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Record W2314798703 · doi:10.1386/ajms.3.2.133_1

The digital transformation of Arab news: Is there a future for online news after the ‘Arab Spring’?

2014· article· en· W2314798703 on OpenAlexaff
Mohamed Ben Moussa, Aziz Douai

Bibliographic record

VenueJournal of Applied Journalism & Media Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsInteractivityJournalismCitizen journalismImmediacyNewspaperPolitical scienceThe InternetTechnical JournalismDigital mediaConvergence (economics)Public relationsNews mediaTechnological convergenceMedia studiesSociologyAdvertisingWorld Wide WebComputer scienceBusinessLawTelecommunications

Abstract

fetched live from OpenAlex

This study examines online news delivery and how Arab newspaper journalism has harnessed the Internet’s potential to deliver news in novel ways, reach new readers and audiences, and the implications for the way these users consume and interact with online news. Existing research on online journalism has documented the web’s effects on journalism practice and online news at several levels, including the reorganization of newsrooms and the incorporation of technical features such as interactivity and media convergence. Based on those research findings, we conduct a comparative analysis of 54 websites of online and print newspapers in the Arab world. The comparative analysis focuses on six main variables: (1) revenue resources, (2) editorial organization, (3) hyperlinks, (4) interactivity, (5) media convergence issues, and (6) updating/immediacy. While the Internet has opened new and immense opportunities for journalism in the region, the study finds little evidence to suggest that it is substantially contributing to transforming communication dynamics and journalistic practices that can foster dialogical discourse and participatory communication, both of which are central to the development of civic culture and liberal democracy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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.

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

Citations7
Published2014
Admission routes1
Has abstractyes

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