JOURNAL ARTICLE
Transcending the Researcher‐Researched Divide: Participatory Linguistics Research in Kongish.
Published In: International Journal of Applied Linguistics, 2025, v. 35, n. 4. P. 2248 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lok, Pedro; Lee, Tong King; Tsang, Alfred; Wei, Li 3 of 3
Abstract
This study explores the transformative impact of participatory research on the conceptualization of Kongish. A hybridized written form comprising a creative blend of Cantonese and English, Kongish is a grassroots media‐linguistic phenomenon within the vibrant sociocultural ecology of Hong Kong. Our research aims to evaluate how a participatory methodology—as opposed to traditional researcher‐oriented approaches where researchers remain at arm's length with their research subjects—reshapes the understanding of Kongish as well as Hong Kong English (HKE). Using a participatory linguistics framework, this study triangulates data from focus group interviews, individual interviews, and online surveys to explore the reciprocal influences between researchers and participants. The study is organized into two tiers: first, the transformation of participants, and second, the transformation of researchers. Each tier draws on personal narratives to offer distinct insights into real‐life encounters with Hong Kong's written vernacular. By integrating the "lived experiences" of both researchers and participants, the study reveals how our interactions continuously influence each other's perceptions of language, language practices, and personal identities. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Applied Linguistics. 2025/11, Vol. 35, Issue 4, p2248
- Document Type:Article
- Subject Area:Sociology
- Publication Date:2025
- ISSN:0802-6106
- DOI:10.1111/ijal.12764
- Accession Number:189063647
- Copyright Statement:Copyright of International Journal of Applied Linguistics is the property of Wiley-Blackwell 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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