The Gulf Information War| Cyberconflict, Online Political Jamming, and Hacking in the Gulf Cooperation Council
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".