Driving Success over the Past 50 Years—The Faculty in Academic Veterinary Medicine
Bibliographic record
Abstract
The faculty at member schools and colleges of the Association of American Veterinary Medical Colleges (AAVMC) are critical for continued progress in veterinary medicine. The success of those faculty members over the past 50 years has positioned veterinary medicine to engage an ever-widening array of opportunities, responsibilities, and societal needs. Yet the array of skills and accomplishments of faculty in academic veterinary medicine are not always visible to the public, or even within our profession. The quality and the wide range of their scholarship are reflected, in part, through the according of national and international awards and honors from organizations relevant to their particular areas of expertise. The goal of this study was to illustrate the breadth of expertise and the quality of the faculty at 34 schools/colleges of veterinary medicine by examining the diversity of organizations that have recognized excellence in faculty achievements through a variety of awards.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".