Phronesis in Veterinary Medicine: Navigating the Complexity of Practice with Wisdom
Bibliographic record
Abstract
With the knowledge explosion currently occurring in veterinary medicine, it is difficult to impart to our learners all the actions that can be done, let alone teach them how to determine what should be done. Ethics curricula can provide an essential part of this answer but leave it incomplete. This can result in the disengagement of veterinary learners from the situational understanding that leads to the most appropriate actions. Phronesis is a practical understanding with sound judgment and ethical orientation. It has recently become a talking point in medicine as a framework of support for health professionals that brings together the goals of ethical care with clinical judgment. We can work to incorporate it more effectively into our curricula by evaluating how phronesis is already used in veterinary medicine. This will give learners the opportunity to practice phronetic judgment and support practical wisdom in clinical settings.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".