A Collaborative Response to the COVID-19 Challenge: Developing an International Platform for Sharing E-learning Materials for Veterinary Education
Bibliographic record
Abstract
When the COVID-19 pandemic swept through Europe in 2020, veterinary educational institutions faced new challenges overnight: distance learning became imperative, and teachers were forced to develop e-learning material on the fly. As a response to the unfortunate situation, veterinary faculties at three European universities (Utrecht, Copenhagen, Helsinki) applied for and received an Erasmus+ grant to develop an international platform for sharing veterinary e-learning material. Technical and administrative challenges caused a slow start. This added to the already limited timeframe and demonstrated the obstacles involved in trying to fuse organizational, legal, digital, educational, and cultural systems across national borders. Still, within the 2-year grant period, the partners managed to establish a platform for sharing veterinary e-learning materials among veterinary schools in Europe and eventually beyond. Furthermore, a website was designed for the project, as well as a Teachers' Forum, and relevant guidelines for up- and downloading and for the creation of new e-learning material. Privacy and copyright regulations were incorporated in a consent form to be accepted before uploading material. In order to disseminate the project, three webinars were held for colleagues at European veterinary schools. The current and additional papers as well as abstracts will make the project visible and subsequently available to the veterinary community. At present, 61 teachers have registered with the Veterinary Online Collection. Hopefully, a growing community of veterinary educators will become interested in sharing teaching material and experiences across national borders, thus facilitating veterinary teaching in general and during future lock-downs in particular.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".