Bridging the commitment-compliance gap in global health politics: Lessons from international relations for the global action plan on antimicrobial resistance
Bibliographic record
Abstract
In 2015, 196 countries boldly committed to address global antimicrobial resistance (AMR). Now, five years later, progress reports suggest the implementation of AMR activities is vastly below what was initially promised. The challenge of overcoming the ‘commitment-compliance gap’ is not unique to AMR and is common in other areas of international politics. Global health policymakers can therefore learn from theories of international relations and experience in other sectors. We reviewed international relations scholarship to generate five hypotheses for why states might comply or not comply with their global commitments. We then conducted a public policy analysis of three past international agreements on biological diversity, climate change, and nuclear weapons to test these hypotheses and identify lessons for encouraging country compliance with global health agreements, with specific application to global AMR policies. To bridge the commitment-compliance gap, international leaders should: (1) frame incentives to maximise interests for action; (2) pursue enforcement mechanisms to induce state behaviour; (3) emphasise building a culture of trust by providing mutual assurance for action; (4) include mechanisms for managing poor performers; and (5) find opportunities for continual social learning. Agreements should be designed with flexibility, data sharing, and dispute settlement mechanisms and provide financial and technical assistance to states with less capacity to deliver.
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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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".