Accelerating Action in Global Health Security: Global Biosecurity Dialogue as a Model for Advancing the Global Health Security Agenda
Bibliographic record
Abstract
Biosecurity and biosafety measures are designed to mitigate intentional and accidental biological risks that pose potentially catastrophic consequences to a country's health system, security, and political and economic stability. Unfortunately, biosecurity and biosafety are often under-prioritized nationally, regionally, and globally. Security leaders often deemphasize accidental and deliberate biological threats relative to other challenges to peace and security. Given emerging biological risks, including those associated with rapid technological advances and terrorist and state interest in weapons of mass destruction, biosecurity deserves stronger emphasis in health and security fora. The Global Biosecurity Dialogue (GBD) was initiated to align national and regional donor initiatives toward a common set of measurable targets. The GBD was launched by the Nuclear Threat Initiative (NTI), with support from Global Affairs Canada's Weapons Threat Reduction Program and the Open Philanthropy Project, and in coordination with the government of The Netherlands as the 2018-19 Chair of the Global Health Security Agenda (GHSA) Action Package Prevent-3 (APP3) on Biosafety and Biosecurity. The GBD provides a multisectoral forum for sharing models, enabling new actions to achieve biosecurity-related targets, and promoting biosecurity as an integral component of health security. The GBD has contributed to new national and continent-wide actions, including the African Union and Africa Centres for Disease Control and Prevention's new regional Initiative to Strengthen Biosafety and Biosecurity in Africa. Here we present the GBD as a model for catalyzing action within APP3. We describe how the benefits of this approach could expand to other GHSA Action Packages and international health security initiatives.
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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 | 0.075 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.031 | 0.042 |
| Open science | 0.003 | 0.041 |
| Research integrity | 0.033 | 0.043 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".