Culling and the Common Good: Re-evaluating Harms and Benefits under the One Health Paradigm
Bibliographic record
Abstract
One Health (OH) is a novel paradigm that recognizes that human and non-human animal health is interlinked through our shared environment. Increasingly prominent in public health responses to zoonoses, OH differs from traditional approaches to animal-borne infectious risks, because it also aims to promote the health of animals and ecological systems. Despite the widespread adoption of OH, culling remains a key component of institutional responses to the risks of zoonoses. Using the threats posed by highly pathogenic avian influenza viruses to human and animal health, economic activity and food security as a case exemplar, we explore whether culling and other standard control measures for animal-borne infectious disease might be justified as part of OH approaches. Our central premise is that OH requires us to reformulate 'health' as universal good that is best shared across species boundaries such that human health and well-being are contingent upon identifying and meeting the relevant sets of human and non-human interests and shared dependencies. Our purpose is to further nascent discussions about the ethical dimensions of OH and begin to describe the principles around which a public health agenda that truly seeks to co-promote human and non-human health could potentially begin to be implemented.
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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 | 0.162 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.103 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".