A Survey of International Legal Instruments to Examine Their Effectiveness in Improving Global Health and in Realizing Health Rights
Bibliographic record
Abstract
Many global health issues, almost by definition, do not recognize state borders and therefore require bi-lateral, or more often multi-lateral international solutions. These latter solutions are articulated in international instruments (declarations, conventions, treaties, constitutions of international bodies, etc). However, the gap between formal adoption of such instruments by signatory states and substantive implementation of the articulated solutions can be very wide. This paper surveys a selection of international legal instruments, including those where the sought after positive outcomes have been achieved, and those that have been ineffective, with little or no real progress being made. The paper looks for commonalities, both in the nature of the problems and the forms of the international legal instruments, to seek answers as to why some instruments ultimately succeeded where others failed. It also provides some guidance to law/ treaty makers to help ensure that they frame future instruments in such a way as to maximize the probability that those instruments will have a substantive positive impact on global health and health rights.
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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 | 0.079 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".