MétaCan
Menu
Back to cohort
Record W2114518724 · doi:10.3138/jvme.29.4.216

Internationalizing Veterinary Education in the 21<sup>st</sup> Century

2002· article· en· W2114518724 on OpenAlexvenueaboutno aff
Christine Jost, Mushtaq A. Memon

Bibliographic record

VenueJournal of Veterinary Medical Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumEndowmentInternationalizationVeterinary educationMedical educationVeterinary medicineInternational educationPolitical scienceMedicineHigher educationBusiness

Abstract

fetched live from OpenAlex

This paper presents the results of a survey conducted in the spring of 2001 to assess international activities at colleges of veterinary medicine in North America. A questionnaire was sent to all 31 colleges of veterinary medicine in the United States and Canada, of which 22 responded. Of those schools responding to the survey, 86% have International Veterinary Medicine (IVM) programs and most have faculty involved in internationally oriented research (95%), in teaching IVM (74%), in mentoring veterinary students in IVM (84%), and in international consultancies (84%). Funding sources for faculty international activities include foundations, intramural funds, curriculum development grants, endowment/development funds, and sabbaticals. Foreign animal diseases are the most commonly taught international topic. The increasing importance of international veterinary issues is leading to the internationalization of the veterinary education in North America. Most IVM programs include activities of both faculty and students. Greater collaboration between faculty and programs across schools would allow schools to benefit from each other's strengths in IVM education.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.363
GPT teacher head0.536
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Veterinary Medical EducationSame topicVeterinary Practice and Education StudiesFrench-language works237,207