From more‐than‐human solidarity to multi‐species biographical value: insights from a veterinary school about ethical dilemmas in One Health promotion
Bibliographic record
Abstract
This article features a partnership between a veterinary school and a charity that aims to enhance the wellbeing of low-income people. Through this partnership, the charity periodically hosts veterinary clinics for clients and their pets. Even as the veterinarians and veterinary students duly examine people's pets, these pop-up clinics aim to help people and their pets. Hence our analysis revolves around the ethics of 'more-than-human solidarity'. By 'more-than-human solidarity', we mean efforts to help others that either center on or that implicate non-human beings. To delve into the ethical and sociological implications of subsidised veterinary services, and to assist with program planning, we conducted several in-depth interviews with veterinarians. Most substantively, we found that the veterinary school's outreach clinics give rise to multi-species biographical value, which is prized as a pedagogical resource for veterinary students. The veterinarians whom we interviewed felt troubled by the extent to which the pop-up clinics ultimately benefited the veterinary school, but also by the shortage of subsidised veterinary services in the vicinity. Based on these interviews and our own reflections, we invite more scholarship on cultural, economic and political influences that shape the lives of human beings and non-human animals alike.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.079 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.016 |
| 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".