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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".