Global Perspectives of Veterinary Education: Reflections from the 27th World Veterinary Congress
Bibliographic record
Abstract
All who work in veterinary education must recognize the breadth of their responsibilities. We are at a time in veterinary history where the profession must look how to meet a global standard for veterinary education and the global recognition of our basic qualification. There is a societal expectation that a professional approach is being taken to managing food security and food safety, as well as the environment and biodiversity. Within our profession, we need to recognize our obligation to fulfill this role. Society and regulators will seek those who have the capacity to provide for society's needs. It is no longer a case of relying on the reputation of our profession alone. There is a significant disparity in universal recognition of the veterinary qualification between the major blocs of the developed world and the developing world. Graduates from developing countries are not widely recognized, and they and their countries may therefore be at a significant disadvantage. There will be significant costs involved in raising the standards of veterinary education. A lead must be taken by a global body such as the World Veterinary Association to develop a long-term strategy toward global recognition of the veterinary qualification.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.036 | 0.030 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".