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Record W2489188104 · doi:10.1080/09546553.2016.1207633

Video games, terrorism, and ISIS’s Jihad 3.0

2016· article· en· W2489188104 on OpenAlexaff
Ahmed Al‐Rawi

Bibliographic record

VenueTerrorism and Political Violence · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and Radicalism
Canadian institutionsConcordia University
Fundersnot available
KeywordsPublicityTerrorismIslamVideo gameDimension (graph theory)Social mediaMedia studiesPolitical scienceState (computer science)Online videoAdvertisingSociologyPublic relationsLawMultimediaHistoryComputer scienceBusiness

Abstract

fetched live from OpenAlex

This study discusses different media strategies followed by the Islamic State in Iraq and Syria (ISIS). In particular, the study attempts to understand the way ISIS’s video game that is called “Salil al-Sawarem” (The Clanging of the Swords) has been received by the online Arab public. The article argues that the goal behind making and releasing the video game was to gain publicity and attract attention to the group, and the general target was young people. The main technique used by ISIS is what I call “troll, flame, and engage.” The results indicate that the majority of comments are against ISIS and its game, though most of the top ten videos are favorable towards the group. The sectarian dimension between Sunnis and Shiites is highly emphasized in the online exchanges, and YouTube remains an active social networking site that is used by ISIS followers and sympathizers to promote the group and recruit others.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.292
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations93
Published2016
Admission routes1
Has abstractyes

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