World Health Organization and disease surveillance: Jeopardizing global public health?
Bibliographic record
Abstract
Health issues now evolve in a global context. Real-time global surveillance, global disease mapping and global risk management characterize what have been termed 'global public health'. It has generated many programmes and policies, notably through the work of the World Health Organization. This globalized form of public health raises, however, some important issues left unchallenged, including its effectiveness, objectivity and legitimacy. The general objective of this article is to underline the impacts of WHO disease surveillance on the practice and theorization of global public health. By using the surveillance structure established by the World Health Organization and reinforced by the 2005 International Health Regulations as a case study, we argue that the policing of 'circulating risks' emerged as a dramatic paradox for global public health policy. This situation severely affects the rationale of health interventions as well as the lives of millions around the world, while travestying the meaning of health, disease and risks. To do so, we use health surveillance data collected by the WHO Disease Outbreak News System in order to map the impacts of global health surveillance on health policy rationale and theory.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".