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Record W2585014195 · doi:10.1080/10584609.2016.1266065

Media Motivation and Elite Rhetoric in Comparative Perspective

2017· article· en· W2585014195 on OpenAlexaffabout
Eran Amsalem, Tamir Sheafer, Stefaan Walgrave, Peter John Loewen, Stuart Soroka

Bibliographic record

VenuePolitical Communication · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of Toronto
FundersEuropean Research Council
KeywordsEliteRhetoricPerspective (graphical)SociologyPolitical sciencePolitical communicationSocial psychologyPositive economicsEpistemologyPsychologyPoliticsPhilosophyEconomicsLawLinguisticsComputer science

Abstract

fetched live from OpenAlex

The exchange of diverse points of view in elite deliberation is considered a cornerstone of democracy. This study presents evidence that variations in political motivation for media use predict the tendency of politicians to present deliberative rhetoric that considers multiple points of view regarding issues and sees those views as related to one another. We surveyed 111 incumbent Members of Parliament in Belgium, Canada, and Israel and analyzed a large sample of their parliamentary speeches. The findings demonstrate that motivation to attain media coverage and act upon information from the news media leads politicians to strategically display simple and unidimensional rhetoric due to newsworthiness considerations, but only in countries where the media constitute important resources for reelection. The results contribute to extant literature by demonstrating a media effect on elite deliberation and by emphasizing the moderating role of political systems on the nature of elite rhetoric.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.148
GPT teacher head0.423
Teacher spread0.275 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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 routes2
Has abstractyes

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