Student insights towards animal welfare science and law. Survey results from Sassari University, Italy
Bibliographic record
Abstract
In this paper, we describe the results of an online survey consisting of 23 questions created to evaluate the knowledge and interest on animal welfare by students attending 15 different scientific, medical, and biomedical courses at University of Sassari, Italy. The survey collected students' demographic data, level of knowledge both on animal welfare and 3Rs, as well as their opinions on animal experimentation. The majority of the cohort was female and over 24 years of age. About a third of the students responded that their graduate programme included subjects that taught science, ethics, and animal welfare legislation. Just 21.2% of respondents had heard about the concept of 3Rs. About a quarter of the students believed that animal models can be replaced by in vitro and in silico methods while half believed that both are needed. However, 70% of the participants did not know the existence of an Ethics and Animal Welfare Committee. The result showed the importance of an Animal Welfare Course for the professional future of a larger number of students and underlined the key role of veterinary medicine in promoting ethics and animal experimentation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".