Public Law’s Cerberus: A Three-Headed Approach to <i>Charter</i> Rights-Limiting Administrative Decisions
Bibliographic record
Abstract
Abstract This article offers a theoretical and doctrinal solution to a vexing question in public law: how to determine the justifiability of Charter rights-limiting administrative decisions. The jurisprudence suggests three approaches, or modes of reasoning: minimal impairment analysis, ‘interest balancing’, and ‘values-advancing reasoning’. Like Cerberus, the guard dog of Hades, Canadian public law has become three-headed. While scholars and courts argue about which mode of reasoning is categorically best, the culture of justification compels us to ask instead which provides the most compelling explanation for each rights-limiting decision. Just as cutting off one of Cerberus’s heads would diminish his effectiveness as a guard dog, rejecting either of the modes of reasoning would limit decision makers’ capacity to explain their decisions and undermine a culture of justification. The article makes a theoretical case for retaining all three modes of reasoning and sets out a doctrinal approach to determining when each is applicable.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".