Narratives and Counternarratives: Somali-Canadians on Recruitment as Foreign Fighters to Al-Shabaab
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.056 | 0.028 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".