Global governance for pandemic prevention and the wildlife trade
Bibliographic record
Abstract
Although ideas about preventive actions for pandemics have been advanced during the COVID-19 crisis, there has been little consideration for how they can be operationalised through governance structures within the context of the wildlife trade for human consumption. To date, pandemic governance has mostly focused on outbreak surveillance, containment, and response rather than on avoiding zoonotic spillovers in the first place. However, given the acceleration of globalisation, a paradigm shift towards prevention of zoonotic spillovers is warranted as containment of outbreaks becomes unfeasible. Here, we consider the current institutional landscape for pandemic prevention in light of ongoing negotiations of a so-called pandemic treaty and how prevention of zoonotic spillovers from the wildlife trade for human consumption could be incorporated. We argue that such an institutional arrangement should be explicit about zoonotic spillover prevention and focus on improving coordination across four policy domains, namely public health, biodiversity conservation, food security, and trade. We posit that this pandemic treaty should include four interacting goals in relation to prevention of zoonotic spillovers from the wildlife trade for human consumption: risk understanding, risk assessment, risk reduction, and enabling funding. Despite the need to keep political attention on addressing the current pandemic, society cannot afford to miss the opportunity of the current crisis to encourage institution building for preventing future pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".