Broadening the Public Perception of Veterinarians: Can We Keep What We've Got While Transforming Ourselves?
Bibliographic record
Abstract
Before becoming Dean, I thought I had a special insight: I would like to broaden the public’s perception of veterinary medicine, to get media attention focused on the roles veterinarians play, including support for research and agriculture. In early January, when I first attended the annual deans’ meeting of the Association of American Veterinary Medical Colleges (AAVMC), I found that this is the explicit goal of the deans, and, indeed, of the AAVMC as an organization. Appropriately humbled to discover that my deep insight was old news, I was also happy to see that the profession is widely engaged in increasing our influence. The deans believe that—in our position within the human–animal bond that includes all caretakers and users of domestic animals for research, agriculture, work, and entertainment—we are the most sensitive detectors of emerging infectious diseases and of some vitally important societal changes in the relationship between man and beast. We are therefore frustrated to be omitted from high-profile media coverage and, more importantly, from federal bodies focused on the future of research and management of infectious-disease and bioterrorism threats. But who must do something about this, ‘‘them’’ or ‘‘us’’?
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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".