Bibliographic record
Abstract
Abstract Research on language teacher identity in the field of heritage language (HL) teaching has received little attention, although identity is a central concern in HL education. Our research seeks to address this gap in the research on language teacher identity. Drawing on the Darvin and Norton’s (2015) conceptual framework of identity and investment, we investigate the extent to which Bangla HL teachers are invested in teaching Bangla, and how their investment provides insight into their identity as heritage language teachers. The study was conducted at the community-based Vancouver Bangla School, and the data, which focuses on our focal participant, Mili, were drawn from a year-long qualitative case study. Data sources include participant classroom observations, field notes, interview transcripts, a questionnaire, and educational resources used in the class, which were analyzed using thematic analysis. Findings indicate that Mili’s investment in teaching Bangla was deeply rooted in her ideological belief in the importance of HL maintenance for cultural continuity. However, she was also interested in the transcultural relationship between Bangla and English, and between Bangladeshi culture and Canadian culture. Her investment in teaching Bangla as a heritage language suggests that an HL teacher may serve as a cultural mentor, collaborator, innovator, and active community member. As a member of both the Canadian and Bangladeshi cultural community, she valued students’ Canadian cultural practices and helped students in negotiating their new transcultural identities as Bangladeshi-Canadians. Our study suggests that the identity of the HL teacher could be expressed as a transcultural identity that resists binaries and embraces hybridity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".