Our quest for creating a space that is welcoming to all: A commentary from the American College of Veterinary Emergency and Critical Care Diversity, Equity, and Inclusion Committee
Bibliographic record
Abstract
Diversity, equity, and inclusion (DEI) are crucial elements of successful veterinary emergency and critical care practices across the world. Embracing the elements of DEI creates a work environment that is safe and welcoming for all the members of the team. The American College of Veterinary Emergency and Critical Care DEI committee was formed to enhance and support efforts to increase racial diversity in veterinary emergency and critical care, as well as provide resources that will generate DEI practices across the country. This article provides an overview of the vision of the committee and some of the steps that have been taken to create a welcoming space for all represented in veterinary emergency and critical care.
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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 | 0.044 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.075 | 0.146 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".