Bibliographic record
Abstract
When it comes to Supreme Court of Canada decisions, I am not really known for my unequivocal, consistent, enthusiastic praise.Indeed, in administrative law, I can be rather uncharitable.Some may recall that in 2016, I wrote an article criticizing the Supreme Court's approach to administrative law.I said that " [Canadian] administrative law is a never-ending construction site where one crew builds structures and then a later crew tears them down to build anew, seemingly without an overall plan." 1 In that article, I targeted the Dunsmuir v. New Brunswick case 2 and the glosses on it in the later years.3 Many followed with their own views.Much praise for Dunsmuir?There most certainly was not!And, in 2019, sure enough, the Supreme Court tore down the Dunsmuir mess.Before us was the Supreme Court's latest structure for the substantive review of administrative decisions, Canada (Minister of Citizenship and Immigration) v. Vavilov. 4 A new structure, gleaming, bright, and shiny!With all the hopes every previous structure had enjoyed.Might it survive, we wondered?Here we are, over five years later.And Vavilov still stands.And as for that oft-visiting wrecking crew, it's nowhere in sight.Today, five years later, Vavilov survives.Indeed, as we shall see, it thrives.It has even extended its reach, spreading into all areas of substantive review and arguably procedural review too! 5Why?
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".