Diets, Diseases, and Discourse: Lessons from COVID-19 for Trade in Wildlife, Public Health, and Food Systems Reform
Bibliographic record
Abstract
The COVID-19 pandemic has brought to light significant failures and fragilities in our food, health, and market systems. Concomitantly, it has emphasized the urgent need for a critical re-evaluation of many of the policies and practices that have created the conditions in which viral pathogens can spread. However, there are many factors that are complicating this process; among others, the uncertain, rapidly evolving, and often poorly reported science surrounding the virus’ origins has contributed to a politically charged and often rancorous public debate, which is concerning insofar as the proliferation of divisive discourse may hinder efforts to address complex and collective concerns in a mutually cooperative manner. In developing ethical and effective responses to the disproportionate risks associated with certain food production and consumption practices, we argue that the focus should be on mitigating such risks wherever they arise, instead of seeking to ascribe blame to specific countries or cultures. To this end, this article is an effort to inject some nuance into contemporary conversations about COVID-19 and its broader implications, particularly when it comes to trade in wildlife, public health, and food systems reform. If COVID-19 is to represent a turning point towards building a more equitable, sustainable, and resilient world for both humans and nonhuman animals alike, the kind of fractioning that is currently being exacerbated by the use of loaded terms such as “wet market” must be eschewed in favour of a greater recognition of our fundamental interconnectedness.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.082 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".