its approach to the standard of review of administrative action. In this paper, the author suggests that the Court must go back to the foundation of judicial review in redesigning the standard of review, namely, the task of courts to police the legal boundaries of the administrative body. To do so, courts must authentically interpret the legislative grant of authority to the administrative decision-maker, particularly to determine the appropriate intensity of review. To that end, the author suggests that the Court should discard two myths that have pervaded modern administrative law: (1) that administrative decisionmakers should be granted deference based on purported expertise in matters of statutory interpretation; and (2) that jurisdictional questions exist separately from questions of law. The myths may impose a different standard of review than the one discernible with the ordinary tools of statutory interpretation. The author argues that these court-created devices should not exist at the expense of the constitutionally prescribed duty of the courts to exercise their policing function and engage in genuine statutory interpretation to determine the appropriate standard of judicial review in a given case.
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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.119 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".