Language, Identity and Social Integration: Stories of Skilled Bangladeshi Immigrants in Canada
Bibliographic record
Abstract
This emerging ethnographic study aims to explore the impact of English language and learning on the settlement and social integration of skilled Bangladeshi immigrants in Canada. Though language is a significant factor in immigrants’ settlement, few researchers have explored the relationship between language and immigrants’ social integration, and even fewer have researched the impact of language in skilled immigrants’ settlement (Han, 2007; Gimpapa & Canagarajah, 2017). Canada is a popular destination for Bangladeshi skilled immigrants; however, they have remained largely ignored in the academic research and in the ethno-social milieu of Canada. Therefore, this study aims to explore how Bangladeshi skilled immigrants learn the English language, how they socialize using the language and what struggles they face in learning the language. The conceptual framework of the research is drawn from a poststructural understanding of language (Bourdieu, 1977, 1986) and sociocultural theory of learning (Block, 2013; Lave & Wenger, 1991; Ochs, 1991), and the research methodology is informed by critical theory and critical ethnographic sociolinguistic research (Heller et al., 2017). The significance of this research lies in exploring the role of language, access and participation in the settlement of the underrepresented Bangladeshi skilled immigrants in Canada and in reconceptualizing migrants’ language learning needs and scopes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.052 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".