Bibliographic record
Abstract
In the previous issue of the Journal of Veterinary Medical Education (JVME), Drs. Regina Schoenfeld-Tacher and Henry Baker introduced a new Special Topic focused on Educational Theory and Practice. The quality and broad relevance of the manuscripts submitted in response to their invitation to authors was impressive, and the number of submissions substantially exceeded the space available. With the continued assistance of Dr. Schoenfeld-Tacher, this issue continues that Special Topic with articles that are again noteworthy not only for quality but also for the significance of the contemporary educational issues that they address. The initiation of this Special Topic was yet another example of the exemplary leadership and guidance provided to the JVME by Dr. Henry Baker during his long tenure as Editor-in-Chief. I am indebted to him, to the JVME Editorial Board, and to the many JVME reviewers for maintaining a high standard of quality over the years. I also thank the Board of Directors of the Association of American Veterinary Medical Colleges (AAVMC) for their intellectual, philosophical, and, critically, financial support of the JVME. Finally, the University of Toronto Press continues to be a superb publishing partner for the JVME, and I am highly appreciative of the professionalism, expertise, and creativity of its staff. Academic veterinary medicine, and the profession of veterinary medicine at large, faces many pressing challenges as well as equally striking opportunities. As the premier, peer-refereed journal focused on innovation and advances in the broad field of veterinary medical education, the JVME is a critical resource at a pivotal time. The JVME is extraordinarily well positioned and equipped to provide the new knowledge in education that is so greatly needed, but also to serve as a venue for an informed and critical assessment of key issues that face academic veterinary medicine, now and in the future. It is my privilege to serve as Editor-in-Chief, and I look forward to working with a remarkable team to carry on the tradition of the JVME. It begs the obvious to say that we share a common goal of making the JVME highly successful, as can be defined by many conventional publishing metrics. However, our goal must be much broader than success alone. Albert Einstein is famously quoted for his cautionary statement ‘‘Try not to become a man of success, but rather try to become a man of value.’’ Applied to a journal rather than an individual, that statement encapsulates my personal goal for the JVME, a goal I know that you share. It is a pleasure and honor for me to work with you toward the attainment of that goal.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".