Beyond the usual suspects: using political science to enhance public health policy making
Bibliographic record
Abstract
That public health policy and practice should be evidence based is a seemingly uncontroversial claim. Yet governments and citizens routinely reject the best available evidence and prefer policies that reflect other considerations and concerns. The most common explanations of this paradox emphasise scientific disagreement, the power of 'politics', or the belief that scientists and policymakers live in two separate communities that do not communicate. However, another explanation may lie in the limits of the very notion of evidence-based policy making. In fact, the social science discipline of political science offers a rich body of theory and empirical evidence to explain the apparent gap between evidence and policy. This essay introduces this literature with a particular emphasis on a recent book by Katherine Smith, Beyond evidence-based policy in public health: the interplay of ideas. As the title suggests, Smith argues that what matters for public health policy is less scientific evidence and much more a more complex set of ideas. Based on detailed case studies of UK tobacco and health inequality policy, Smith offers a richly textured alternative account of what matters for policy making. This excellent book is part of a small but growing body of political science research on public health policy that draws on contemporary theories of policy change and governance more generally. This essay provides a window on this research, describes some examples, but emphasises that public health scholars and practitioners too often retain a narrow if not naive view of the policy-making process.
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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 | 0.102 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.086 |
| Scholarly communication | 0.040 | 0.043 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 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".