The WTO and the Covid‐19 “Vaccine Apartheid”: Big Pharma and the Minefield of Patents
Bibliographic record
Abstract
Unequal access to vaccines for the Covid-19 pandemic, also referred to as “vaccine apartheid,” has marginalized low-income countries again. In October 2020, India and South Africa proposed a temporary waiver from certain provisions of the TRIPS Agreement for the prevention of Covid-19 at the World Trade Organization (WTO). An agreement was later reached in Geneva on June 17, 2022. The objective of this article is to analyze the negotiation and agreement reached at the WTO. This article explores the difficulties of creating international public good in the field of public health within the milieu of powerful actors, namely big pharmaceutical companies with vested interests. The central argument of this article is that this agreement alone will not solve the vaccine access problem for low-income countries. It is too restrictive, does not cover trade secrets and know-how, production capacity, availability of raw materials, and even adds new limitations that did not exist before. The best option to promote the production of quality vaccines in low-income countries is to share technology and know-how on a voluntary basis through production agreements. One way to facilitate the cooperation of large pharmaceutical corporation is to make it easier for low-income countries to use compulsory licenses. Simplifying the use of this mechanism could help encourage pharmaceutical companies to enter into voluntary licensing agreements.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".