Bibliographic record
Abstract
This article examines the meaning of federalism for health care financing (HCF) and is based on two considerations. First, federal institutions are embedded in their national context and interact with them. The design and performance of HCF policy will be influenced by contexts, the workings of the federal institutions, and the interactions of these institutions with different elements of the context. This article unravels these influences. Second, there is no unique model of federalism, and so we have to specify the particular form to which we refer. The examination of the influence of federalism and its context on HCF policy is facilitated by using a transnational comparative approach, and this article examines four mature federations: the United States, Australia, Canada, and Germany. The relatively poor performance of the U.S. HCF system seems associated with the fact that it operates in a context markedly less benign than those of the other national HCF systems. Heterogeneity of context appears also to have contributed to important differences between the United States and the other countries in the design of HCF policies. An analysis of how federalism works in practice suggests that, while U.S. federalism may be overall less favorable to the development of well-functioning HCF policies, the inferior performance of these policies is to be principally attributed to context.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".