Bibliographic record
Abstract
ABSTRACT One of the most significant innovations within policy process theories is the concept of advocacy coalitions, namely how policy actors self‐organize to collectively pursue shared goals. Despite the centrality of advocacy coalitions and extensive research spanning decades on this concept, questions remain about their functional purpose in a democracy. For this paper, we introduce the novel concept of “advocacy coalition representation” to describe how advocacy coalition members act as representatives of their constituencies. Taking an Advocacy Coalition Framework perspective, this paper begins with an argument incorporating democratic representation into the theoretical infrastructure of the Framework. It illustrates this argument by examining advocacy coalition representation in Colorado's abortion policy subsystem through interviews with pro‐ and anti‐abortion‐access actors. Ultimately, we find that the two coalitions offer patterns of advocacy coalition representation distinct from one another along two key variables: constituencies ( who advocates represent) and engagement ( how advocates represent). This paper ends with theoretical and empirical implications before returning to the theme of democracy and policy process research. Related Articles Myers, N., A. T. Ann Pendergast, H. A. Gage Atwood, W. Brian and H. Danielle. 2025. “Roles in Resilience: The Intersection of Genetic Counseling, Policy Advocacy, and Community Resilience.” Politics and Policy 53, no. 2: e70023. https://doi.org/10.1111/polp.70023 . Silagadze, N. 2021. “Abortion Referendums: Is There a Recipe for Success?” Politics and Policy 49, no. 2: 352–389. https://doi.org/10.1111/polp.12398 . Swigger, A. and B. T. Heinmiller. 2014. “Advocacy Coalitions and Mental Health Policy: The Adoption of Community Treatment Orders in Ontario.” Politics and Policy 42, no. 2: 246–270. 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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".