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Record W2272585968 · doi:10.3138/jvme.0615-089r

Association of American Veterinary Medical Colleges (AAVMC): 50 Years of History and Service

2015· article· en· W2272585968 on OpenAlexvenueaboutno aff
Andrew T. Maccabe, Lester M. Crawford, Lawrence E. Heider, Billy E. Hooper, Curt J. Mann, Marguerite Pappaioanou

Bibliographic record

VenueJournal of Veterinary Medical Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationChampionService (business)WorkforceVeterinary medicineDiversity (politics)MedicinePolitical scienceMedical educationBusinessLaw

Abstract

fetched live from OpenAlex

The mission of the Association of American Veterinary Medical Colleges (AAVMC) is to advance the quality of academic veterinary medicine. Founded in 1966 by the 18 US colleges of veterinary medicine and 3 Canadian colleges of veterinary medicine then in existence, the AAVMC is celebrating 50 years of public service. Initially, the AAVMC comprised the Council of Deans, the Council of Educators, and the Council of Chairs. In 1984, the tri-cameral structure was abandoned and a new governing structure with a board of directors was created. In 1997, the AAVMC was incorporated in Washington, DC and a common application service was created. Matters such as workforce issues and the cost of veterinary medical education have persisted for decades. The AAVMC is a champion of diversity in the veterinary profession and a strong advocate for One Health. The AAVMC has adopted a global perspective as more international colleges of veterinary medicine have earned COE accreditation and become members.

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)0.009
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0080.004
Scholarly communication0.0090.005
Open science0.0010.008
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0310.009

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 head0.323
GPT teacher head0.514
Teacher spread0.191 · 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
GenreOther

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

Citations3
Published2015
Admission routes2
Has abstractyes

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