Han H., Dugalich N.M. —
Self-mention in Chinese linguistic MA novices’ and experts’ academic writing: A corpus-driven investigation of ‘we’
// Litera. – 2024. – ¹ 4.
– 和。 182 - 194.
DOI: 10.25136/2409-8698.2024.4.70516
URL: https://e-notabene.ru/fil/article_70516.html
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注释,注释: The research aims to conduct a comparative analysis of self-mention, particularly the usage of self-mention ‘we’, as a means of academic persuasion between Chinese linguistic MA novices and linguistic experts. Self-mentions serve various rhetorical functions in academic persuasion. However, for second language writers, mastering these rhetorical functions represents an advanced writing skill, which is contingent upon a proficient command of the structural aspects of self-mention phrases. In light of this issue, this study undertakes a collocation and chunk analysis. The objective is to analyze the collocation characteristics and chunk features of self-mention ‘we’ in international journal articles (ILJA_C) and Chinese MA theses (CLMA_C). This objective informs the choice of the research subject – identifying similarities and differences in the utilization of self-mention ‘we’ in two databases: ILJA_C and CLMA_C. This study’s methodology utilizes a corpus-driven approach alongside comparative academic discourse analysis within academic writing. The novelty of this research lies in its investigation of the collocation characteristics and chunk features of self-mention ‘we’ in CLMA_C and ILJA_C. This study represents a substantial contribution to the fields of second language acquisition and comparative linguistics by enhancing our understanding of self-mention in academic persuasion. Findings reveal significant disparities in the usage of ‘we’ between Chinese MA novices and linguistic experts. Novices tend to focus on constructing discourse logic, whereas experts prioritize establishing academic positions. Analysis of chunk structures exposes varying approaches to discourse and interpersonal functions, underscoring the necessity for novices to emulate expert usage patterns.