Bibliographic record
Abstract
According to a recent study published by Statistics Canada, in 2036, more than half of immigrants in Canada will be of Asian origin, and South Asians will be the group with most people. Today Tamil people represent the most important South Asian group in Montréal, but their profiles and stories are many and diverse. Immigrants of Indian origin, refugees from the civil war in Sri Lanka or re-settlers from Malaysia or Africa, they recount dissimilar migration histories and profess different faiths. Focusing on the largest group, the Sri Lankan Tamil Saivite Hindus, this paper explores the relationships of this group with other Tamils living in Montréal, namely Tamil Catholics and Pentecostal Christians, as well as with Tamil Hindus of Indian origin. Also, this article discusses the different strategies of integration of these Tamil communities into the French-speaking majority of Québec and the English-speaking majority of Canada, which represent a main figure of the ‘Otherness’ encountered by the Sri Lankan Tamil Hindus in this diasporic context. More broadly, the article shows that the development of Hindu religious solidarities and interplays in diaspora depend on the socio-cultural composition and cohesion of the Hindu groups, but also on their migration stories, and on the social and political context of the host country. As a result, it turns out that in Montréal, Sri Lankan Hindus feel much closer to Sri Lankan Catholics than to Indian Tamil Hindus, which seems to imply that the sharing of the same land of origin, language, and migration pattern, is much more important than the belonging to Hindu religion in the re-building of togetherness and solidarity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".