When Policies Undo Themselves: Self‐Undermining Feedback as a Source of Policy Change
Bibliographic record
Abstract
Most studies of policy feedback have focused on processes of self‐reinforcement through which programs bolster their own bases of political support and endure or expand over time. This article develops a theoretical framework for identifying feedback mechanisms through which policies can becomeself‐underminingover time, increasing the likelihood of a major change in policy orientation. We conceptualize and illustrate three types of self‐undermining feedback mechanisms that we expect to operate in democratic politics: the emergence of unanticipated losses for mobilized social interests, interactions between strategic elites and loss‐averse voters, and expansions of the menu of policy alternatives. We also advance hypotheses about the conditions under which each mechanism is likeliest to unfold. In illuminating endogenous sources of policy change, the analysis builds on efforts by both historically oriented and rationalist scholars to understand how institutions change and seeks to expand political scientists’ theoretical toolkit for explaining policy development over time.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".