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Record W2041358795 · doi:10.3138/jvme.30.4.358

International Cooperation in Veterinary Public Health Curricula Using Web-Based Distance Interactive Education

2003· article· en· W2041358795 on OpenAlexvenueno aff
Len J.A. Lipman, Valérie M. Barnier, Katalin K. de Balogh

Bibliographic record

VenueJournal of Veterinary Medical Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumVeterinary public healthThe InternetPublic healthInformation and Communications TechnologyMedical educationPublic relationsVeterinary medicinePolitical scienceMedicineSociologyPedagogyComputer scienceNursingWorld Wide Web

Abstract

fetched live from OpenAlex

The expanding field of Veterinary Public Health places new demands on the knowledge and skills of veterinarians. Veterinary curricula must therefore adapt to this new profile. Through the introduction of case studies dealing with up-to-date issues, students are being trained to solve (real-life) problems and come up with realistic solutions. At the Department of Public Health and Food Safety of the Veterinary Faculty at the University of Utrecht in the Netherlands, positive experiences have resulted from the new opportunities offered by the use of information and communication technology (ICT) in education. The possibility of creating a virtual classroom on the Internet through the use of WebCT software has enabled teachers and students to tackle emerging issues by working together with students in other countries and across disciplines. This article presents some of these experiences, through which international exchange of ideas and realities were stimulated, in addition to consolidating relations between universities in different countries. Long-distance education methodologies provide an important tool to achieve the increasing need for international cooperation in Veterinary Public Health curricula.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.462
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2003
Admission routes1
Has abstractyes

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