Health policy and territorial politics: disciplinary misunderstandings and directions for research
Bibliographic record
Abstract
The territorial politics of health is both underexploited by mainstream political scientists and the subject of a large and distinctive health policy literature that rarely connects with political science. This chapter first argues for the usefulness of health as a source of data for a more grounded and policy-focused territorial politics. It then summarizes the health policy approach to territorial politics, arguing that its empirical findings, more than its theories, can enrich political science on the topic. Subsequently, it turns to the findings of political scientists, highlighting the extent to which comparative welfare state literature is skeptical about federalism and could handle it much better, and the extent to which the literature about federalism and health is mostly nationally specific and over-represents North American experiences. The last sections turn to some findings for comparative territorial politics from health policy studies, and some potential future directions for research.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".