Bibliographic record
Abstract
I have taken this opportunity to review several challenges that have faced veterinary medical education in my time, to recount how they were dealt with, and to probe what lessons might be learned that might frame responses to some of the current challenges facing veterinary education. To lay the background for this discussion, I would like first to comment briefly on my view of the present state of veterinary medicine and veterinary education. Taking a long-term perspective, the veterinary profession today, by all measures, is fulfilling its role of service to society better than at any other time in its long and illustrious history. Although improvements are needed, and we shall refer to some of them, veterinary medicine can proudly take its place as one of the most competent, effective, and service-oriented of all the world’s professions. The veterinary education establishment also is highly accomplished and generally successful in its teaching, research, and public service activities, but it faces some difficult problems that largely have been created by the changing needs of some of veterinary medicine’s most important constituencies. Veterinary medical colleges also are confronted with serious funding problems for bricks and mortar, operations, and training stipends for post-DVM students.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".