Biodefense Policy Analysis—A Systems-based Approach
Bibliographic record
Abstract
Understanding the overall biosecurity and biodefense policy landscape, the relationships between policies and their effects on each other, and the mechanisms for leveraging advances in science and technology to enhance defensive capabilities is crucial for ensuring that policy strategies address long-standing gaps and challenges. To date, policy analyses have been conducted primarily on single issues, which limits analyses of broader effects of policies, particularly after implementation. Here we describe the first-ever systems-based analysis of the US biosecurity and biodefense policy landscape to analyze functional relationships between policies, including examination of the unintended positive or negative consequences of policy actions. This analysis revealed a striking bifurcation of the US policy landscape for countering biological threats, with one grouping of policies focused on prevention of theft, diversion, or deliberate malicious use of biological sciences knowledge, skills, materials, and technologies (ie, biosecurity) and a second grouping on development of capabilities and knowledge to assess, detect, monitor, respond to, and attribute biological threats (ie, biodefense). An analysis of indirect effects demonstrated that policies within groups may result in mutual benefit, but policies in different groups may counteract each other, limiting achievement of the policy objectives in either group. The current policy landscape predominantly focuses on pathogens and toxins, having limited focus on rapidly changing biotechnologies with potential to positively contribute to biodefense capabilities or introduce unknown and/or unacceptable security risk. Based on our analyses, we present actions for implementing biosecurity and biodefense policy in the United States that intends to harness the benefits of science and technology while also minimizing potential risks. This article synthesizes and highlights the major findings and conclusions from the detailed analyses, which can be found in the full report (http://www.gryphonscientific.com/biosecurity-policy/). The authors describe a systems-based analysis of the US biosecurity and biodefense policy landscape to analyze functional relationships between policies, which revealed 2 approaches in US policy for countering biological threats: (1) prevention of theft, diversion, or deliberate malicious use of biological sciences knowledge, skills, materials, and technologies, and (2) development of capabilities and knowledge to assess, detect, monitor, respond to, and attribute biological threats. Current policy focuses on pathogens and toxins, having limited focus on rapidly changing biotechnologies with potential to positively contribute to biodefense capabilities or introduce unknown and/or unacceptable security risk.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".