Attaquer une décision en révision judiciaire (Challenging Administrative Decisions on Judicial Review)
Bibliographic record
Abstract
French Abstract: J’essayerai lors de cet expose du droit canadien en matiere de revision judiciaire de fournir quelques conseils pratiques aux plaideurs qui contestent des decisions administratives.Admettons des le depart que le sort des plaideurs n’est pas enviable. La decision administrative est presumee valide et l’individu mecontent qui intente un recours devant les tribunaux de justice se trouve alors avec un fardeau imposant sur ses epaules. Notons en plus que l’auteur du present texte est un partisan feroce de la deference envers les decideurs administratifs. Neanmoins, il y a une tendance, particulierement marquee dans la fonction publique, a pousser a la limite l’approche deferentielle mandatee par la CSC. Je discute notamment des deux volets de raisonnabilite: justification, transparence et intelligibilite (avec ces deux exigences: les decisions doivent etre comprehensibles et revisables) et les issues possibles acceptables (une zone de raisonnabilite construite du contexte juridique et factuel).Le message du present texte est que la deference n’est pas absolue. Les plaideurs ont plusieurs moyens de contester la validite d’une decision administrative qui ne plait pas a leur client.English Abstract: In this short, French-language overview of the Canadian law of judicial review of administrative action, I formulate practical guidance for those challenging administrative decisions.Such challenges face a daunting task. Administrative decisions are presumed valid, which places a significant burden on the shoulders of the litigant. Indeed, the present author is a strong advocate of deference to administrative bodies. Nonetheless, deference cannot and should not be pushed too far, though government lawyers often have a strong incentive to do so.In this paper I place heavy emphasis on the two core concepts of reasonableness review: justification, transparency and intelligibility in the decision-making process (which requires that decisions be understandable and reviewable); and the range of reasonable outcomes (which is set by the legal and factual context).My message is that deference in Canadian law is far from absolute. Litigants and litigators have several avenues of attack against administrative decisions.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".