The VetEd Conference: Evolution of an Educational Community of Practice
Bibliographic record
Abstract
The VetEd conference was developed with the aim of growing an educational community by providing an opportunity to share ideas, innovations, research, and best practices in veterinary education in a friendly, affordable, and inclusive environment. The annual conference has been hosted by the veterinary schools in the UK, Ireland, and the Netherlands, becoming the official conference of the Veterinary Schools Council in 2017. The current study investigates the extent to which the development of the conference has contributed to the evolution of a community of practice. The conference proceedings' abstracts were analyzed to identify trends in number, type, and author information. This was complemented by oral histories exploring the impact of VetEd on developing the veterinary education community. The number of abstracts has increased from 40 (2010) to 137 (2018), and these are predominantly posters, with the major themes being technology-enhanced learning, clinical skills, and assessment. The authors have been increasingly international, representing 8 countries in 2010 and 22 in 2018. Nine interviews were undertaken with those involved in organizing VetEd. The inclusivity of the conference and the engagement of a wide variety of delegate groups are key themes that emerged. Concerns emerged around the organizational challenges and the potential for the conference to outgrow the founding principles in the future. VetEd has become a key event in the annual calendar and represents an initiative that has contributed to the ongoing development of the veterinary education community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".