Introducing One Health to the Ethical Debate About Zoonotic Diseases in <scp>S</scp>outheast Asia
Bibliographic record
Abstract
Pandemic plans recommend phases of response to an emergent infectious disease (EID) outbreak, and are primarily aimed at preventing and mitigating human-to-human transmission. These plans carry presumptive weight and are increasingly being operationalized at the national, regional and international level with the support of the World Health Organization (WHO). The conventional focus of pandemic preparedness for EIDs of zoonotic origin has been on public health and human welfare. However, this focus on human populations has resulted in strategically important disciplinary silos. As the risks of zoonotic diseases have implications that reach across many domains outside traditional public health, including anthropological, environmental, and veterinary fora, a more inclusive ecological perspective is paramount for an effective response to future outbreaks.
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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 | 0.060 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.084 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.033 | 0.057 |
| Insufficient payload (model declined to judge) | 0.004 | 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".