Addressing Antibiotic Resistance Requires Robust International Accountability Mechanisms
Bibliographic record
Abstract
Most proposals for new international agreements aim to address important global challenges. If the goal is to solve problems, then the value of these agreements depends on their ability to influence the world — to shape norms, constrain behavior, facilitate cooperation, and mobilize action. A recent review of empirical studies has suggested that many international agreements fail to achieve their aspirations. The review indicates that the form in which states make commitments to each other — through an international legal agreement or through other means — may not be as important as commonly thought. It is the content of the commitments and how these are supported by mechanisms to encourage implementation that matter the most. When developing proposals for new international agreements, like the one that has recently been proposed to address antibiotic resistance (ABR), attention to implementation mechanisms should therefore be equal to if not greater than the attention paid to its form.
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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 | 0.100 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".