A Historical Overview of African American Veterinarians in the United States: 1889–2000
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".