Advocacy coalitions and political control
Bibliographic record
Abstract
Abstract The Advocacy Coalition Framework (ACF) posits that policy actors, including elected officials and bureaucrats, aggregate into coalitions based on shared beliefs and coordinate to achieve policy objectives. Yet, bureaucrats are often subject to political control mechanisms understood within a principal‐agent framework. Combining insights from principal‐agent theory and the ACF, we explore the nature of principal‐agent relationships within and across advocacy coalitions in the United States using case studies of nuclear waste management and fair housing policy. Specifically, we develop three propositions regarding principals and agents as members of advocacy coalitions and examine those propositions by comparing the two case studies. We find that powerful elected officials and expert bureaucrats are important resources for coalitions; bureaucrats are in coalitions but face cross‐pressure from principals in opposing coalitions; and control mechanisms embedded in policy designs by principals can limit bureaucratic discretion in a way that aligns with coalition goals. Related Articles Neill, Katharine A., and John C. Morris. 2012. “A Tangled Web of Principals and Agents: Examining the Deepwater Horizon Oil Spill through a Principal–Agent Lens.” Politics & Policy 40(4): 629–56. https://doi.org/10.1111/j.1747‐1346.2012.00371.x Peterson, Holly L., Mark K. McBeth, and Michael D. Jones. 2020. “Policy Process Theory for Rural Studies: Navigating Context and Generalization in Rural Policy.” Politics & Policy 48(4): 576–617. https://doi.org/10.1111/polp.12366 Swigger, Alexandra, and Bruce Timothy Heinmiller. 2014. “Advocacy Coalitions and Mental Health Policy: The Adoption of Community Treatment Orders in Ontario.” Politics & Policy 42(2): 246–70. https://doi.org/10.1111/polp.12066
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".