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Record W1940610975 · doi:10.1111/1467-9477.12003

The Effects of Human Interest and Conflict News Frames on the Dynamics of Political Knowledge Gains: Evidence from a Cross‐national Study

2013· article· en· W1940610975 on OpenAlex
Nael Jebril, Claes H. de Vreese, Arjen van Dalen, Erik Albæk

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

VenueScandinavian Political Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsPoliticsDynamics (music)Political scienceNational interestSociologyLaw

Abstract

fetched live from OpenAlex

A well‐functioning democracy needs the news media to provide information to its citizens. It is therefore essential to understand what kinds of news contents contribute to gains in citizens' political knowledge and for whom this takes place. Extant research is divergent on this matter, especially with respect to ‘softer’ news coverage. This cross‐national study investigates the effects of exposure to human interest and conflict frames in the news on political knowledge. Drawing on panel surveys and media content analyses in three countries, the study shows how these two frames contribute positively to political knowledge gain. This relationship is moderated by political interest so that those who are least interested learn the most from this type of easily accessible news coverage. The results are discussed in the light of research on news media and knowledge acquisition.

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)0.000
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.129
GPT teacher head0.450
Teacher spread0.321 · 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