Rounding Up the Usual Criminal Suspects, and a Few More Civil Ones: Section 7 After "Chaoulli"
Bibliographic record
Abstract
By the 1990s, any criminal lawyer or judge could readily "round up the usual suspects," a string of solid section 7 authorities in criminal law. The civil cases, by contrast, have been narrow, cautious and inconsistent, until Chaoulli. After a review of various narrowing devices used in civil matters, the author looks at the scope of "life, liberty and security of the person" and suggests some problems with the Supreme Court's "psychological stress" analysis of "security." A quick look at the "top ten" criminal "principles of fundamental justice" is followed by a discussion of the three related crossover principles of overbreadth, vagueness and arbitrariness. To date, any "principles" in civil matters have reflected their criminal origins in physical liberty and the state-vs-citizen structure of proceedings. Chaoulli and Charkoui may mark a new beginning, or just another twist for section 7 in civil matters.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".