The proposal for waiver of WTO’s TRIPS Agreement to prevent, contain and treat COVID-19: investigating the benefits and challenges for low- and middle-income countries
Bibliographic record
Abstract
In October 2020, India and South Africa submitted a proposal to temporarily waive the provisions of the WTO Agreement on Trade-Related Aspects of Intellectual Property (TRIPS Agreement)1 at the Council for TRIPS.2 This proposal aims to waive the member countries’ obligations to implement, apply and enforce the provisions relating to copyright, industrial designs, patents and undisclosed information for the prevention, containment and treatment of COVID-19.3 In other words, the TRIPS waiver would remove the intellectual property rights (IPRs)-related barriers to access COVID-19 vaccine and treatments worldwide, particularly in low- and middle-income countries (LMICs),4 minimising the vaccine gap between high-income countries and LMICs.5 LMICs, civil society organisations and global health and human rights advocates have embraced the implementation of a TRIPS waiver wholeheartedly.6 The traditional opponents to the TRIPS waiver—the multinational pharmaceutical companies (Big Pharma)—have shown their displeasure, and some high-income countries, including the USA, the UK, European Union, Australia and Canada, have challenged its implementation at the TRIPS Council.7 However, there is a growing tendency among some of these initially opposed high-income countries, including the USA and Canada, and the World Trade Organization (WTO) to offer limited support for TRIPS waiver.8 Indeed, the emerging concerns among global policy actors raise expectations that a TRIPS waiver will be implemented in the future.9 However, the TRIPS waiver alone would not ensure that all LMICs obtain equitable access to COVID-19 vaccines, as LMICs are not homogenous regarding resource availability and financial capacity to produce or import vaccines. Especially, the TRIPS waiver is unlikely to work without the transfer of know-how or expertise relating to COVID-19 vaccines. Further, it is reasonable to foresee that Big Pharma may interfere with implementing a TRIPS waiver, particularly in LMICs.10 Therefore, there need to be specific mechanisms enabling LMICs to obtain the benefits of the proposed TRIPS waiver.11 Accordingly, this paper considers how global policy actors can enable LMICs to obtain the benefits of a TRIPS waiver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.068 | 0.036 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".