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Record W2165858390 · doi:10.3138/jvme.31.4.409

A Historical Overview of African American Veterinarians in the United States: 1889–2000

2004· article· en· W2165858390 on OpenAlexvenueaboutno aff
E. W. Adams

Bibliographic record

VenueJournal of Veterinary Medical Education · 2004
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceEthnic groupDiversity (politics)PaceInclusion (mineral)MulticulturalismMedicineUnderrepresented MinorityVeterinary medicinePolitical scienceMedical educationSociologySocial scienceLawGeography

Abstract

fetched live from OpenAlex

The annals of veterinary medical history rarely mention the presence of African American veterinarians and other minorities. Between 1889 and 1948, records show, a meager 70 African Americans graduated from veterinary schools in the United States and Canada. It was not until the veterinary school at Tuskegee (Institute) University was established in 1945 that a significant increase in the number of African American veterinarians occurred in the United States, and over the ensuing years their participation in every facet of the profession has been striking. Their employment in various areas of the profession and their successful performance in the workforce have done much to dispel stereotypical perceptions about minorities. Despite demographic data indicating that the United States is moving rapidly toward a multicultural society, recruitment programs to increase the number of African American students and faculty at the 27 US veterinary colleges have not kept pace with the declared goals of ethnic diversity. If the needs of a changing culture are to be met, veterinary medical education must look toward more ethnic inclusion in the student body and faculty. To that end, the Iverson C. Bell Symposium has consistently advocated the adoption of new and creative methods for increasing minority student enrollment and expanding faculty opportunities in the nation's veterinary colleges.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.432
GPT teacher head0.544
Teacher spread0.113 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2004
Admission routes2
Has abstractyes

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