The evolving international law standards governing restrictions of economic, social, and cultural rights during public health emergencies
Bibliographic record
Abstract
While COVID-19 lockdowns extensively disrupted health services, food supply, and education, such disruptions are rarely framed as human rights restrictions or derogations. I argue that this silence is rooted in contrasting limitations regimes that arose from Cold War disputes framing these rights as progressive, programmatic, and non-justiciable. In this paper, I analyze the evolution of standards on restrictions of social rights during a public health emergency. First, I compare legal regimes in the two primary covenants, and what drafting history illuminates about the reasons for these differences. I then compare these to the regional human rights systems. Second, I consider interpretations of restrictions under both covenants, focusing on the Committee on Economic, Social, and Cultural Rights’ general comments and emerging Optional Protocol jurisprudence. Third, I analyze what these interpretations indicate about the international law standards for restrictions of these rights during a public health emergency. I find that these legal criteria increasingly recognize their evolving nature as justiciable rights that impose clear obligations. However essential components like core obligations and public health crisis must be further developed as key dimensions of economic, social, and cultural rights-related proportionality.
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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 | 0.096 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".