Crucial but Neglected: Limited Availability of Animal Welfare Courses in Education of Wildlife Researchers
Bibliographic record
Abstract
Animal welfare is a subject of increasing scientific and ethical concern in today's society, crucial for the well-being of animals used in research and the integrity of scientific data. Equipping researchers in the life science disciplines with a science-based knowledge of animal welfare, behaviour, physiology, and health is, therefore, essential. Nevertheless, previous studies evaluating animal welfare education focused on veterinary, laboratory, or farm animal science. Consequently, the aim of this study was, for the very first time, to map the prevalence of animal welfare courses in the university education of ecologists, wildlife biologists, and conservation managers in Europe, Canada, the USA, Australia, and New Zealand. A comprehensive assessment of 1548 universities was conducted, resulting in the identification of 596 relevant programs at the bachelor's and master's levels. Analysis of the curricula revealed that only 1% of the programs offered a formal course on animal welfare, while 65% provided courses on animal behaviour, 59% on animal physiology, and 34% on animal health. However, the majority of these courses were listed as electives rather than mandatory components of the programs. These results underscore the need for universities to incorporate more formal and obligatory education in animal welfare in order to better prepare future ecologists, wildlife biologists, and conservation managers for the challenges of working with wildlife.
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.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".