Refugee Legacies, Media Objects, and Collective Memory: Evolutions of Diasporic Consciousness among Sri Lankan Tamils in Canada
Bibliographic record
Abstract
Since the 1980s, Sri Lanka’s ethnic conflict has caused the mass migration of over a million ethnic Tamils from their homeland, creating a transnationally interconnected Sri Lankan Tamil diaspora deeply engaged in homeland politics. This study focuses on the Tamil-Canadian diaspora, the largest Sri Lankan Tamil community outside of Sri Lanka. It seeks to explore the way particular histories of war, refugee migration, and a sustained relationship with media technologies and objects have shaped the Sri Lankan Tamil diaspora’s collective memory and diasporic consciousness. Through paying particular attention to second-generation Tamil-Canadians, this study investigates how this generation interacts with, are influenced by, and ultimately tries to contribute to this community’s evolving diasporic consciousness. Building an interdisciplinary methodology, this study blends together digital ethnography, participant observation, media and visual culture analysis, as well as in-depth interviews to consider various interrelated questions about the forces that shape the identity, collective memory, and political subjectivities of this diasporic community. This study argues that it is useful to approach this community and others like it through the framework of what it means to study a refugee diaspora. The refugee diaspora framework asks us to consider how diasporic communities that migrate to escape war are wholly shaped by the particular conditions of their violent dispersal and the continuities that stem from it. These continuities extend far beyond the legal term, ‘refugee’ and have lasting effects on the diasporic imaginings of future generations. This study also argues that the collective memory of this transnational diaspora is one that is maintained by both politicized narratives forwarded by community organizations and more amorphous and embodied memories and traumas carried by individuals and families. This collective memory is then continually mediated through what this study calls a visual landscape of identity, which is made up of the media technologies, media objects, local realities, imagined homelands, and inherited traumas that the Tamil-Canadian diaspora engages with. Finally, this study argues that in their place-making projects, the second-generation of this diaspora is learning how to contribute to an evolving sense of diasporic consciousness through integrating their unique experiences, perspectives, and political subjectivities into the tapestry of Tamil identity woven by the generations before them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".