Conclusion: The Added Value of Political Science in, of, and with Public Health
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has brought into never-before-seen sharp focus the challenges at the interface between health and public policy. To address these challenges, epistemic trespassing is required and, more precisely, engagement between public health and political science. This book highlights the theoretical and conceptual underpinnings of public health political science, explores the empirical contributions, and calls for deeper engagement between public health and political science. Not surprisingly, challenges remain: the need to unite, both spatially and conceptually, the global network of colleagues at this interface and expand it to include perspectives from the Global South and from places where democratic institutions are truncated if not completely absent; the need to promote more cross-disciplinary teaching, training, and research in public health and political science; and engagement with the full range of political science sub-disciplines beyond those highlighted in this volume. Finally, there is a need to leave the ivory towers of academe (whether political science or public health) and more proactively engage with policymaking efforts if we are to not simply make a point but make a difference.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".