Strategies for Educational Action to Meet Veterinary Medicine’s Role in Biodefense and Public Health
Bibliographic record
Abstract
It is clear that the profession is not well prepared to respond to society's needs in bio-defense and public health. The imperatives that face the veterinary profession, as emphasized by the agenda for action conference deliberations that are reported in this issue of the journal, require action on many fronts, but possibly none more essential than to address how veterinary education needs to change to meet these challenges. Addressing these needs, participants at the agenda for action conference met in groups of 30 to 50 to shape approaches that would address these key questions. The 161 participants were broadly representative of government, private practice, corporate practice, organized veterinary medicine, and academia (Appendix A). Reported here are the results of those deliberations, with each of the seven sections written up by the discussion leader. Included in the participants were 20 students, representative of eight different veterinary colleges, who both participated in the group discussions and have presented their own report.
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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 | 0.056 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 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".