Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, governments worldwide invoked the ‘precautionary principle’ to justify policies designed to protect public health. This principle holds that the state may act proactively to avert harm where there is factual uncertainty about that harm and the efficacy of policies proposed to mitigate it. Many of the policies introduced during the pandemic limited citizens’ constitutional rights. This article accordingly analyzes how the precautionary principle can be integrated into the proportionality doctrine courts use to assess the validity of rights limitations. As our case study, we take the jurisprudence of the Supreme Court of Canada and its globally influential Oakes proportionality test. When articulating the test in the past, the Court has grappled with the need to defer to laws that pursue important public objectives when the evidence underlying those policies is indeterminate. However, it has been criticized for not creating detailed guidelines for when judges should defer, which is said to breed arbitrary, results-oriented decision-making. We update this criticism by showing that it continues to apply to judgments of lower courts in Canada that have followed the Court’s proclamations to evaluate laws that limit constitutional rights to combat COVID-19. We then construct the requisite guidelines by drawing analogies with existing legal principles found in tort and criminal law. We argue that in contexts of factual uncertainty, the degree of judicial deference should vary according to the gravity and likelihood of the harm the government seeks to prevent. This risk-based framework restrains judicial subjectivity and illuminates how precaution should operate at each stage of the proportionality test. We further argue that it can assist courts across jurisdictions when incorporating precaution within proportionality because, unlike approaches to this problem offered by other comparative constitutional scholars, it is suitably modest and avoids excessive revision of accepted proportionality principles.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".