Bibliographic record
Abstract
In Dunsmuir v. New Brunswick, the Supreme Court of Canada attempted to clarify and simplify Canadian judicial review doctrine. I argue that the Court got it badly wrong, as evidenced by four of its recent decisions. The cases demonstrate that the new categorical approach is unworkable. A reviewing court cannot apply the categorical approach without reference to something like the much-maligned “pragmatic and functional” analysis factors. The categories regularly come into conflict, in that decisions could perfectly reasonably be assigned to more than one category. When conflict occurs, it must be resolved by reference to some factors external to the categorical approach. The new, single standard of reasonableness is similarly unworkable without reference to external factors. Different types of decision attract different degrees of deference, on the basis of factors that are external to the elegant elucidation of reasonableness offered in Dunsmuir. Clarification and simplicity have thus not been achieved.
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 | 0.128 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.017 | 0.027 |
| 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".