Anniversary Narratives of the Health Care State: Institutional Entrenchment in Retrospect
Bibliographic record
Abstract
Institutional narratives, appealing both to the intellect and the imagination, are powerful mechanisms of entrenchment. Drawing on close examination of legislative debates, interview transcripts, and official documents, this article analyzes institutional narratives of the British National Health Service (NHS) and American Medicare and Medicaid. These narratives take the form of epics, featuring founding heroes, adversaries, stewards, saviors, and other characters, and are retold on multiple occasions, and especially on anniversaries of the founding date. In the process, certain elements of history are remembered, and others forgotten. The myth of the NHS as a single national institution obscured much of the complexity and compromise that went into its founding and subsequent development, but preserved fidelity to its founding principles. In the United States, the dominant narrative belonged to Medicare, while Medicaid featured as an afterthought. In the case of the NHS, narrative entrenchment served to preserve universal access to comprehensive health care. In the case of American Medicare, entrenchment preserved the original mission of the institution but kept it from expanding to a broader swath of the population, even as its less-entrenched companion Medicaid provided a vehicle for coverage of an increasingly wide range of population groups. A distinct Medicaid narrative developed only after incremental expansion was well underway.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.045 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".