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Record W2094768337 · doi:10.3138/jvme.37.2.207

Fulbright Scholar International Teaching and Research Opportunities for Veterinary Faculty

2010· article· en· W2094768337 on OpenAlexvenueno aff
Mushtaq A. Memon, Gary L. Garrison, Ted Y. Mashima, Mike Chaddock

Bibliographic record

VenueJournal of Veterinary Medical Education · 2010
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumInternationalizationGovernment (linguistics)Political sciencePandemicCoronavirus disease 2019 (COVID-19)Medical educationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Library scienceMedicineBusiness

Abstract

fetched live from OpenAlex

The Fulbright program was established by the US Congress to "enable the government of the United States to increase mutual understanding between the people of the United States and the people of other countries." The Core Fulbright Scholar Program sends more than 800 US faculty and administrators to 125 countries to lecture or conduct research around the world each year. Unfortunately, only 28 faculty members from the US veterinary colleges have used Fulbright Scholar opportunities in the last 20 years (1989-2009). Considering recent worldwide events, such as the global dispersion of the Asian strain of highly pathogenic avian influenza and pandemic H1N1 2009 affecting human and animal species, the importance of awareness and education of veterinarians to such global issues is obviously urgent. Therefore, Fulbright scholarships represent an important opportunity to gain experience and bring this time-critical information back to fellow faculty and students. Veterinarians who wish to contribute to internationalization of the curricula and their campuses should consider applying for Fulbright Scholar support to launch their career in this pivotal direction. For details about the Fulbright Scholar Program, eligibility, and application procedures, please visit <http://www.cies.org/us_scholars/>.

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)0.011
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.776
GPT teacher head0.659
Teacher spread0.117 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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