Human rights law, intellectual property and vaccine nationalism: lessons for the post-COVID-19 world
Bibliographic record
Abstract
The rollout of COVID-19 vaccines during the pandemic was labelled a ‘parallel pandemic of human rights’. Occuring simultaneously with the primary public health crisis, the production, distribution, and profiteering that followed the announcement of vaccines were satirically referred to as ‘vaccine nationalism’ or ‘vaccine apartheid’. Against the backdrop of the COVID-19 pandemic and its attendant vaccine inequality, this article examines the extent to which existing international human rights laws provide a framework for equity in global vaccine distribution. The article explores the delicate balance between protecting intellectual property and promoting public health and human rights: the private property rights of vaccine developers and the broader human rights of members of the public to vaccine access. It does this by appraising the decision on the proposed waiver of intellectual property rights for COVID-19 vaccines and treatments under the World Trade Organization's (WTO) Agreement on Trade-Related Aspects of Intellectual Property Rights during the pandemic. The article argues that a reform of the WTO multilateral system is needed to ensure that in future global health emergencies, private interests are subordinated to global public health needs and that global action is not conditioned by nationalism and hegemonic positions in international relations.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".