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Record W2696834201 · doi:10.1111/1475-6765.12220

How mass media attract political elites’ attention

2017· article· en· W2696834201 on OpenAlexaboutno aff
Julie Sevenans

Bibliographic record

VenueEuropean Journal of Political Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersEuropean Research Council
KeywordsPoliticsMass mediaPublic opinionMedia coveragePolitical sciencePublic relationsNews mediaMedia biasMedia relationsPolitical communicationMedia system dependency theorySociologyMedia studiesLaw

Abstract

fetched live from OpenAlex

Abstract Political agenda‐setting research has shown that policy makers are responsive vis‐à‐vis media priorities. However, the mechanisms behind this effect have remained understudied so far. In particular, agenda‐setting scholars have difficulties determining to what extent politicians react to media coverage purely because of the information it contains ( information effect ), and to what extent the effect is driven not by what the media say but by the fact that certain information is in the media ( media channel effect ), which is valued for its own sake – for instance, because media coverage is considered to be a reflection of public opinion. By means of a survey‐embedded experiment with Belgian, Canadian and Israeli political elites (N = 410), this study tests whether the mere fact that an issue is covered by the news media causes politicians to pay attention to this issue. It shows that a piece of information gets more attention from politicians when it comes via the media rather than an identical piece of information coming via a personal e‐mail. This effect occurs largely across the board: it is not dependent on individual politician characteristics.

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.

How this classification was reachedexpand

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)0.008
metaresearch head score (Gemma)0.055
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.255
GPT teacher head0.483
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2017
Admission routes1
Has abstractyes

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