Bibliographic record
Abstract
Considerable scholarly and judicial attention has been devoted to the selection of the standard of review in Canadian administrative law. Through generational analysis of the developments and challenges in administrative law, and a comparison of the different standards of review, the article examines the place of Canada (Minister of Citizenship and Immigration) v. Vavilov in the jurisprudential landscape. The article suggests that Vavilov now serves as the new Baker v. Canada (Minister of Citizenship and Immigration), providing practical guidance and a stable framework by simplifying the selection of the standard of review process but requires further refinement by attending to transparency and justification regarding the reweighing of factors and the use of Charter values. Ultimately, this article proposes that Baker and Vavilov together could inform the next generational shift in administrative law: the formal recognition of a general duty to provide reasons.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Administrative law analysis of the judicial standard of review post-Vavilov; 'standard of review' is judicial, not peer review.
The article analyzes administrative-law doctrine, not the research system.
Canadian administrative-law analysis of judicial review standards, not research evaluation or policy of science.
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 | 0.358 | 0.531 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.039 | 0.022 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.030 | 0.028 |
| Insufficient payload (model declined to judge) | 0.001 | 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".