The Potential of a Corpus-based Approach to Studying the Move Structure of Scientific Abstracts
Year: 2026
Type: article
Abstract: Move structure analysis of scientific abstracts has traditionally relied on manual, qualitative genre coding within frameworks such as Swales' CARS model, an approach that is time-consuming, difficult to scale, and vulnerable to subjective boundary decisions, especially for under-researched academic traditions such as Uzbek. This paper explores the potential of a corpus-based methodology that complements manual move annotation with computational corpus tools, frequency word-lists, concordance analysis, and the extraction of recurrent lexical bundles, to identify and validate move boundaries in scientific abstracts. A corpus of abstracts was manually annotated for moves and then cross-analysed against automatically extracted lexical bundles to test how consistently these bundles signal individual moves. The results show that each move is associated with a fairly stable set of recurrent lexical signals, allowing much of the move structure to be recovered or anticipated from corpus data alone, although cyclical and embedded moves still require manual, context-sensitive judgment. The study concludes that a hybrid corpus-assisted methodology offers a scalable, replicable, and cross-linguistically applicable complement to traditional genre analysis of scientific abstracts.
Author: Eshchanova, Mavjuda
Language: English
Subfield: Linguistics and Language
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204596005