Global health diplomacy at the intersection of trade and health in the COVID-19 era
Bibliographic record
Abstract
Global health diplomacy has gained significant importance and undoubtedly remained high on the agendas of many nations, regional and global platforms amid the coronavirus disease 2019 (COVID-19) pandemic. Many countries have realized the importance of the health sector and the value of a healthy workforce. However, there is little control on issues related to trade that impact on human health due to the dominance of profit-oriented business lobbies. A balance, however, needs to be struck between economic profits and a healthy global population. This paper aimed to highlight the importance of building capacity in global health diplomacy, especially during the COVID-19 pandemic so that health personnel may effectively negotiate on the multisectoral stage to secure the resources they need. The recent proposal to waive off certain provisions of the Trade-Related Aspects of Intellectual Property Rights (TRIPS) agreement for the prevention, containment and treatment of COVID-19 by India and South Africa at the World Trade Organization (WTO) presents an important opportunity for all governments to unite and stand up for public health, global solidarity, and equitable access at the international level so that both developed and developing nations may enjoy improved health outcomes related to the COVID-19 pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".