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Record W1654534265 · doi:10.1080/1461670x.2013.765636

INTERNATIONAL TV NEWS, FOREIGN AFFAIRS INTEREST AND PUBLIC KNOWLEDGE

2013· article· en· W1654534265 on OpenAlexaffabout
Toril Aalberg, Stylianos Papathanassopoulos, Stuart Soroka, James Curran, Kaori Hayashi, Shanto Iyengar, Paul K. Jones, Gianpietro Mazzoleni, Hernando Rojas, David Rowe, Rodney Tiffen

Bibliographic record

VenueJournalism Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsMcGill University
FundersEconomic and Social Research Council
KeywordsPolitical scienceCurranForeign policyPromotion (chess)Public relationsNews mediaPublic opinionNews bureauGovernment (linguistics)NewspaperPublic interestInternational relationsPoliticsLaw

Abstract

fetched live from OpenAlex

This article investigates the volume of foreign news provided by public service and commercial TV channels in countries with different media systems, and how this corresponds to the public's interest in and knowledge of foreign affairs. We use content analyses of television newscasts and public opinion surveys in 11 countries across five continents to provide new insight into the supply and demand for international television news. We find that (1) more market-oriented media systems and broadcasters are less devoted to international news, and (2) the international news offered by these commercial broadcasters more often focuses on soft rather than hard news. Furthermore, our results suggest that the foreign news offered by the main TV channels is quite limited in scope, and mainly driven by a combination of national interest and geographic proximity. In sum, our study demonstrates some limitations of foreign news coverage, but results also point to its importance: there is a positive relationship between the amount of hard international news coverage and citizens' level of foreign affairs knowledge.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.121
GPT teacher head0.372
Teacher spread0.250 · 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 designObservational
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

Citations128
Published2013
Admission routes2
Has abstractyes

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