Bibliographic record
Abstract
In Dunsmuir v. New Brunswick, the Supreme Court re-examined its approach to judicial review of administrative decisions to develop a "more coherent and-workable" framework. It merged the deferential standards of reasonableness simpliciter and patent unreasonableness into a single reasonableness standard and emphasized the importance of precedent in determining the standard applicable to a specific category of decision makers. The author makes a preliminary assessment of Dunsmuir's impact on judicial review through an analysis of recent Canadian appellate decisions. He concludes that, white Dunsmuir simplifies the standard of review analysis by encouraging courts' reliance on satisfactory precedents and guidelines to determine the appropriate standard, there is a risk that courts may uncritically adhere to inappropriate precedents or carry out unduly intrusive review by inappropriately characterizing as jurisdictional the questions before them. Substantive review retains its complexity, which now resides at the stage of courts' application of the merged reasonableness standard.
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 | 0.047 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".