Bibliographic record
Abstract
The norm against overbreadth—a law should not be overbroad in relation to its own purposes—is well established as a principle of fundamental justice under section 7 of the Charter. But the Supreme Court of Canada’s case law contains two competing formulations of this norm. According to the strict version of the norm, a law is overbroad if it applies in even one (actual or hypothetical) case that is not directly necessary to the achievement of its purpose. According to the relaxed version of the norm, a law is overbroad only if it applies in cases beyond those that are reasonably neces-sary to its operation. The strict version of the norm is unworkable because it relies on two un-tenable assumptions: first, that a law is always an instrument for achieving a purpose that can be fully specified apart from the idea of legal order; second, that a law can be drafted and applied so that it never goes beyond that pur-pose. The result is that, on a proper application of the strict version of the norm, all laws are overbroad. The relaxed version of the norm shares the first assumption but not the second. With respect to those laws that are properly characterized as instrumental, it would be bet-ter to abandon the strict version of the norm and adopt the relaxed version
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 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".