Bibliographic record
Abstract
The story of Canadian administrative law could be seen as a move toward deference driven by some fundamental decisions of the Supreme Court of Canada. Debates about this move centre on the proper role for reviewing courts as well as the politics lying behind administrative law decisions. Most recently, the 2019 Supreme Court decision in Vavilov raised concerns that it licenses judges to undertake more intrusive review. Key to this story is the assumption that these groundbreaking decisions of the Supreme Court influence how lower court judges decide challenges in the administrative law context. Prior empirical studies have found that the 2008 Supreme Court decision in Dunsmuir increased the use of the reasonableness standard of review as well as the rate at which judges affirm administrative decisions. However, it can be difficult to empirically account for the variety of contexts and decision makers involved. This article uses decisions of the Federal Court to examine whether Dunsmuir and Vavilov changed how judges decide. It finds that, while the use of reasonableness has dramatically increased, the rate at which judges affirm administrative decisions has not changed over time. The article discusses these results and what they imply about the influence of these groundbreaking Supreme Court decisions.
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 | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".