Book Review: Human Rights in Global Health: Rights-Based Governance for a Globalizing World, edited by Benjamin M. Meier & Lawrence O. Gostin
Bibliographic record
Abstract
This groundbreaking compilation—edited by two scholars who helped to establish the “health and human rights” field—systematically explores the structures and processes of human rights implementation in global health institutions, arguing that a rights-based approach to health governance advances global health. This 640-page volume brings together forty-six experienced scholars and practitioners who have contributed to twenty-five chapters organized into six thematic sections. This “unprecedented collection of experts” provides unique, hands-on insights into how the “institutional determinants of the rights-based approach to health” facilitate—or hinder—the “mainstreaming” of human rights into global health interventions. The “institutional determinants,” which, in the contributors’ view, promote the effective integration of human rights implementation into global health governance, are: “governance” (formal commitments, human rights leadership, and member State support); “bureaucracy” (institutional structure and human rights culture); “collaborations” (inter-organizational partnerships and civil society participation); and “accountability” (internal monitoring and independent evaluation).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.022 |
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".