A Policy at the University of Adelaide for Student Objections to the Use of Animals in Teaching
Bibliographic record
Abstract
In veterinary medical education, the use of animals or cadaveric tissue as a component of teaching practice is common. Teachers are required, during the process of ethical review, to apply the 3 Rs principle (replacement, refinement, reduction) whenever they consider using animals during a teaching exercise. This often involves use of replacement strategies, such as utilization of video footage or simulation-based training. However, aside from legislative or ethical requirements imposed by a country's regulatory framework on the institution, students are often the key advocates for using alternative teaching practices that do not make use of animals. This has prompted many institutions with veterinary and other life sciences teaching programs to develop student-conscientious objection policies to the use of animals in teaching. In this article, we discuss the procedures implemented to make provision for student-conscientious objectors at a new Australian Veterinary School, at the University of Adelaide. We also describe the processes to provide information to students and faculty on this issue and to facilitate information gathering on alternatives.
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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 | 0.042 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".