The Protection of Human Rights in Pandemics—Reflections on the Past, Present, and Future
Bibliographic record
Abstract
Abstract This special section tells the story of Covid-19 through the lens of national responses, serious concerns about unprecedented human rights limitations and infringements, and the respective role of courts in public health emergencies. It compiles perspectives on disease control developments in Brazil, Italy, Poland, Taiwan, the U.S., and the EU to explore various aspects of judicial review protecting, or failing to protect, human rights. It offers insights from states and regions which have experienced high pandemic rates or may attract attention for not treating human rights as a priority. Amidst the crisis of multilateralism and the World Health Organization (WHO) authority, and the fact that public health is typically a national power, the Articles focus on the state-level analyses to inspire comparative findings and further research. The section also draws on diversity and transdisciplinarity. The contributions are authored by scholars specializing in wide-ranging areas of law, including constitutional, health, private, and human rights law, as well as in political philosophy and public health. This text introduces the special section by offering a broader picture of the human rights’ problématique in times of pandemics.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".