What is Knowledge Exchange for Educators and Students? A Framework Based on Findings from a Literature Search and Veterinary Education Conference Workshop
Bibliographic record
Abstract
There has been growing interest in knowledge exchange (KE) activities as a result of recent calls for higher education establishments in the UK to provide more evidence of how they serve society for the benefit of the economy, the public, and the community. KE has been defined as "a collaborative, creative endeavor that translates knowledge and research into impact in society and the economy," where this exchange takes the form of sharing knowledge, experience, ideas, evidence, or expertise. While well established in the context of research, it is less clear what KE activities are in the context of teaching. The aim of this project was to use a collaborative approach to identify types of KE activity relevant for veterinary educators and undergraduate students (pre-veterinary registration) and ways of measuring these activities. Initially, a literature search identified four main overarching categories of interactions that KE activities for veterinary educators and undergraduate students could be assigned to: people-based activities, problem-solving activities, commercialization activities, and community activities. Second, a workshop with members of the wider veterinary education community evaluated these lists of activities and discussed how the impact of these could be measured. The lists generated provide a starting point for understanding how educators and undergraduate students can maximize their impact in relation to KE activities. It is expected that over time these will be built upon to represent the breadth of current and future activities undertaken in the clinical sciences. While the focus is on veterinary education, this framework can be applied to reviewing KE in a range of health care and client-facing disciplines.
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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 | 0.137 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.050 | 0.037 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.050 | 0.063 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".