A Public-Policy Practicum to Address Current Issues in Human, Animal, and Ecosystem Health
Bibliographic record
Abstract
There are recognized needs for cross-training health professionals in human, animal, and ecosystem health and for public health policy to be informed by experts from medical, science, and social science disciplines. Faculty members of the Community Health and Preventive Medicine Section at the University of Illinois at Urbana-Champaign, College of Veterinary Medicine, and the Institute of Government and Public Affairs, University of Illinois at Urbana-Champaign, have offered a public-policy course designed to meet those needs. The course was designed as a practicum to teach students the policy-making process through the development of policy proposals and to instruct students on how to effectively present accurate scientific, demographic, and statistical information to policy makers and to the public. All students substantially met the learning objectives of the course. This course represents another model that can be implemented to help students learn about complex, multifactorial issues that affect the health of humans, animals, and ecosystems, while promoting participation in public health policy development.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".