Exploring Socio-Political Dimensions of Heritage Language Maintenance: The Case of Vietnamese Speakers in Montréal
Bibliographic record
Abstract
Research on heritage language (HL) development often focuses on immigrants’ identity and social network as predictors of HL maintenance. However, an important and overlooked factor is the socio-political circumstances that trigger emigration, such as whether immigrants relocate due to economic hardship or political turmoil. This study examines if the pattern of HL maintenance and its association with immigrants’ ethnolinguistic identity and social engagement differ for families of political versus economic immigrants. Participants were 38 parent–child pairs from Montreal’s Vietnamese diaspora. The parents identified their reason for immigration and completed an ethnolinguistic questionnaire, their children provided a HL communication profile, and all participated in interviews. To determine participants’ Vietnamese speaking skills, short interview excerpts were rated for accentedness, comprehensibility, fluency, and global knowledge. Participants were generally successful at maintaining their HL, but there was a decline in the children’s Vietnamese, especially for the economic immigrants. Several ethnolinguistic variables (desire to preserve a HL, pride in heritage culture) and social network properties (network size, intimacy, communication-related stress) appeared to contribute to HL maintenance. However, these relationships were different (and sometimes opposite) in the groups of political versus economic immigrants, suggesting that HL development and maintenance are subject to various contextual influences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".