Exploring the ethics of a One Health approach to harm reduction: A way forward for industrialized animal-sourced foods
Bibliographic record
Abstract
Abstract Industrialized animal-sourced foods (IASFs) are well-documented for their benefits (nutritional, economic) and harms (health, environment, welfare, justice). How we should address harms and effect a favourable balance with IASF benefits requires both practical and normative analyses. To reduce the significant harms across human, non-human animal, and environmental aspects of IASFs, this commentary considers the ethical possibilities of a One Health approach to harm reduction. We examine what a One Health-informed harm reduction approach might include ethically, and what questions would need to be asked and answered to begin to address the multifaceted harms posed by perpetuating IASF industries. One Health impact statement The ethical debate considers the foundational ethics of a One Health approach to harm reduction, and what that ethic would look like in practice if applied to the complex issue of industrial animal-sourced foods. A One Health-oriented approach to harm reduction uniquely considers harms to health and well-being across the environment, human, non-human animal nexus. We outline the history of ethics in One Health and harm reduction, and consider how these ethical foundations might be intertwined in a One Health approach to harm reduction, particularly as applied to the case of IASF.
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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 | 0.065 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.093 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".