An Inter-Institutional Collaboration to “Make Teaching Matter”: The Teaching Academy of the Consortium of West Region Colleges of Veterinary Medicine
Bibliographic record
Abstract
Veterinary medical education is a relatively small community with limited numbers of institutions, people, and resources widely dispersed geographically. The problems faced, however, are large-and not very different from the problems faced by (human) medical education. As part of an effort to share resources and build a community of practice around common issues, five colleges in the westernmost region of the United States came together to form a regional inter-institutional consortium. This article describes the processes by which the consortium was formed and the initiation of its first collaborative endeavor, an inter-institutional medical/biomedical teaching academy (the Regional Teaching Academy, or RTA). We report outcomes, including the successful launch of three RTA initiatives, and the strategies that have been considered key to the academy's success. These include strong support from the consortium deans, including an ongoing financial commitment, a dedicated part-time Executive Coordinator, regular face-to-face meetings that supplement virtual meetings, an organization-wide biennial conference, an effective organizational structure, and a core group of dedicated leaders and RTA Fellows. The western consortium and RTA share these processes, insights, and outcomes to provide a model upon which other colleges of veterinary medicine can build to further leverage inter-institutional collaboration.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".