Racial, Cultural, and Ethnic Diversity within US Veterinary Colleges
Bibliographic record
Abstract
A comprehensive survey containing 30 questions regarding racial, cultural, and ethnic issues was sent electronically to each of the member colleges within the Association of American Veterinary Colleges (AAVMC) during 2005. Responses were received from 25 of the 28 veterinary colleges in the United States and two foreign colleges. Most colleges had more than one respondent complete the survey. Since the respondents were not identified and were not uniform in regards to position within each college, some responses might have reflected the individual respondent's views rather than the college's actual situation or philosophy. The information gained from this survey demonstrates strong trends in attitudes to and practices with respect to diversity in US veterinary colleges. Three major areas were addressed in the survey-college and university environment and cultures, faculty and curriculum, and recruitment and retention of veterinary students from underrepresented minorities. In many instances, the survey confirmed a lack of knowledge about diversity issues at the respondents' institutions. These survey results will serve as a benchmark for gauging changes in the profession's racial, cultural, and ethnic demographics in the future and as a foundation upon which to build effective diversity programs.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".