The Affordable Care Act in the States: Fragmented Politics, Unstable Policy
Bibliographic record
Abstract
Many argue that the frustrated implementation of the 2010 Affordable Care Act (ACA) stems from the unprecedented level of political polarization that has surrounded the legislation. This article draws attention to the law's "institutional DNA" as a source of political struggle in the 50 states. As designed, in the context of US federalism, the law fractured authority in ways that has opened up the possibility of contestation and confusion. The successful implementation of the ACA varies not only across state lines but also across the various components of the law. In particular, opponents of the ACA have experienced their greatest successes when they could take advantage of weak preexisting policy legacies, high levels of institutional fragmentation, and negative public sentiments. As argued in this article, the fragmented patterns of health care politics in the 50 states identified in previous research have largely persisted during the Trump administration. Moreover, while Republicans were unsuccessful at repealing the legislation, the administration has taken advantage of its structural deficiencies to further weaken the legislation's capacity to expand access to affordable, quality health insurance.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".