The Precautionary Principle as a Justification for Limiting Constitutional Rights
Bibliographic record
Abstract
This article examines the use of the precautionary principle in the context of section 1 of the Canadian Charter of Rights and Freedoms (Charter) during the COVID-19 pandemic. The author argues that the definitions of the precautionary principle used in the COVID-19 cases are unacceptably vague and that the propositions labelled in this way do not, for the most part, have a valid role to play when assessing a rights limitation under section 1. Only the “weakest” form of the principle, which states that scientific uncertainty does not preclude state action, should be allowed to play a role in section 1 analysis — although any contribution it makes at this level will likely be negligible. All other forms of PP — more specifically, “strong” forms asserting that scientific uncertainty about the probability or magnitude of a potential harm constitutes a justification for state action, or that there exists a duty to act in the face of such uncertainty — should be granted no free-standing role in the section 1 analysis. Continued uncritical use of the precautionary principle in this context would be a mistake as it risks weakening the justification for rights limitations under the Charter.
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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 | 0.044 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.061 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".