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Record W3160837559 · doi:10.1080/19434472.2021.1919911

Re-examining the explanations of convert radicalization in Salafi-Jihadist terrorism with evidence from Canada

2021· article· en· W3160837559 on OpenAlexaffabout
David A. Jones, Lorne L. Dawson

Bibliographic record

VenueBehavioral Sciences of Terrorism and Political Aggression · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of WaterlooUniversity of Alberta
Fundersnot available
KeywordsRadicalizationDisappointmentTerrorismJihadismIslamEpistemologyPeriod (music)SociologyPsychologyPositive economicsSocial psychologyPoliticsCriminologyPolitical scienceLawPhilosophyEconomicsAestheticsTheology

Abstract

fetched live from OpenAlex

Evidence from multiple sources suggests converts to Islam are significantly overrepresented in the ranks of Salafi-jihadist terrorists. Researchers have been speculating for some time why this might be the case. This paper identifies, and critically examines, four hypothetical explanations commonly found in the literature: (1) some explanations focus on the significance of prior personal characteristics of the converts; (2) some explanations emphasize the rapidity of the movement from conversion to radicalization; (3) some explanations highlight the lack of religious knowledge on the part of radicalized converts; and (4) some explanations point to the role of the zealotry of converts. Examining each explanation, we find the causal mechanisms hypothesized are inadequate and the hypotheses are incongruent with the data we have collected on radicalized Canadian converts. In the end, we offer an alternative hypothesis, based on the analysis of the response of radicalized converts to an experience of disappointment that is common in the post-conversion period.

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)0.000
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 score0.363
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.071
GPT teacher head0.360
Teacher spread0.289 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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