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Record W20236699

Re-examining the determinants in international news coverage : cross-news-category and cross-country comparisons

2003· dissertation· en· W20236699 on OpenAlexaboutno aff
He Huan

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2003
Typedissertation
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperNews mediaNews valuesContent analysisPolitical scienceAdvertisingPoliticsChinaGeographySociologySocial scienceBusinessLaw
DOInot available

Abstract

fetched live from OpenAlex

Since the 1960's, communication scholars have conducted numerous studies to identify determinants of international news flow and coverage. Most of these studies have failed to recognize the fact that different news categories are governed by different criteria of newsworthiness. Also, there has been a lack of research that investigates non-Western, especially non-U.S. news media, and a lack of comparative studies examining media in different parts of the world. The current study was designed to address the two problems. Using quantitative content analysis, four newspapers, which were published in Canada, India, China and the United States, were investigated. Their international news outputs in four news categories were compared. The four categories were political news, military news, economic news and news on natural disasters. Two hypotheses were tested: (1) In a newspaper, the determinants of international news coverage vary among different news categories; (2) The determinants of international news coverage in a particular news category are similar among newspapers from different countries. Hypothesis I was generally supported by the data; however, hypothesis II was only partially supported. The results suggest that research should be conducted to understand news-making processes in different news categories. More comparative studies are required to further explore similarities and variations in news values and practices in different countries.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
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 head0.056
GPT teacher head0.371
Teacher spread0.315 · 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 teacher head, not a consensus.

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

Citations0
Published2003
Admission routes1
Has abstractyes

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