The Political Effects of Policy Drift: Policy Stalemate and American Political Development
Bibliographic record
Abstract
In recent years, scholars have made major progress in understanding the dynamics of “policy drift”—the transformation of a policy's outcomes due to the failure to update its rules or structures to reflect changing circumstances. Drift is a ubiquitous mode of policy change in America's gridlock-prone polity, and its causes are now well understood. Yet surprisingly little attention has been paid to the political consequences of drift—to the ways in which drift, like the adoption of new policies, may generate its own feedback effects. In this article, we seek to fill this gap. We first outline a set of theoretical expectations about how drift should affect downstream politics. We then examine these dynamics in the context of four policy domains: labor law, health care, welfare, and disability insurance. In each, drift is revealed to be both mobilizing and constraining: While it increases demands for policy innovation, group adaptation, and new group formation, it also delimits the range of possible paths forward. These reactions to drift, in turn, generate new problems, cleavages, and interest alignments that alter subsequent political trajectories. Whether formal policy revision or further stalemate results, these processes reveal key mechanisms through which American politics and policy develop.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".