The Prevention Imperative: International Health and Environmental Governance Responses to Emerging Zoonotic Diseases
Bibliographic record
Abstract
Abstract Despite widespread recognition of the threat posed by emerging zoonotic diseases (EZDs) to human and animal health and the economy, the root causes of EZDs are largely ignored by the international community. In particular, the links between wildlife health, human-induced land-use change, and EZDs have not been adequately addressed. Generally, states are not required to evaluate the health impacts of land-use decisions within their territories. Similarly, global efforts to protect wild spaces are rarely identified as a health imperative. Where initiatives have been undertaken, they remain focused largely on detecting and controlling only those wildlife diseases that are known or suspected to be a threat to human and animal health or the economy. A critique of the existing international responses leaves no doubt that a preventative approach must be adopted to address human vulnerability to EZDs.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".