Media in the Policy Process: Using Framing and Narratives to Understand Policy Influences
Bibliographic record
Abstract
Abstract Policy scholarship has long sought to understand the role of knowledge and information in the policy process. Of the actors, institutions, and resources involved in shaping policy processes and outcomes, media and narratives have been incorporated into empirical policy scholarship and theories with varying success. The Narrative Policy Framework (NPF) is a framework through which scholars can bring analysis of narratives into studies of policy making. The NPF moves the field forward in understanding the role of narratives, communication, and stakeholder beliefs in the policy process, while at the same time striving for theoretical rigor. We embed the discussion of frames and narratives in the NPF to provide an empirical and theoretical cohesion to our understanding of media and public policy and then provide a brief empirical example of how such an integration may prove fruitful for policy scholars.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.008 |
| 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".