Institutional Experience with Curricular Renewal during the OIE Veterinary Education Twinning Program between the University of Peradeniya, Sri Lanka, and Massey University, New Zealand
Bibliographic record
Abstract
The Veterinary Education Twinning Program between the University of Peradeniya (UP) and Massey University (MU) was carried out within the mandate of the World Organisation for Animal Health (OIE) to create opportunities for developing countries to establish educational facilities and methods based on current and accepted international standards. This article describes how the twinning partnership between UP and MU enabled a strong flow of expertise that benefited Sri Lanka to develop a new veterinary undergraduate curriculum. The new curriculum was created to improve the relevance and quality of veterinary education, incorporating current international best practices, to strengthen national veterinary services. Adoption of an outcome-based educational framework has allowed the incorporation of tools such as problem-based learning and student-centered pedagogies and assessments. Extending the duration of the program from 4 academic years to 5 has expanded the scope for clinical learning, particularly in terms of student exposure to livestock veterinary services. This article documents the processes followed during the twinning program to highlight those factors that were critical for success or that were found surprising, difficult, or problematic.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".