Violence at the Margins: Street Gangs, Globalized Conflict and Sri Lankan Tamil Battlefields in London, Toronto and Paris
Bibliographic record
Abstract
This article explores the global dimensions of violent conflict and the parallels and links between violence in the diaspora and the homeland. It does so by discussing Tamil street gangs in London, Toronto and Paris. The Tamil diaspora played a key role in the war between the Sri Lankan government and the Liberation Tigers of Tamil Eelam (LTTE), which raged between 1983 and 2009. In spite of being a marginal phenomenon in the Tamil diaspora, Tamil street gangs became part of a wider culture of fear within the Tamil community and possibly reinforced the LTTE’s dominance over and fundraising in the diaspora. Although some of the rivalling gangs have been cast as pro- and anti-LTTE, gang violence cannot be interpreted as a direct continuation of conflict from Sri Lanka but has to be understood in relation to marginalization and identification in the city of residence. In everyday life in the diaspora, ‘the gang’ has been a way for some young Tamil men to strive for respect, riches and heroism, employing a mixture of references to gang culture and the LTTE and building on both ethnic and geographical identifications. The larger Tamil community, on its part, has been eager to dissociate itself from the street gangs as they threaten the image of the Tamils as law-abiding and well-adjusted migrants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".