Policy feedback, varieties of federalism, and the politics of health‐care funding in the United States, Mexico, and Canada
Bibliographic record
Abstract
Abstract Long before the COVID‐19 pandemic, health‐care spending became a key policy issue across the OECD. Comparing recent institutional trends in public health‐care financing in three federal countries—Canada, the United States, and Mexico—this article explores the political struggles over fiscal federalism in health care related to both vertical and horizontal imbalances that can generate regional tensions and pit subnational governments against one another and, especially, against the national government. Grounded in a historical institutionalist perspective, the article shows how different institutional legacies shape current conflicts over federal health‐care funding in these three highly dissimilar federal countries. Such an analysis stresses the role of policy feedback in social politics and the enduring “varieties of federalism” in North American public health‐care funding. More specifically, the article shows how the distinct nature of policy feedback effects in the United States, Mexico, and Canada helps explain why the two former countries recently witnessed major changes in intergovernmental relations over fiscal federalism for health care while the latter did not. Related Articles Bode, Ingo, and Jorge E. Culebro Moreno. 2018. “Paradoxical Internationalization: Regulatory Reforms in the Mexican Health‐Care System through the Lens of European Experience.” Politics & Policy 46(4): 678–710. https://doi.org/10.1111/polp.12268 . Lauby, Fanny, and Christopher J. McKinley. 2023. “Factors of Early COVID‐19 Prevention Policy Engagement in France and the United States.” Politics & Policy 51(5): 830–48. https://doi.org/10.1111/polp.12556 . Luccisano, Lucy, and Amy Romagnoli. 2007. “Comparing Public Social Provision and Citizenship in the United States, Canada, and Mexico: Are There Implications for a North American Space?” Politics & Policy 35(4): 716–51. https://doi.org/10.1111/j.1747‐1346.2007.00082.x .
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".