Corpus-based contrastive study of interactional metadiscourse in Spanish academic writing
Metadiscourse refers to the reflexive language that writers/speakers use to interact with readers/hearers, and it has gained increasing scholarly interest over the past few decades (Hyland, Wang, & Jiang, 2022; Hyland, 2017; Flowerdew, 2015). In academic writing, metadiscourse is considered crucial as its use i) helps writers organise information flow to guide readers through the text (interactive dimension); ii) allows writers to express their stance on propositional information and engage readers in co-constructing the text (interactional dimension) (Hyland, 2005).
Despite the popularity of this research topic, most previous studies have centred around English, whereas other languages such as Spanish have been under-researched. Moreover, prior research has often focused on a single factor or multiple factors but without considering their potential interaction.
This study aims to address these gaps by investigating the effects of nativeness and expertise on the use of interactional metadiscourse in Spanish academic writing. Four corpora were compiled to represent different levels of nativeness (native and non-native) and expertise (expert and novice). Drawing on the work by Hu & Cao (2015), Lee & Casal (2014) and Mur-Dueña (2011), we adopted a fine-grained taxonomy as the analytical framework. We used MAXQDA 2020 (VERBI Software, 2019) for corpus management and metadiscourse annotation.
The statistical results showed significant differences in some interactional categories across native/non-native and expert/novice groups. Furthermore, a key item analysis revealed that certain interactional markers were specific to certain writer groups, possibly due to differences in register awareness, lexical competence, and cultural background. Finally, we discuss the implications of the findings.
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VERBI Software. (2019). MAXQDA 2020. VERBI Software. www.maxqda.com