Comparison of the Moral Sensitivity, Judgment, and Actions of Australian and Turkish Veterinary Students in Relation to Animal Ethics Issues
Bibliographic record
Abstract
Veterinarians regularly face animal ethics conflicts, and research has identified the moral reasoning methods that they utilize to solve these. It is unclear whether students' sensitivity to animal ethics conflicts influences their reasoning methods, and the recent development of appropriate tests allows this to be assessed. We compared the moral reasoning methods, intended action and sensitivity of 112 first-year veterinary students in two contrasting veterinary schools, in Australia and Turkey. Students were presented with two animal ethics issues: breeding blind hens to address welfare concerns in intensive housing, for moral reasoning evaluation; and a video of a lame dairy cow walking, for sensitivity assessment. The sensitivity score was not related to the principal moral reasoning methods, which are Personal Interest (PI), Maintaining Norms (MN), and Universal Principles (UP). However, less sensitive students were more concerned about professional criticism of emotional reactions when addressing the hen scenario. Turkish students, mostly males, used more MN reasoning when deciding the hen dilemma. Australian, mostly female, students did not. Overall, female students were more likely to consider the universal moral principles in moral reasoning than male students and were more likely to recommend against breeding blind hens. This suggests that females are more likely to consider the ethical implications of their actions than males. This study demonstrates relationships between ethical sensitivity (ES) and moral reasoning, and cultural and gender effects on moral action choices. Students placing greater importance on professional criticism about having an emotional reaction are more likely to be those who have less ES.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".