"Digital global health diplomacy" for climate change and human security in the Anthropocene
Bibliographic record
Abstract
The COVID-19 pandemic has now affected everyone, threatening every aspect of our well-being with over 617597680 confirmed cases, including 6532705 deaths globally. The context of the Anthropocene is the backdrop for the novel, interlinked, systemic, and global threats. Anthropocene is a term proposed to designate the era in which human beings have become predominant drivers of planetary change, drastically altering the planet's biosphere. The concept of global health diplomacy (GHD), which connects the domains of health and international relations, has a critical role in advancing human security. Thus, there is a need for new forms of diplomacy, which is critically important in this complex intermestic and interdependent Anthropocene era, where globalization has inevitably linked nations and population health. This paper introduces, analyzes, and attempts to define "Digital Global Health Diplomacy" (DGHD), which has gained great momentum during this COVID-19 pandemic with concurrent health and human security threats. The application of digital formats to the existing traditional structures for dialogue has become a more popular tool recently. Furthermore, digital means are being used during the COVID-19 pandemic to share the health diplomacy discourse at subnational, supranational, international, regional, and global platforms. DGHD reminds us again of the criticality of this multidisciplinary concept involving the contributions of diplomats, global health specialists, digital technology experts, economists, trade specialists, international law, political scientists, etc., in the global policymaking process. If used effectively by trained global health diplomats through innovative digital platforms, DGHD has a great scope of delivering results faster and has more reach than the traditional approach.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".