Conquering the Common Law Hydra: A Probably Correct and Reasonable Overview of Current Standards of Appellate and Judicial Review
Bibliographic record
Abstract
Just as Hercules eventually conquered the Hydra by using a firebrand to cauterize the beast’s neck stumps immediately after he severed each of its heads, this work will (much less heroically) endeavor to conquer the common law equivalent of the Hydra by describing and explaining the application of the only two remaining standards of appellate and judicial review in Canada: reasonableness and correctness. Part II discusses the nature of the two standards of review that apply in Canada, by defining certain key terms and by conducting an exegesis of recent Supreme Court of Canada (SCC) case law. Part III explains which standard of review applies to the various grounds of appellate and judicial review, including reviews on questions of fact, questions of law, questions of mixed fact and law, and questions of “discretion.” I will also examine the standards of review currently in use within Nova Scotia for different grounds of review, in an effort to demonstrate their functional conformity to the reasonableness/correctness standards set down by the SCC. Ultimately, even though the SCC has not said as much in a single decision yet, I will conclude that there are only two standards of review in Canada - reasonableness and correctness - and that the law is now quite clear as to which standard applies to each of the various possible grounds of review.
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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 | 0.061 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.032 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".