JOURNAL ARTICLE

The dynamics of bilingualism in language shift ecologies.

  • Published In: Linguistic Approaches to Bilingualism, 2023, v. 13, n. 1. P. 1 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Grenoble, Lenore A; Osipov, Boris 3 of 3

Abstract

A large percentage of the world's languages – anywhere from 50 to 90% – are currently spoken in what we call shift ecologies, situations of unstable bi- or multilingualism where speakers, and in particular younger speakers, do not use their ancestral language but rather speak the majority language. The present paper addresses several interrelated questions with regard to the linguistic effects of bilingualism in such shift ecologies. These language ecologies are dynamic: language choices and preferences change, as do speakers' proficiency levels. One result is multiple kinds of variation in these endangered language communities. Understanding change and shift requires a methodology for establishing a baseline; descriptive grammars rarely provide information about usage and multilingual language practices. An additional confounder is a range of linguistic variation: regional (dialectal); generational (language-internal change without contact or shift); contact-based (contact with or without shift); and proficiency-based (variation which develops as a result of differing levels of input and usage). Widespread, ongoing language shift today provides opportunities to examine the linguistic changes exhibited by shifting speakers, that is, to zero in on language change and loss in process, rather than as an end product. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Linguistic Approaches to Bilingualism. 2023/01, Vol. 13, Issue 1, p1
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2023
  • ISSN:1879-9264
  • DOI:10.1075/lab.22035.gre
  • Accession Number:162086303
  • Copyright Statement:Copyright of Linguistic Approaches to Bilingualism is the property of John Benjamins Publishing Co. 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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