Intergovernmental or fully independent? Designing a scientific panel on evidence for action against antimicrobial resistance
Bibliographic record
Abstract
Effective global action against antimicrobial resistance (AMR) relies on the successful synthesis and translation of rigorous scientific evidence into policy and practice. Despite a call in 2019 by the Interagency Coordination Group on AMR to establish a policy-science interface, and the reaffirmation to establish a scientific panel in the 2024 Political Declaration on Antimicrobial Resistance, no authoritative entity currently exists that synthesizes the scientific evidence on AMR and outlines policy options based on the best scientific insight. A Scientific Panel on Evidence for Action against AMR (SPEA) could address this gap, as well as contribute to additional governance gaps in the space of AMR, by facilitating better global coordination and cooperation; establishing real-time evidence to guide policy actions; and monitoring progress towards any globally agreed upon AMR goals and targets. In this essay, we argue that SPEA has the potential to fulfill several governance functions, and we explore two design options for such a scientific panel to promote equitable and evidence-informed policy implementation. We first reflect on how the successes and failures of the Intergovernmental Panel on Climate Change (IPCC) should inform the SPEA. Building on these lessons, we then highlight the key functions of the SPEA, before proposing two models for how it could function in the context of the existing global governance of AMR. Finally, we reflect on the challenges inherent to each proposed governance model. The recent reaffirmation by the United Nations General Assembly to establish a scientific panel in the area of AMR represents a critical opportunity to enhance global AMR governance, promote evidence-based policy implementation, and foster international cooperation in combatting AMR.
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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 | 0.447 | 0.392 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.037 | 0.045 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.035 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".