Transcultural identity and Bangla heritage language teaching
Bibliographic record
Abstract
The new waves of immigrants in Canada have exciting linguistic resources that need to be harnessed for personal, professional, and national interests. While prior studies have investigated the opportunities and challenges of heritage language (HL) community programs, few have explored the negotiation of transcultural identity in HL teaching, particularly with reference to the growing Bangladeshi community. Drawing on a two-year qualitative case study from September 2020 to December 2022, at the community-based Vancouver Bangla School (VBS), this research sought to address this gap by investigating a broad research question: How did Bangla HL teachers navigate their transcultural identity to promote student investment in the learning of Bangla? The study conceptualizes HL teachers’ transcultural identity using four key constructs: identity and investment (Darvin & Norton, 2015), translingual practices (Garcia & Li Wei, 2014), multimodal practices (Kress, 2014), and emotion labor (Benesch & Prior, 2023). The broad research question gave rise to three central research questions: (i) What is the identity of the Bangla HL teacher? (ii) How did VBS teachers leverage show-and-tell tasks to promote student investment in online Bangla HL learning? and (iii) How did volunteer teachers navigate emotion labor, identity, and investment in Bangla HL teaching? Data sources included participant classroom observations, field notes, interviews, a focus group discussion, questionnaires, and educational resources, which were analyzed using thematic analysis. Seven volunteer teachers participated in the study. Findings indicate that teachers were deeply invested in promoting Bangla as a mother tongue in multicultural Canada, with a strong ideological belief in the importance of HL maintenance for cultural continuity. They also embraced the transcultural relationship between Bangladeshi and Canadian culture. Teachers leveraged students’ translingual and multimodal resources using innovative activities like show-and-tell tasks to help students negotiate their transcultural identities. Teachers’ investment in promoting students’ transcultural identities was powerful, enabling them to navigate and manage the emotion labor associated with volunteer teaching and limited resources. The study found that the conceptualization of transcultural identity resists binaries and embraces hybridity. The research contributes to a growing body of literature exploring the complexity of teacher identity and transculturalism in heritage language education.
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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 | 0.002 |
| 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 | 0.016 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".