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Record W2014116359 · doi:10.3138/jvme.31.2.100

Accreditation of Veterinary Schools in Australia and New Zealand

2004· article· en· W2014116359 on OpenAlexvenueno aff
John Craven, Julie Strous

Bibliographic record

VenueJournal of Veterinary Medical Education · 2004
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationVeterinary medicineVeterinary parasitologyMedical educationVeterinary educationMedicinePolitical scienceCurriculumLaw

Abstract

fetched live from OpenAlex

Veterinary schools in Australia and New Zealand are assessed for accreditation purposes every six years by the Veterinary Schools Accreditation Advisory Committee (VSAAC), which is a standing committee of the Australasian Veterinary Boards Council (AVBC).1 Prior to undertaking an assessment, VSAAC requests a Self Evaluation Report from the school and subsequently spends a week on site to collect additional information. The committee also takes into consideration other quality assurance procedures within the university and aims for a process that complements other evaluation activities. Internal evaluation procedures within VSAAC are designed to reflect the process and outcomes of each visit and lead to annual revisions of the publication Policies, Procedures and Guidelines publication. The committee has close links with the Royal College of Veterinary Surgeons (RCVS), and there is a routine exchange of observers on all visits in the United Kingdom and Australasia. In recent years VSAAC has become increasingly interested in looking at ways to place greater emphasis on the outcomes of veterinary education and, eventually, to reduce our reliance on input measures. There has been good progress in identifying desirable attributes for veterinary graduates, but further work is needed to establish the reliability of assessment procedures. The Australasian accreditation system is very supportive of recent moves to achieve greater compatibility of veterinary accreditation systems in different parts of the world because we believe it has the potential to assist globalization of animal disease control and veterinary 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)0.017
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.469
GPT teacher head0.574
Teacher spread0.105 · 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 designObservational
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

Citations6
Published2004
Admission routes1
Has abstractyes

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