Strengthening the COVID-19 pandemic response, global leadership, and international cooperation through global health diplomacy
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic continues to claim lives around the world and, to some extent, reflects the failure of international cooperation. Global health diplomacy (GHD)can be a bridge for international cooperation for tackling public health crises, strengthening health systems through emphasizing universal health coverage for sustainable and equitable development, and rebuilding multilateral organizations. It can be a catalyst for future global health initiatives. Health should not be used as a political tool at the cost of people's lives, nor should it become a proxy for geopolitics but can be used to diffuse tensions and create a positive environment for political dialogue. Health diplomacy's focus should be to mitigate inequality by making available diagnostics, therapeutics, and vaccines as a global public good. The implications for the lack of international cooperation will lead to increased global disparities and inequities as the countries that cannot procure vaccines will find their population more vulnerable to the pandemic's repercussion. Though the international cooperation on trade has suffered the impact of geopolitical shifts and competition, through engaging in GHD, the governments can align the trade and health policies. Amid this global health crisis, the World Health Organization (WHO) has faced an increase in International Health Regulations violations, limiting its influence and response during this COVID-19 pandemic. Nations need to develop a sense of cooperation that serves as the basis for a mutual strategic trust for international development. The priorities of all the countries should be to find the areas of common interest, common operational overlap on development issues, and resource allocation for this global fight against COVID-19.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".