The Relationship between Intuitive Action Choices and Moral Reasoning on Animal Ethics Issues in Students of Veterinary Medicine and Other Relevant Professions
Bibliographic record
Abstract
With growing understanding of animals' capabilities, and public and organizational pressures to improve animal welfare, moral action by veterinarians and other relevant professionals to address animal issues is increasingly important. Little is known about how their action choices relate to their moral reasoning on animal ethics issues. A moral judgment measure, the VetDIT, with three animal and three non-animal scenarios, was used to investigate the action choices of 619 students in five animal- and two non-animal-related professional programs in one Australian university, and how these related to their moral reasoning based on Personal Interest (PI), Maintaining Norms (MN), or Universal Principles (UP) schemas. Action choices showed significant relationships to PI, MN, and UP questions, and these varied across program groups. Having a previous degree or more experience with farm animals had a negative relationship, and experience with horses or companion animals a positive relationship, with intuitive action choices favoring life and bodily integrity of animals. This study helps to explain the complex relationship between intuitive moral action choices and moral reasoning on animal ethics issues. As a useful research and educational tool for understanding this relationship, the VetDIT can enhance ethical decision making.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".