Bibliographic record
Abstract
In the summer of 2019, I returned to Canada after three years in the United Kingdom, at the Faculty of Law, University of Cambridge.Te timing was fortuitous.Just a few months after I settled into my new ofce at the University of Ottawa, the Supreme Court of Canada handed down the Vavilov decision.In the whirlwind weeks of December 2019 and January 2020, I spoke about the decision to students, journalists, lawyers, judges, and even friends and neighbours who would not ordinarily take any interest at all in administrative law.I had been interested in the subject for much longer, of course, and indeed had been a consistent critic of the Supreme Court's approach over the preceding decade.In deciding Vavilov, the court listened to my complaints -and those of many others -and attempted to fashion a framework that responded to academic, judicial, and practitioner critiques.Ten, in March 2020, the COVID-19 pandemic hit Canada and life was turned upside down.With three young children at home (two, fve, and six years old when we were frst locked down) for long stretches over the next two years, many research projects had to be placed on hold.As things slowly -oh, so slowly -returned to normal, Canadian courts continued to apply Vavilov, with the volume of decisions growing steadily.I watched as the body of case law built up -often from the basement sofa with the sounds of Disney+ or Netfix ringing in my ears -and, via Zoom and Teams, often spoke with lawyers, judges, and academic colleagues about developments in Canadian administrative law.Around that time, it occurred to me that I might have a book on Vavilov in me.On my blog, Administrative Law Matters, and in multiple academic articles and presentations, I had criticized the Supreme Court's vii
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.436 | 0.267 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".