Bibliographic record
Abstract
Abstract Conceptually, advocacy coalitions are referenced in several policy process theories and frameworks to describe groups of actors that share beliefs and coordinate efforts to influence public policy. In the past decades, advocacy coalitions have received increased attention as a concept and a theoretical approach to understanding collective action in the policy process. In this study, we argue that despite its growing popularity, past empirical research has mainly focused on identifying and describing advocacy coalitions while largely overlooking their role and impact as political organizations. Many of the core premises and assumptions about advocacy coalitions hereby remain understudied and untested. Here, we depart from the Advocacy Coalition Framework (ACF) to discuss the political organization of advocacy coalitions by focusing on four dimensions: (1) a basis for engagement in joint strategies, (2) capacity to mobilize political resources, (3) ability to gain influence in policy processes, and (4) perceptions of advocacy coalitions as a political entity. We briefly review the theory and evidence of each dimension and conclude that several core assumptions about advocacy coalitions yet remain to be empirically tested to enable further conceptual specification and theory development within the ACF and beyond. To this end, we propose a research agenda with suggested research questions, designs, and methodological considerations for advancing empirical research on the role and impact of advocacy coalitions in different cases and contexts.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".