Intercultural identity and heritage language maintenance among 1.5-generation immigrants
Bibliographic record
Abstract
The maintenance of heritage languages among immigrant populations is closely intertwined with identity and cultural dynamics. However, the interaction between intercultural identity and language maintenance across immigrant groups remains underexplored. This study examined how intercultural identity relates to Persian language maintenance among 1.5-generation Iranian immigrants in English-speaking contexts (the United States, Canada, and the UK). Data were collected through semi-structured interviews and narrative prompts with 25 participants and analyzed using a qualitative, bottom-up approach. Findings revealed that intercultural identity fostered emotional connections to Persian through family interactions and cultural practices, reinforcing its use as a symbol of authenticity, while Persian use deepened emotional ties to heritage and strengthened intercultural identity. Intercultural identity and heritage language maintenance were found to be mutually reinforcing through bilingual communication and blended cultural traditions, creating hybrid spaces where Persian remains meaningful. Yet, assimilation pressures and comfort with English sometimes weakened attachment to Persian. Overall, the study highlights the dual role of intercultural identity as both a facilitator and barrier to heritage language maintenance, offering implications for families, educators, and policymakers in supporting immigrant youth through culturally responsive and inclusive practices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".