Bibliographic record
Abstract
In previous issues of the Journal of Veterinary Medical Education, wide-ranging insights on how to achieve excellence in the classroom have been framed by award-winning teachers. These recipes for educational success, however, invariably lack a key ingredient-the teacher's process of self-renewal. What skills and attitudes prime the teacher for continued high performance? To stay out of the ruts of expertise, where does the teacher turn? Teachers and administrators alike recognize its great importance, yet few opportunities for the renewal of teachers are built into the educational system. In this article, we challenge teachers to see their own self-renewal as an underutilized approach to innovate education. We propose a schema for sustained self-renewal: each educator developing her own personalized, hand-picked gallery of intellectual heroes who in turn serve as the educator's life-long teachers. To illustrate the value of this activity, we introduce our own collection of 10 gifted thinkers, providing a brief encounter with each sage as a way of stimulating new thinking on the skills and attitudes that promote personal growth and transformative teaching. We conclude that the veterinary profession should work to create better opportunities for the self-renewal of teachers. By envisioning even our best teachers as unfinished and under construction, we open up a new dialogue situating the self-renewal of teachers at the very core of educational excellence.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".