Reconsidering the Lecture in Modern Veterinary Education
Bibliographic record
Abstract
Those teaching in the higher-education environment are now increasingly meeting with larger cohorts of students. The result is additional pressure on the resources available and on the teacher and learners. Against this backdrop, discussions and reflections took place between a practitioner, within a UK veterinary school, and an educational researcher with extensive experience in observing teaching in veterinary medicine. The result was an examination of the lecture as a method of teaching to consider how to resolve identified challenges. The focus of much of the literature is on technical aspects of teaching and learning, reverting to a range of tips to resolve particular issues recognized in large-group settings. We suggest that while these tips are useful, they will only take a practitioner so far. To be able to make a genuine connection to learners and help them connect directly to the discipline, we need to take account of the emotional aspects of our role as teachers, without which, delivery of knowledge may be undermined.
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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 | 0.036 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".