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Record W2007243013 · doi:10.3138/jvme.33.4.479

Veterinary Medical Education for Modern Food Systems: Setting a Vision and Creating a Strategic Plan for Veterinary Medical Education to Meet Its Responsibilities

2006· article· en· W2007243013 on OpenAlexvenueno aff
Daryl D. Buss, Bennie I. Osburn, Norman Willis, Donal A. Walsh

Bibliographic record

VenueJournal of Veterinary Medical Education · 2006
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVeterinary educationPlan (archaeology)Strategic planningMedical educationVeterinary medicineMedicineBusinessCurriculumPsychologyMarketingPedagogyBiology

Abstract

fetched live from OpenAlex

BACKGROUND Veterinary medicine had its origins in the supply of food. Nurturing the health of animals that are a source of food has been a central constituent of its professional responsibilities since its ancestral inception. The industrialization of civilizations gave veterinary medicine an expanded focus as supplying food of animal origin moved from being just part of the activities of individual small producers to larger and larger productionunitsthatareoftendistant fromlargepopulationcenters. Three major factors have arisen within the last several decades. Their dynamics now dominate the decisions of veterinary medicine as a whole and of food-systems veterinary medicine in particular: 1. Those now living within the increasingly urbanized communities have lost their connection with their formerly close neighbors who produce their food. 2. Increasing competition and technological advances have stimulated the development of today’s modern, often highly integrated, animal-food industry, with its ever-increasing demands for efficiency and cost-effective production. This has changed the entire set of dynamics between food-supply animal owner, food-supply processor and distributor, food-system veterinarian, and consumer.

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)0.007
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.330
GPT teacher head0.535
Teacher spread0.205 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

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