How Has Dunsmuir Worked? A Legal- Empirical Analysis of Substantive Review of Administrative Decisions after Dunsmuir v. New Brunswick: Findings from the Federal Courts
Bibliographic record
Abstract
This is the first in a series of three articles undertaking an in-depth study of Canadian judicial review and appeal decisions decided in English post-Dunsmuir. This article sets out the foundations of our research based on a set of 104 questions, focusing on both choice and application of standard of review and, applying that analytical structure, examines federal court decisions made between 2008 and 2015. We ask how those decisions reflect the key changes introduced by Dunsmuir, including: (a) the move to one deferential standard; (b) an analytical focus on applying rather than selecting the standard of review; (c) a description of reasonable decisions as ones where the reasoning is transparent, justifiable and intelligible, and the result falls within a reasonable range of outcomes, taking account of the law and the facts; and (d) a renewed emphasis on the centrality of deference.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".