Making Use of Existing International Legal Mechanisms to Manage the Global Antimicrobial Commons: Identifying Legal Hooks and Institutional Mandates
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is an urgent threat to global public health and development. Mitigating this threat requires substantial short-term action on key AMR priorities. While international legal agreements are the strongest mechanism for ensuring collaboration among countries, negotiating new international agreements can be a slow process. In the second article in this special issue, we consider whether harnessing existing international legal agreements offers an opportunity to increase collective action on AMR goals in the short-term. We highlight ten AMR priorities and several strategies for achieving these goals using existing "legal hooks" that draw on elements of international environmental, trade and health laws governing related matters that could be used as they exist or revised to include AMR. We also consider the institutional mandates of international authorities to highlight areas where additional steps could be taken on AMR without constitutional changes. Overall, we identify 37 possible mechanisms to strengthen AMR governance using the International Health Regulations, the Agreement on the Application of Sanitary and Phytosanitary Measures, the Agreement on Trade-Related Aspects of Intellectual Property Rights, the Agreement on Technical Barriers to Trade, the International Convention on the Harmonized Commodity Description and Coding System, and the Basel, Rotterdam, and Stockholm conventions. Although we identify many shorter-term opportunities for addressing AMR using existing legal hooks, none of these options are capable of comprehensively addressing all global governance challenges related to AMR, such that they should be pursued simultaneously with longer-term approaches including a dedicated international legal agreement on AMR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".