Unfinished Business in Unwritten Justice: Unwritten Constitutional Principles After Toronto (City) v. Ontario (Attorney General)
Bibliographic record
Abstract
This article examines unwritten constitutional principles (UCPs) within the context of the Supreme Court of Canada’s 2021 obiter opinion in Toronto (City) v. Ontario (Attorney General). The Supreme Court has traditionally accepted three main arguments in justifying the use of UCPs. The Toronto (City) v. Ontario (Attorney General) majority strictly prescribed a “textual approach,” whereby a court broadly interprets the written Constitution, negating the importance of UCPs as independent legal tools. I respectfully submit that the majority failed to provide a reasoned framework for UCPs. I argue that certain constitutional issues arise that cannot be addressed through explicit constitutional provisions. Relying exclusively on enumerated provisions to invalidate legislation may stress the democratic authority of the Constitution when its provisions have a weak tie to a desired principle that addresses the constitutional threat at hand. In these cases, it is better if constitutional principles and values are openly acknowledged and subjected to careful consideration, analysis, caution, and criticism through structural argumentation. While the written text of the Constitution must always take priority, Canadian courts must sometimes turn to the full legal power of UCPs when faced with novel constitutional issues unforeseen when the Constitution was drafted.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".