Assessing Proposals for New Global Health Treaties: An Analytic Framework
Bibliographic record
Abstract
We have presented an analytic framework and 4 criteria for assessing when global health treaties have reasonable prospects of yielding net positive effects. First, there must be a significant transnational dimension to the problem being addressed. Second, the goals should justify the coercive nature of treaties. Third, proposed global health treaties should have a reasonable chance of achieving benefits. Fourth, treaties should be the best commitment mechanism among the many competing alternatives. Applying this analytic framework to 9 recent calls for new global health treaties revealed that none fully meet the 4 criteria. Efforts aiming to better use or revise existing international instruments may be more productive than is advocating new treaties.
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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 | 0.123 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.032 | 0.028 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".