Policy Feedback and the Politics of the Affordable Care Act
Bibliographic record
Abstract
There is a large body of literature devoted to how “policies create politics” and how feedback effects from existing policy legacies shape potential reforms in a particular area. Although much of this literature focuses on self‐reinforcing feedback effects that increase support for existing policies over time, Kent Weaver and his colleagues have recently drawn our attention to self‐undermining effects that can gradually weaken support for such policies. The following contribution explores both self‐reinforcing and self‐undermining policy feedback in relationship to the Affordable Care Act, the most important health‐care reform enacted in the United States since the mid‐1960s. More specifically, the paper draws on the concept of policy feedback to reflect on the political fate of the ACA since its adoption in 2010. We argue that, due in part to its sheer complexity and fragmentation, the ACA generates both self‐reinforcing and self‐undermining feedback effects that, depending of the aspect of the legislation at hand, can either facilitate or impede conservative retrenchment and restructuring. Simultaneously, through a discussion of partisan effects that shape Republican behavior in Congress, we acknowledge the limits of policy feedback in the explanation of policy stability and change.
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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".