Instruction and Curriculum in Veterinary Medical Education: A 50-Year Perspective
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
Our knowledge of veterinary medicine has expanded greatly over the past 50 years. To keep pace with these changes and produce competent professionals ready to meet evolving societal needs, instruction within veterinary medical curricula has undergone a parallel evolution. The curriculum of 1966 has given way, shifting away from lecture-laboratory model with few visual aids to a program of active learning, significant increases in case- or problem-based activities, and applications of technology, including computers, that were unimaginable 50 years ago. Curricula in veterinary colleges no longer keep all students in lockstep or limit clinical experiences to the fourth year, and instead have moved towards core electives with clinical activities provided from year 1. Provided here are examples of change within veterinary medical education that, in the view of the authors, had positive impacts on the evolution of instruction and curriculum. These improvements in both how and what we teach are now being made at a more rapid pace than at any other time in history and are based on the work of many faculty and administrators over the past 50 years.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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