An Inter-Institutional External Peer-Review Process to Evaluate Educators at Schools of Veterinary Medicine
Bibliographic record
Abstract
Despite its fundamental importance, the educational mission of most schools of veterinary medicine receives far less recognition and support than the missions of research and discovery. This disparity is evident in promotion and tenure processes. Despite the frequent assertion that education is every college's core mission, there is a broad consensus that faculty are promoted primarily on the basis of meeting expectations relative to publications and grant funding. This expectation is evident in the promotion packets faculty are expected to produce and the criteria by which those packets are reviewed. Among the outcomes is increasing difficulty in hiring and retaining faculty, including young clinicians and basic scientists who are drawn to academic institutions because of the opportunity to teach. The Regional Teaching Academy (RTA) of the West Region Consortium of Colleges of Veterinary Medicine initiated an inter-institutional collaboration to address the most important obstacles to recognizing and rewarding teaching in its five member colleges. Working from the medical education literature, the RTA developed an Educator's Promotion Dossier, workshops to train promotion applicants, and an external review process. Initial use has shown that the reviews are efficient and complete. Administrators have expressed strong support for the product, a letter of external review that is returned to a promotion applicant's home institution. The overall result is an evidence-based, structured process by which teaching-intensive faculty can more fully document their achievements in teaching and educational leadership and a more rigorous external review process by which member colleges can assess quality, impact, and scholarly approach.
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 | 0.383 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".