Language and Acculturation Among Iranian Immigrants in Canada
Bibliographic record
Abstract
Since Canada is one of the world’s major recipients of immigrants and refugees, their social \naccommodation remains one of the priorities in immigrant studies, bilingualism studies, and \nimmigrant language research (Green & Worswick, 2017; Picot, 2008). However, since the cultural \nand linguistic accommodation needs differ by the group of immigrants, it is essential to increase \nthe diversity of ethnic groups in the scope of research (Shea et al., 2022). More specifically, \nlinguistic and acculturation experiences of Persian-speaking immigrants in Canada have been \nunder-investigated despite a steady increase in the population of Iranian immigrants in Canada \nover the last decade (Rahnama, 2020). This study investigates how Persian-speaking immigrants \nadjust to life in Canadian society, acquire English, and whether they are preserving their heritage \nculture and maintaining the Persian language. \nThe research is grounded in bilingualism and multilingualism theory (Lorenz et al., 2023), \nas well as in the bidimensional model of immigrant acculturation theory (Berry, 1997). The study \nadopts a mixed methodology based on a descriptive and quantitative analysis of survey responses \nand a qualitative analysis of interviews with participants. The study thus falls into “Explanatory \nDesign” (Creswell, 2006, p. 37) informed by Model 4 of Steckler et al. (1992) classification of \nmixed methods designs in which qualitative and quantitative methods are used equally and in \nparallel. The results of the present study are based on the responses of 67 participants (age group \nof 18 years old and above) who spoke Farsi as the first language and also had some knowledge of \nthe English language. The analysis of survey questions was conducted using Chi-square test and \nKruskal-Wallis tests by rank. The participant responses to interview questions were first \ntranscribed using Otteri.ai, and the analysis of interview responses was conducted through \nthematic analysis with NVivo software. \nThe findings demonstrate a significant increase in the participants’ perceived importance \nof maintaining Persian language over time of immigration (at the time of immigration and at the \ntime of the study), whereas the importance of acquiring English skills remains relatively stable. \nProficiency in English emerged as a key factor in employment opportunities. Furthermore, \nbilingualism in English and Farsi was found to play a critical role in shaping and enriching the\nparticipants’ social interactions.\niv\nThe participants also suggested several measures that the government could apply to \nfacilitate the integration of newcomers in Canada. These suggestions include organizing social \nevents and gatherings where immigrants can interact and share their culture, government sponsored language training programs to help immigrants improve their English or French skills \nand offering other government programs and services for newcomers. Additionally, the \ndevelopment of Persian language learning programs in education was identified as a valuable \nstrategy for preserving the Persian culture, which the government could facilitate.\nThe current study extends the understanding of acculturation among Persian-speaking \nimmigrants in Canada, offering insights that could be helpful in developing a more supportive and \nwelcoming environment for newcomers.
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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 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".