Making Tigers from Tamils: Long‐Distance Nationalism and Sri Lankan Tamils in Toronto
Bibliographic record
Abstract
ABSTRACT This article discusses the Sri Lankan Tamil diaspora in Toronto and its relationship to the Tamil separatist group, the Liberation Tigers of Tamil Eelam (LTTE). Taking the case of the Sri Lankan Tamils, oft‐cited as the example par excellence of long‐distance nationalism, I argue against naturalizing diasporic ethnonationalism to investigate instead how diasporas are fashioned into specific kinds of actors. I examine tensions that emerged as an earlier elite Tamil movement gave way to the contemporary migration of much larger class‐and caste‐fractured communities, while a cultural imaginary of migration as a form of mobility persisted. I suggest that concomitant status anxieties have propelled culturalist imaginations of a unified Tamil community in Toronto who, through the actions of LTTE‐affiliated organizations, have condensed the Tigers and their imagined homeland, Tamil Eelam, into representing Tamil community life. While most Tamils may not have explicitly espoused LTTE ideology, as a result of the LTTE becoming the backbone of community life, Tamils became complicit with and reaffirmed the LTTE project of defending “Tamilness” militarily in Sri Lanka and culturally in Toronto. I suggest that the self‐presentation of diasporic communities should be analyzed within specific histories, contemporary conflicts and fractures, and active mobilizing structures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".