Where do deans of veterinary medicine in the United States and Canada come from?
Bibliographic record
Abstract
Deans use passion, integrative thinking, communication skills, charisma, and other leadership skills to build collaborations to advance academic innovation, promote societal awareness of veterinary medicine, and enhance diversity and inclusion. This study analyzed the educational and experiential backgrounds as well as the ethnicity and gender of veterinary medical college deans in the United States and Canada. Data were collected on the deans using public sources from 1966 when the Association of American Veterinary Medical Colleges was started, until 2018. It was found that the advent of specialty colleges led to more visibility of clinical credentials of deans; about 17% of the deans were pathologists, and few deans came from basic science disciplines. The data show that an overwhelming majority of deans have been Caucasian male and very few were racialized/non-Caucasian academics. There are growing numbers of women veterinarians becoming deans. These data may provide some insights on how to assemble leadership training programs to create a more diverse pool of academic veterinary leaders so that more women and ethnic minorities can aspire to become deans.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".