Advocacy coalitions and political control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it