Bibliographic record
Abstract
In Canada (Minister of Citizenship and Immigration) v Vavilov, the Supreme Court of Canada endeavoured to reformulate the law of substantive review of administrative decisions. This was familiar territory for the Court. Over the last four decades, the Court has revised the doctrinal framework for substantive review on numerous occasions, with limited success in promoting stability in the law. Early academic responses to Vavilov considered whether the new doctrinal framework would endure. This paper focuses on a prior question: What is the problem with substantive review? It argues that contrary to the Court’s longstanding posi-tion, the foundations of the law of substantive review are neither clear nor stable. Rather, substantive review doctrine is built upon two heavily contested principles capable of being conceptualized in different ways. The jurispru-dence features multiple competing conceptions of those principles, producing tensions which create instability in the law. This suggests that to solve the problem, a coherent theory of sub-stantive review that either resolves or prevents these tensions is necessary.
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 | 0.134 | 0.322 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".