Integrating Animal Welfare into Veterinary Education: Using an Online, Interactive Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Veterinarians in the United States and abroad are faced with growing public concern for the welfare of animals, particularly those in production. To prepare veterinarians to exert the leadership expected by the public and industry, steps should be taken to provide instruction in animal welfare at veterinary colleges. The ultimate goal is to offer courses in animal welfare in a consistent manner on a global scale, utilizing existing expertise in an efficient and cost-effective manner. Given the intense curricula of veterinary schools and the scarcity of instructors trained in animal welfare, a nontraditional approach is needed to educate veterinary students in the United States and abroad in animal welfare. Michigan State University (MSU) is developing a graduate-level, online interactive course in animal welfare assessment. The course will approach the topic of animal welfare education from a holistic, multidisciplinary standpoint (encompassing ethics, economics, and behavior) and address issues important to the general public and the international community. The MSU course will draw on renowned international animal welfare experts, allowing students to receive high-quality education that would be difficult in any other circumstance. The course will bridge an important gap in the veterinary curriculum and offer a complete and congruous education in animal welfare to veterinarians worldwide. The MSU course will also serve as a model for collaboration in content assembly and course delivery, by using technology to leverage global expertise in the interests of educational equity. In addition to innovative technology, such as the use of Web-collaboration software to create the course, a variety of media that enable students to interact with the material will also be incorporated throughout the course. Thus, the course will not only utilize the Internet to provide access to high-quality material, but will also require the active participation of the student, which is needed for effective learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it