Education and the Food-Systems Veterinarian: The Impact of New Information Technologies
Bibliographic record
Abstract
Food systems educators face a double challenge: (1) the inherent change in scope and perspective from raising animals to producing safe food in an environmentally conscientious manner; and (2) the unprecedented demand for higher education, both nationally and internationally. In the modern world, small numbers of producers are capable of feeding a growing population. As demographics have shifted from rural to urban areas, more global livelihoods are derived from manufacturing and services than from agriculture. Education, as one of those services, is accounting for an increasing percentage of world trade, through the physical translocation of students and, more recently, through online education. Within the veterinary realm, colleges outside the United States seek accreditation to better compete for students, and there is increasing pressure from private schools. Today's food systems require a high level of veterinary expertise, with specialization in a particular production system as well as the ability to contribute as part of a larger team that can address economic, bio-security, biological-waste, animal-welfare, food-safety, and public-health concerns. The need for different expertise from food systems specialists (indeed, shortages of all types of veterinary specialists), combined with global competition in education, is a call to action for the veterinary profession. This is an opportunity to revisit and reorganize the delivery of veterinary education, making use of new collaborative technologies for greater efficiency and effectiveness.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".