JOURNAL ARTICLE

Chinese Heritage Language Maintenance in the Context of Superdiversity: Perspectives from Dialect-background Heritage Learners.

  • Published In: Researching & Teaching Chinese as a Foreign Language, 2024, n. 1. P. 97 1 of 3

  • Database: Education Source Ultimate 2 of 3

  • Authored By: Chen, Lin; Wang, Danping 3 of 3

Abstract

This article examines Chinese heritage language (CHL) maintenance among dialect-background CHL learners within the superdiverse context of New Zealand's Chinese diaspora. Based on questionnaire data from 56 university students who speak non-Mandarin Chinese dialects, the study finds that these learners exhibit diverse linguistic repertoires and sociocultural backgrounds, including multilingualism with Chinese dialects, Mandarin, English, and other third-country languages. While dialects remain prevalent in home use, there is a generational decline in the number of Chinese varieties spoken, alongside a general shift toward English, and Mandarin proficiency among dialect-background learners remains low despite its status as the primary language taught in Chinese programs. The findings highlight the complexity of CHL maintenance in superdiverse immigrant communities and suggest that policymakers and educators should recognize the heterogeneity of CHL learners, support language use beyond the home domain, and address educational inequities in Mandarin-focused curricula.

Additional Information

  • Source:Researching & Teaching Chinese as a Foreign Language. 2024/01, Issue 1, p97
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2024
  • ISSN:20531788
  • DOI:10.1558/rtcfl.26170
  • Accession Number:187542081
  • Copyright Statement:Copyright of Researching & Teaching Chinese as a Foreign Language is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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