Reconciling civil liberties and public health in the response to COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the challenges governments face in balancing civil liberties against the exigencies of public health amid the chaos of a public health emergency. Current and emerging pandemic response strategies may engage diverse rights grounded in civil liberties, including mobility rights, freedom of assembly, freedom of religion, and the right to liberty and security of the person. As traditionally conceived, the discourses of civil rights and public health rest on opposite assumptions about the burden of proof. In the discourse of civil and political rights of the sort guaranteed under the Canadian Charter of Rights and Freedoms, the onus rests on government to show that any limitation on rights is justified. By contrast, public health discourse centers on the precautionary principle, which holds that intrusive measures may be taken—lockdowns, for example—even in the absence of complete evidence of the benefits of the intervention or of the nature of the risk. In this article, we argue that the two principles are not so oppositional in practice. In testing for proportionality, courts recognize the need to defer to governments on complex policy matters, especially where the interests of vulnerable populations are at stake. For their part, public health experts have incorporated ideas of proportionality in their evolving understanding of the precautionary principle. Synthesizing these perspectives, we emphasize the importance of policy agility in the COVID-19 response, ensuring that measures taken are continually supported by the best evidence and continually recalibrated to avoid unnecessary interference with civil liberties.
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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 | 0.077 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.128 |
| Scholarly communication | 0.033 | 0.032 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.029 | 0.036 |
| 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".