Caught between the 'bleeding homeland' and the 'safe haven': negotiating loyalties in times of conflict
Bibliographic record
Abstract
The loyalties of immigrant groups have often been questioned, particularly when they are considered to be suspect minorities whose loyalties to their homelands may outweigh their loyalties to their countries of settlement. As such, the concept of "conflicting allegiances" is built on the premise that the two loyalties are mutually exclusive, and that one must be prioritized over the other. However, this dissertation argues that the narratives that second-generation members of the Sri Lankan Tamil diasporic community hold regarding their homeland and their country of settlement opens space for the adoption of a hybrid Canadian-Tamil/Tamil-Canadian identity, as well as dual loyalties for both their homeland and their country of settlement. In conceptualizing their homeland as a "bleeding homeland", with a history of discrimination and victimization, this diasporic community is motivated to engage in homeland politics and to identify strongly with their Tamil ethnic identity. This loyalty to their homeland is further reinforced by conceptualizing their country of settlement as a "safe haven", where the Canadian identity is centred on tolerance, diversity and multiculturalism. This dissertation draws on interviews conducted with second-generation members of the Sri Lankan Tamil community in Toronto as well as their age-cohort in Sri Lanka, and argues that while there may be concerns about immigrants as suspect minority groups who hold conflicting allegiances, the story of Canada as conceptualized by second-generation immigrants actually encourages the development of a hybrid identity and the maintenance of dual loyalties.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.035 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".