The fragile relationship between the amended International Health Regulations and human rights law
Bibliographic record
Abstract
While the 2005 International Health Regulations (IHR) introduced unprecedented references to human rights, these inclusions were inadequate to address the challenges posed by pandemic measures. This weakness was illuminated during COVID-19 as the IHR's general ambiguity facilitated measures often abusive of human rights. The 2024 IHR reforms have done little to augment these provisions, despite novel inclusion on equity, solidarity, access to vaccines, financing, and pandemic preparedness which remain vague and delinked from formal human rights protections. What is needed instead is an interdependent focus on the human rights impacted by the IHR: economic, social, cultural, civil and political. This paper focuses on these weaknesses and gaps in its central focus on the relationship between the amended IHR and human rights. First, it outlines human rights impacted by the IHR during COVID-19. Second, it considers the relationship between the IHR and international human rights law. Third, it explores the extent to which the 2005 IHR incorporated human rights criteria and considerations. Fourth, it overviews the human rights implications of the 2024 IHR reforms. The paper concludes by arguing that IHR reforms have only partly met the need to better address the instrument's human rights impacts.
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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 | 0.043 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".