New approaches to teaching the art and science of veterinary medicine
Bibliographic record
Abstract
WCVM instructors were selected from cross-campus nominees to win all 4 categories, while a fifth faculty member earned a college-specific teaching award from USask.These honors demonstrate the caliber of teaching at the WCVM, which has been Western Canada's center of veterinary education, clinical expertise, and research since 1965.The college has had many gifted teachers during its history, and the next generation continues to introduce novel teaching approaches.One catalyst for change is the shift to competencybased veterinary education for the Doctor of Veterinary Medicine program.Another factor is the college's collaboration with the USask Gwenna Moss Centre for Teaching and Learning.The COVID-19 pandemic has also motivated creativity in online instruction for WCVM students. Art-inspired teaching-WCVM associate professor Dr.Nicole Fernandez trained in visual design before becoming a veterinary pathologist.Using her background, she recently worked with colleagues at the WCVM and University of Calgary to introduce veterinary students to observation and description-skills that are especially critical in clinical pathology.The result is a clinical skills lab during which veterinary students study works of art at a Saskatoon gallery and then practice describing these art pieces in group discussions.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 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".