Language and Acculturation Among Iranian Immigrants in Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».