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Record W4297989458 · doi:10.1215/03616878-10234212

Anniversary Narratives of the Health Care State: Institutional Entrenchment in Retrospect

2022· article· en· W4297989458 on OpenAlexaff
Carolyn Hughes Tuohy

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNarrativeState (computer science)Political scienceHealth careArtLiteratureLawComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.322
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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