COVID-19 and Global Distributive Justice: ‘Health Diplomacy’ of India and South Africa for the TRIPS waiver
Bibliographic record
Abstract
The second wave of the COVID-19 pandemic had left heart-wrenching impacts on all facets of life in general and the availability, accessibility, and affordability of medicines and vaccines in particular. Rather, the world has been divided into two groups regarding access to medicine and vaccines as haves and have-nots. The rich countries had pre-ordered the vaccines of COVID-19 along with the holding of the same. The pandemic situation was further worsened, given the Trade-Related Intellectual Property Rights (TRIPS) in practice and restrictions on sharing technology of vaccines, medicines, and life-saving equipment. In this context, India and South Africa have proposed the joint proposal and garnered support for waiving off TRIPS to ensure equity, accessibility, and affordability of vaccines and the same as public goods. In this review, we emphasize that global justice is one of the important elements of normative international theories, which focus on all the moral obligations from the world's rich to the world's poor. The paper also questions and argues that if the rich countries fail to go by the principles of global justice, can the Indian and South African (SA) patent diplomacy play a catalyst role in global justice? The review concludes with an emphasis on global solidarity, and the acceptance of joint India-South Africa's "patent diplomacy" for TRIPS waiver would result in mass production and fair distribution, making the COVID-19 medicines and technologies available to everyone regardless of their poor-rich status.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".