Introducing DVM: DiVersity Matters (An Association of American Veterinary Medical Colleges Initiative)
Bibliographic record
Abstract
Now more than ever, colleges of veterinary medicine (CVMs) are challenged to improve the educational experience, build environments that support long-term student and faculty success, and create a diverse and competitive workforce. Additionally, the nation's fast-evolving racial and ethnic demographics demand that the veterinary medical profession be responsive to the emerging needs of this changing population. In March 2005, during the 15th Iverson Bell Symposium, the Association of American Veterinary Medical Colleges (AAVMC) unveiled its DiVersity Matters (DVM) initiative, designed to bring the CVMs closer to achieving these goals. Several key objectives of the initiative and their possible long-term significance to success of the DiVersity Matters initiative are explored here, and CVMs are encouraged to expand efforts to increase racial and ethnic diversity in academic veterinary medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".