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Record W2891247799 · doi:10.1080/17467586.2018.1517943

Theology, heroism, justice, and fear: an analysis of ISIS propaganda magazines<i>Dabiq</i>and<i>Rumiyah</i>

2018· article· en· W2891247799 on OpenAlexaff
Tyler Welch

Bibliographic record

VenueDynamics of Asymmetric Conflict · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTerrorismIslamNarrativeSociologyRadicalizationLoyaltyTypologyInjusticeEconomic JusticeMedia studiesContent analysisCriminologyLawPolitical scienceSocial scienceHistoryLiterature

Abstract

fetched live from OpenAlex

This paper analyses a large content sample of Islamic State of Iraq and al-Sham (ISIS) English-language magazines. Dabiq (15 issues, 2014–2016) and Rumiyah (13 issues, 2016–2017) represent the largest cohesive text sample of ISIS propaganda targeted at English speakers. This qualitative analysis, creates a typology to explain and categorize articles within the sample. Magazine articles are divided into five categories: (1) Islamic theological justification and inspiration for violence, (2) descriptions of community, belonging, and meaning, (3) stories of progress or heroism, (4) establishment of a common enemy, i.e., the West and Muslim “apostate s,” and (5) instructional and inspirational articles empowering individual violent action. A focus on unity and community was more common in Dabiq, while instructional articles encouraging lone wolf attacks appeared more often in Rumiyah. Moreover, tales of heroism and progress are far more common in Dabiq, while Rumiyah issues focus on Islamic justification and call for loyalty and sacrifice. This follows the shift in ISIS’s operational focus from administering a physical caliphate to inspiring attacks locally and abroad. Knowing exactly what types of messages and narratives are being circulated in ISIS propaganda has important implications for understanding the psychology of terrorism, radicalization, securitization, and counterterrorism.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.318
Teacher spread0.301 · 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 designObservational
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

Citations32
Published2018
Admission routes1
Has abstractyes

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