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Record W2326368425 · doi:10.1080/1057610x.2016.1157403

“Electronic <i>Jihad</i>”: The Internet as Al Qaeda's Catalyst for Global Terror

2016· article· en· W2326368425 on OpenAlexaff
Martin Rudner

Bibliographic record

VenueStudies in Conflict and Terrorism · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsCarleton University
Fundersnot available
KeywordsRadicalizationTerrorismThe InternetPolitical scienceDiasporaAl qaedaSocial mediaJihadismAfghanInternet privacyCriminologyMedia studiesLawSociologyPoliticsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The Internet has emerged as a key technology for Al Qaeda and other jihadist movements waging their so-called electronic jihad across the Middle East and globally, with digital multiplier effects. This study will examine the evolving doctrine of “electronic jihad” and its impact on the radicalization of Muslims in Western diaspora communities The study describes Internet-based websites that served as online libraries and repositories for jihadist literature, as platforms for extremist preachers and as forums for radical discourse. Furthermore, the study will then detail how Internet connectivity has come to play a more direct operational role for jihadi terrorist-related purposes, most notably for inciting prospective cadres to action; for recruiting jihadist operatives and fighters; for providing virtual training in tactical methods and manufacture of explosives; for terrorism financing; and for actual planning and preparations for specific terror attacks. Whereas contemporary jihadist militants may be shifting from the World Wide Web to social media, such as Facebook, YouTube, and Twitter for messaging and communications, nevertheless the Internet-based electronic jihad remains a significant catalyst for promoting jihadist activism and for facilitating terrorist operations.

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 score0.712
Threshold uncertainty score0.736

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.051
GPT teacher head0.395
Teacher spread0.344 · 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

Citations76
Published2016
Admission routes1
Has abstractyes

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