Opportunities for Learning about Animal Welfare from Online Courses to Graduate Degrees
Bibliographic record
Abstract
Knowledge of animal welfare has become essential for veterinarians. However, there is no clear consensus about how to provide veterinarians and students with this critical information. The challenges associated with finding qualified instructors and fitting additional courses into an already full curriculum mean that options for learning about animal welfare beyond the veterinary school classroom must be explored. Online courses can be excellent ways for veterinary students and graduate veterinarians to become familiar with current animal-welfare science, assessment schemes, and regulations while removing geographical barriers and scheduling difficulties. Faculty at Michigan State University have created an online animal-welfare course with lecture material from experts in welfare-related social and scientific fields that provides an overview of the underlying concepts as well as opportunities to practice assessing welfare. However, to develop expertise in animal welfare, veterinarians need more than a single course. Graduate degrees can be a way of obtaining additional knowledge and scientific expertise. Traditional thesis-based graduate programs in animal-welfare science are available in animal-science departments and veterinary colleges throughout North America and offer students in-depth research experience in specific areas or species of interest. Alternatively, the University of Guelph offers a year-long Master of Science degree in which students complete a series of courses with a specialization in animal behavior and welfare along with a focused research project and paper. In summary, a range of options exist that can be tailored to provide graduate veterinarians and veterinary students with credible education regarding animal welfare beyond the veterinary curriculum.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.145 | 0.055 |
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".