Developing an approach to assessing the political feasibility of global collective action and an international agreement on antimicrobial resistance
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AMR) is a global issue. International trade, travel, agricultural practices, and environmental contamination all make it possible for resistant microbes to cross national borders. Global collective action is needed in the form of an international agreement or other mechanism that brings states together at the negotiation table and commits them to adopt or implement policies to limit the spread of resistant microorganisms. This article describes an approach to assessing whether political and stakeholder interests can align to commit to tackling AMR. METHODS: Two dimensions affecting political feasibility were selected and compared across 82 countries: 1) states' global influence and 2) self-interest in addressing AMR. World Bank GDP ranking was used as a proxy for global influence, while human antibiotic consumption (10-year percent change) was used as a proxy for self-interest in addressing AMR. We used these data to outline a typology of four country archetypes, and discuss how these archetypes can be used to understand whether a proposed agreement may have sufficient support to be politically feasible. RESULTS: Four types of countries exist within our proposed typology: 1) wealthy countries who have the expertise and financial resources to push for global collective action on AMR, 2) wealthy countries who need to act on AMR, 3) countries who require external assistance to act on AMR, and 4) neutral countries who may support action where applicable. Any international agreement will require substantial support from countries of the first type to lead global action, and from countries of the second type who have large increasing antimicrobial consumption levels. A large number of barriers exist that could derail efforts towards global collective action on AMR; issues of capacity, infrastructure, regulation, and stakeholder interests will need to be addressed in coordination with other actors to achieve an agreement on AMR. CONCLUSIONS: Achieving a global agreement on access, conservation, and innovation - the three pillars of AMR - will not be easy. However, smaller core groups of interested Initiator and Pivotal Countries could develop policy and resolve many issues. If highly influential countries take the lead, agreements could then be scaled up to achieve global action.
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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 | 0.042 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".