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Record W2519077949 · doi:10.1111/ropr.12187

Media in the Policy Process: Using Framing and Narratives to Understand Policy Influences

2016· article· en· W2519077949 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Policy Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsNarrativeFraming (construction)ScholarshipPolicy analysisCohesion (chemistry)Public relationsPolitical sciencePublic policyEmpirical researchProcess (computing)SociologyEpistemologyPublic administrationComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.254
GPT teacher head0.579
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it