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Record W2944521182

The Gulf Information War| Cyberconflict, Online Political Jamming, and Hacking in the Gulf Cooperation Council

2019· article· en· W2944521182 on OpenAlex
Ahmed Al‐Rawi

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

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHackerPoliticsGovernment (linguistics)State (computer science)Political sciencePolitical economyMiddle EastSociologyLawComputer security
DOInot available

Abstract

fetched live from OpenAlex

This article offers insight into the role of hacking during the Qatar diplomatic crisis in 2017. I argue that the Middle East region has been witnessing an ongoing cyberconflict waged among different factions separated along regional rivalries, political alliances, and sectarian divisions. In relation to Qatar, systematic and well-calculated cyberoperations and hacking measures have been employed to pressure the Qatari government and influence its regional policies. Hackers, whether state-sponsored or not, intentionally created a diplomatic crisis in response to the perceived oppositional and unilateral policies carried out by the Qatari government in the region. The hacking incident led to other cyber-retaliations, and there is currently a cyberconflict between Qatar and a few other Arab states. I argue here that hacking is a form of online political jamming whose goal is to influence politics and/or change policies, and its communication impact flows either vertically (top-down or bottom-up) or horizontally.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.199
GPT teacher head0.503
Teacher spread0.304 · 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