Gazing Into the Crystal Ball: Where Should the Veterinary Profession Go Next?
Bibliographic record
Abstract
The Foresight Report, published in 2007, provided a vision for the future of the veterinary profession. Many of the directions set in the report have already been remarkably accurate, but fiscal constraints stemming from the global recession that began in 2008 will have a significant impact on academia and on the future of the veterinary profession. Three primary forces will shape veterinary education in the coming decade: (1) the urgent need to lead an integrated approach to animal, human, and environmental health; (2) the continued information explosion; and (3) the challenge of delivering high-quality veterinary clinical training at a time of fiscal restraint. Despite economic woes and financial pressure, this is the time to rethink veterinary medical education. This article outlines these challenges and suggests ways to continue to evolve veterinary education.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".