Understanding and strengthening wildlife and zoonotic disease policy processes: A research imperative
Bibliographic record
Abstract
The COVID-19 pandemic highlights the urgency and importance of monitoring, managing and addressing zoonotic diseases, and the acute challenges of doing so with sufficient inter-jurisdictional coordination in a dynamic global context. Although wildlife pathogens are well-studied clinically and ecologically, there is very little systematic scholarship on their management or on policy implications. The current global pandemic therefore presents a unique social science research imperative: to understand how decisions are made about preventing and responding to wildlife diseases, especially zoonoses, and how those policy processes can be improved as part of early warning systems, preparedness and rapid response. To meet these challenges, we recommend intensified research efforts towards: (i) generating functional insights about wildlife and zoonotic disease policy processes, (ii) enabling social and organizational learning to mobilize those insights, (iii) understanding epistemic instability to address populist anti-science and (iv) anticipating evolving and new zoonotic emergences, especially their human dimensions. Since policy processes for zoonoses can be acutely challenged during the early stages of an epidemic or pandemic, such insights can provide a pragmatic, empirically-based roadmap for enhancing their robustness and efficacy, and benefiting long-term decision-making efforts.
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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 | 0.079 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.023 | 0.049 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".