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Record W2027915772 · doi:10.3138/jvme.37.2.118

A Century of Veterinary Education in Cuba (1907–2007)

2010· article· en· W2027915772 on OpenAlexvenueno aff
Elpidio Gonzalo Chamizo Pestana, José Manuel Aparicio Medina, Alexander López Padrón, Francisco Lam Romero, Regina Schoenfeld‐Tacher

Bibliographic record

VenueJournal of Veterinary Medical Education · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumVeterinary educationVeterinary medicineMedical educationLivestockPoliticsCurriculum developmentMedicinePolitical scienceSociologyPedagogyGeography

Abstract

fetched live from OpenAlex

The development of veterinary education in Cuba has closely mirrored the political changes the nation has undergone. Veterinary studies in Cuba began in 1907, with an emphasis on clinical (individual-animal) medicine. Over time, the professional curriculum has evolved to meet the needs of the nation. Preventive medicine topics were added to the curriculum in 1959. Food-animal production was taught by a separate college until 1990. In 1991, these topics were incorporated into the professional veterinary medical curriculum, and they continue to be an area of emphasis. All veterinary colleges in Cuba follow a centrally organized, student-centered curriculum. A substantial portion of instruction is delivered at educational units, housed on livestock operations, where students participate in extensive field experiences while receiving didactic instruction. The amount of instructional time devoted to hands-on activities increases as students progress through the five-year curriculum.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.256
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.052
GPT teacher head0.355
Teacher spread0.303 · 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 designQualitative
Domainnot available
GenreReview

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

Citations0
Published2010
Admission routes1
Has abstractyes

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