Learning from the Montreal Protocol to improve the global governance of antimicrobial resistance
Bibliographic record
Abstract
The Montreal Protocol has played a critical role in promoting global collective action to phase out the use of ozone-depleting substances, ultimately preventing millions of cases of skin cancer, cataracts and other health issues related to ultraviolet radiation exposure. This success entails transferable lessons for coordinated action required to improve the global governance of other challenges. Like ozone depletion, antimicrobial resistance (AMR) is a challenge of the global commons, requiring coordinated actions across human, animal and environmental sectors. We identify equity, flexibility and accountability as three core governance principles that underlie the success of the protocol and employ the 3-i framework to understand how interests, ideas and institutions contributed to the protocol's success. Equity-promoting strategies consisted of an inclusive negotiation process, supporting developing countries with multilateral funding and a progressive compliance model. Flexibility was built into the protocol through the development of country-specific strategies, reorienting incentive structures for industry and facilitating regular amendments in response to emerging scientific evidence. Accountability was promoted by mobilising public advocacy, establishing targets and enforcement mechanisms and conducting independent scientific and technical assessments. Applying our proposed principles presents an opportunity to improve the global governance of AMR. Finally, we acknowledge limitations to our analysis, including our focus on a single environmental treaty, significantly greater funding requirements and multifacetted stakeholder involvement in the case of AMR, differing market and incentives structures in antibiotic development and distribution, and ethical concerns with using trade restrictions as a policy tool.
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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 | 0.204 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".