Operationalizing the One Health approach: the global governance challenges
Bibliographic record
Abstract
While there has been wide-ranging commitment to the One Health approach, its operationalisation has so far proven challenging. One Health calls upon the human, animal and environmental health sectors to cross professional, disciplinary and institutional boundaries, and to work in a more integrated fashion. At the global level, this paper argues that this vision is hindered by dysfunctions characterising current forms of global health governance (GHG), namely institutional proliferation, fragmentation, competition for scarce resources, lack of an overarching authority, and donor-driven vertical programmes. This has contributed, in part, to shortcomings in how One Health has been articulated to date. An agreed operational definition of One Health among key global institutions, efforts to build One Health institutions from the ground up, comparative case studies of what works or does not work institutionally, and high-level global support for research, training and career opportunities would all help to enable One Health to help remedy, and not be subsumed by, existing dysfunctions in GHG.
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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 | 0.103 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".