Why and How Political Science Can Contribute to Public Health? Proposals for Collaborative Research Avenues
Bibliographic record
Abstract
Written by a group of political science researchers, this commentary focuses on the contributions of political science to public health and proposes research avenues to increase those contributions. Despite progress, the links between researchers from these two fields develop only slowly. Divergences between the approach of political science to public policy and the expectations that public health can have about the role of political science, are often seen as an obstacle to collaboration between experts in these two areas. Thus, promising and practical research avenues are proposed along with strategies to strengthen and develop them. Considering the interdisciplinary and intersectoral nature of population health, it is important to create a critical mass of researchers interested in the health of populations and in healthy public policy that can thrive working at the junction of political science and public health.
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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 | 0.332 | 0.279 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.084 |
| Scholarly communication | 0.046 | 0.082 |
| Open science | 0.011 | 0.035 |
| Research integrity | 0.073 | 0.049 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".