Off to the Courts? Or the Agency? Public Attitudes on Bureaucratic and Legal Approaches to Policy Enforcement
Bibliographic record
Abstract
A key curiosity in the operation of the American regulatory state lies with its hybrid structure, defined by centralized, bureaucratic approaches but also more decentralized actions such as lawsuits brought by private citizens in the courts. While current research on these two pathways focuses at the elite level—exploring how and why political actors and institutions opt for legal or administrative strategies for implementing different public policies—there is little research that examines public attitudes toward how policy is enforced in the U.S. Given that the public is a key partner in this process, this paper integrates public attitudes into the discussion, tapping into conceptions of “big government,” privatization, and the tort reform movement. Using original data from a series of vignette-based experiments included in the 2014 Cooperative Congressional Election Survey, we examine public preferences about how policy is regulated—by private citizens in the courts or by government officials in agencies—across a broad number of policy areas. We offer one of the first studies that adjudicates the boundaries of public attitudes on litigation and bureaucratic regulation in the U.S., offering implications for how elites might approach the design of policy implementation for different issue areas.
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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 | 0.012 | 0.024 |
| 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 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".