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Record W2117075192 · doi:10.1093/bjc/azu103

Narratives and Counternarratives: Somali-Canadians on Recruitment as Foreign Fighters to Al-Shabaab

2015· article· en· W2117075192 on OpenAlexaffabout
Paul Joosse, Sandra M. Bucerius, Sara K. Thompson

Bibliographic record

VenueThe British Journal of Criminology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNarrativeSomaliRadicalizationPolitical scienceScholarshipTerrorismDiasporaGender studiesCriminologySociologyLaw

Abstract

fetched live from OpenAlex

Recently, the Somali diaspora has found itself at the centre of heightened security concerns surrounding the proliferation of international terrorist networks and their recruitment strategies. These concerns have reached new levels since the absorption of al-Shabaab into al-Qaeda in 2012. Based on a qualitative analysis of interviews with 118 members of Canada’s largest Somali community, this article draws upon narrative criminology to reverse the ‘why they joined’ question that serves as the predicate for much recent radicalization scholarship, and instead explores, ‘why they would never join’. We encounter Somali-Canadians equipping themselves with sophisticated counternarratives that vitiate the enticements of al-Shabaab. Particularly, notions of ‘coolness’, ‘trickery’ and ‘religious perversion’ mediate participants’ perceptions of al-Shabaab and enable a self-empowering rejection of its recruitment narratives. In particular, we find resonances between the narratives of non-recruits and ‘bogeyman’ narratives that exist commonly in many cultures. The efficacy of these narratives for resilience is three-fold, positioning the recruiters as odious agents, recruits as weak-minded dupes and our participants as knowledgeable storytellers who can forewarn others against recruitment to al-Shabaab.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.149
GPT teacher head0.371
Teacher spread0.223 · 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

Citations68
Published2015
Admission routes2
Has abstractyes

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