Global Health Law: Between Hard and Soft Law
Bibliographic record
Abstract
The field of global health law encompasses both "hard" law treaties and "soft" law policies that shape global health norms. Transitioning from "international health law" to "global health law and policy," global health policymakers have increasingly looked to soft law instruments to address public health needs in a rapidly globalizing world - within the World Health Organization and across global health governance. Yet, as policymakers have expanded the landscape of soft law policy instruments to advance global health across state and non-state actors, the COVID-19 response revealed the limitations of this soft law approach to global health threats, with states now seeking hard law reforms to strengthen global health governance. As hard and soft law can provide complementary approaches to preventing disease and promoting health, future research must conceptualize how these normative frameworks interact in advancing global health.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| 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".