An Investigation of Veterinary College Websites in the US and Canada: Representations and Content of Equity, Diversity, and Inclusion
Bibliographic record
Abstract
University websites are often a first point of contact for prospective students. Portrayals of diversity on the website can influence current and future students' perceptions. Using content and statistical analysis, all 38 veterinary college websites in Canada and the US were systematically coded for representations of people in photos and content related to equity, diversity, and inclusion (EDI). In both Canada and the United States, people perceived as male or Black, Indigenous, and/or People of Color (BIPOC) were more likely to be portrayed without animals, and fewer photos with males depicted engagement in an activity compared with those depicting females. Compared with the self-identified data reported by US colleges to the American Association of Veterinary Medical Colleges, BIPOC students were under-depicted by approximately 16.3% (95% confidence interval [CI] = 11.1%-21.7%) on some college websites and over-depicted on others by approximately 23.4% (95% CI = 15.4%-31.2%). A land acknowledgment or a link for a land acknowledgment was found on only six websites (16%). These results provide evidence and support that veterinary colleges should monitor their websites for depictions of people and content related to EDI, providing the opportunity to attract a diverse student audience.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".