The Canadian Override: Constitutional Model or<i>Bête Noire</i>of Constitutional Politics?
Bibliographic record
Abstract
Israeli proponents of the enactment of a legislative override often invoke Canada as a model that Israel should follow. Their proposals would allow the Knesset to ‘override’ a decision of the Supreme Court of Israel that strikes down a law on the ground that it violates a Basic Law. Proponents of an Israeli override seek recourse to various types of argument to support their position. This article focuses on one such argument: the use of Canada as a model to support the Israeli argument for enacting an override. It argues that in order to evaluate both the value of adopting the Canadian override and the likelihood of its transplantation to Israel being successful, one needs to acquire a deep understanding of its operation in Canada. The article contains four sections in addition to the introduction. Section 2 briefly explains what ‘the Canadian override’ is and how it came to be. Section 3 analyses the positive attraction of the Canadian override as a constitutional model, and identifies three different models of the Canadian override. Section 4 focuses on the Canadian experience with its override. It explains why Canadians have come to view it in negative terms – the ‘bête noireof Canadian constitutional politics’– because of the manner in which it was adopted and the circumstances in which it was first used. Section 5 concludes with some thoughts on legal transplants, legitimacy and lessons for Israel from the Canadian experience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".