Bibliographic record
Abstract
This article suggests that veterinary medicine has a leadership role to play in our society on ethical matters involving non-human animals. The article contrasts two trends within veterinary medicine; the first trend is a continuation of the avowedly utilitarian attitude toward non-humans that has its roots in Western veterinary medicine's eighteenth-century origins, and the second is the implicit view in veterinary practice that animals matter in and of themselves. Using the idea of alternatives in research and teaching, the article suggests that, in the years to come, veterinary medicine's answers to the relationships of these two trends will shape not only the soul of veterinary medicine, veterinary education, and the veterinary profession but, just as importantly, the larger society and culture themselves. This text is based on the keynote address delivered at the AAVMC Education Symposium in Washington, DC, on March 9, 2006, under the title "Ethical Issues Impacting Animal Use in Veterinary Medical Teaching."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".