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Record W3200694876 · doi:10.1515/opis-2020-0117

Political fact-checking in the Middle East: What news can be verified in the Arab world?

2021· article· en· W3200694876 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpen Information Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMiddle EastGatekeepingPoliticsAuthoritarianismPolitical scienceGovernment (linguistics)Face (sociological concept)Selection (genetic algorithm)CensorshipJournalismMedia studiesPublic relationsLawSociologyComputer scienceLinguisticsSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This study examines the news selection processes followed by fact-checking organizations in the Middle East, specifically Egypt, Jordan, and the United Arab Emirates, and gatekeeping such organizations face while working under authoritarian rule. By reviewing fact-checked news posted on the Facebook pages of six Arabic language organizations: Da Begad, HereszTruth, Fatabyyano, Matsad2sh, MisbarFC, and Saheeh Masr, this study manually analyzes about 5,000 fact-checked news stories to understand the extent of political fact-checking performed on Arab presidents, heads of government, and rulers, along with the most verified news topics. Results show that organizations in the Middle East rarely fact-check Arab rulers or refute their claims, while their news selection process prioritizes human interest topics. The study suggests that Arab fact-checkers resort to self-censorship due to gatekeeping influences that impact the region’s media climate.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0060.017
Open science0.0020.000
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 head0.179
GPT teacher head0.382
Teacher spread0.203 · 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