150th Anniversary of Veterinary Education and the Veterinary Profession in North America: Part 4, US Veterinary Colleges in 2011 and the Distribution of their Graduates
Bibliographic record
Abstract
This fourth article in an ongoing series of articles published in the Journal of Veterinary Medical Education on veterinary education and the veterinary profession provides information on the colleges and schools that exist in the US in 2011. This article provides a brief description of the educational programs and recent accreditation of the veterinary schools at Western University of the Health Sciences and Ross University on the Island of St. Kitts. Without taking into consideration Caribbean colleges, the number of veterinary student positions in US colleges has increased by approximately 24% in the past decade. The number of students attending veterinary colleges is unevenly distributed across the country with many of the more populous states having fewer students per 100,000 people than less populous states. The percentage of veterinarians who reside in the state of their alma mater also varies widely with alumni from some colleges remaining in the state of the college from which they graduated (e.g., Texas A&M and the University of California at Davis) and the graduates of other colleges (e.g., Cornell University and the University of Pennsylvania) being more widely distributed across the country. The location of veterinarians is also provided by state and adjusted for population and state size.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".