Welfare State Eras, Policy Narratives, and the Role of Expertise: The Case of the Affordable Care Act in Historical and Comparative Perspective
Bibliographic record
Abstract
Abstract This article presents a way of understanding the linkage of expert and public opinion through a focus on policy narratives, which serve as deliberately crafted rhetorical bridges between expert discourses and broader cultural experiences. Across advanced nations, the dynamics of this bridging function has differed in different phases of policy development (such as welfare-state establishment, retrenchment, and redesign), depending on the state of discourse in each realm. In the establishment phase in which most programs of universal health care coverage were adopted, expert discourses were relatively synchronized with but subservient to broader policy narratives about collective and individual rights and responsibilities. The United States, in contrast, pursued its final sprint toward universal coverage in a later phase, in which the policy analysis community had greatly expanded and expert discourses had evolved to focus on specialized issues of system redesign. The resulting highly complex technical design did not readily align with an epic narrative of public purpose. Advocates instead relied principally on two narrative lines: an aggregation of anecdotes that was vulnerable to the simpler opposition narrative of an overweening state, and a crusade narrative that met the opposing narrative of patriotic resistance on its own terms but could not allay partisan polarization.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.082 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".