Transnational Radicalization, Diaspora Groups, and Within-group Sentiment Pools: Young Tamil and Somali Canadians on the LTTE and al Shabaab
Bibliographic record
Abstract
In recent years, the Tamil and Somali diasporas have come under intense scrutiny by the media and national security agencies in Canada. This is due to concerns that members of both communities may hold political grievances associated with their respective homelands that could be acted upon by joining or supporting transnational terrorist groups. Drawing on 168 in-depth interviews with youth and young adults in Toronto’s Tamil and Somali diasporas, we provide a comparative analysis of the varying ways that existing sentiment pools can operate to mobilize broad-levels of support for, or vilification of, the framing strategies of the LTTE and al Shabaab, respectively. Our findings show that frames that portray the LTTE in a positive light resonate deeply with the young Tamil-Canadians we interviewed, characterizing a “narrative fidelity” between these frames and the existing sentiment pool. By contrast, there exists considerable disconnect between the framing strategies of al Shabaab, their supporters, and existing sentiment within the Somali diaspora – a divide that illustrates the notion of “framing failure”. We conclude with a discussion of the dynamic nature and inherent malleability of group-level sentiment pools, and highlight why this may be important from a national security standpoint.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".