Preparing the Veterinary Profession for Corporate and Trade Issues in the Americas: Proceedings of a Conference on Synergism and Globalization, Santiago, Chile, May 6–8, 2001
Bibliographic record
Abstract
The complex and rapid-paced development of international trade, coupled with increasing societal demands for the production not only of abundant and inexpensive food, but also of food that is safe and has been raised in a humane and environmentally friendly manner, demands immediate attention from the veterinary community. The new culture of global trade agreements, spurred by the development of the WTO, dictates massive changes and increasing integration of public and private sectors. This is a huge growth area for our profession and will require individuals with a skill set we do not yet provide in our educational framework. In North America, veterinary education is parochial and focused on specialization. This strong orientation toward companion animals fails to provide adequate training for those interested in acquiring the necessary skills for the emerging area of globalization and trade. In South America, curricula are less harmonized with one another and there is tremendous variation in degree programs, rendering it difficult to ascertain whether veterinarians are prepared to assume decision-making responsibilities regarding international transport of food. If we do not begin to prepare our graduates adequately for this emerging market demand, the positions will be filled by other professions. These other professions lack broad-based scientific knowledge about animal physiology and disease causation. Decisions made without adequate background could have devastating consequences for society, including incursions of unwelcome diseases, food safety problems, and public health issues. To prepare our new veterinary graduates for the future and this emerging market, it is important to nurture a global mindset within our academic communities and to promote communications, languages, and an interdependent team mentality. Areas of technical expertise that need a place, perhaps a parallel track, in the curriculum include production medicine, public health, food safety, and international veterinary medicine. The major trade corridors of the future regarding animal-based protein flow between North and South America. It is absolutely essential that we find areas of consensus and deficiencies so that we can harmonize our trade agreements and ensure adequate flow of safe food products from one continent to the other.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".