Carving the meat at the joint: The role of defining how animals are viewed and treated in the governance of (re‐)emergent pandemic zoonoses in international law
Bibliographic record
Abstract
Abstract Pandemic zoonoses, such as COVID‐19, are one of the greatest challenges of the 21st century. International governance tasked with attempting to prevent the (re‐)emergence of zoonotic disease in the first place, or preparation and actual response once (re‐)emergence or spread has occurred, has largely been fragmented among different governance systems, such as health, food, environment, and trade. The international legal instruments that these governance systems use reflect different ways of viewing and treating animals, which has led to a similarly fragmented approach to the regulation of human–animal interactions related to zoonoses. To illustrate this state of affairs, we develop a descriptive conceptual taxonomy to elucidate and map out how the status and evaluative stance taken toward animals can lead to shaping human‐animal relationships by structuring the nature of their interactions and disposes us to adopt governance approaches that seek to regulate human–animal relationships in particular ways. This paper concludes by highlighting some implications surrounding the fragmented conceptualization and practice around how animals are viewed and treated for the future of international legal governance of pandemic zoonoses.
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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 | 0.050 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.127 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".