A corpus-based cross-disciplinary analysis of hedging strategies in linguistics and engineering doctoral dissertation introductions

Hedging strategies are essential rhetorical tools in academic writing, enabling authors to express caution, manage interpersonal relationships, and conform to disciplinary conventions. This study examined the use of hedging strategies in the Introduction sections of doctoral dissertations (DDs) in Linguistics (Ling) and Electrical and Electronic Engineering (EEE) through a cross-disciplinary lens. Using a corpus-based, mixed-method approach, the research analyzed a specialized corpus of 50 DDs from leading US universities (2019–2023), comprising approximately 165,000 words. The analysis integrated Hyland’s (1998) poly-pragmatic model of hedging and Varttala’s (1999) lexical categorization, focusing on content-oriented and reader-oriented hedges. Corpus tools and log-likelihood tests were used to identify hedges and examine cross-disciplinary differences. Results revealed that Ling dissertations exhibited higher hedge frequency and lexical diversity, reflecting their interpretative nature, while EEE prioritized precision and objectivity. Key findings indicated that Ling employed diverse reader-oriented hedges for engagement, whereas EEE favored content-oriented hedges for technical precision. These distinctions highlight the discipline-sensitive variation in hedge frequency, functional orientation, and epistemic stance. The study contributes to a deeper understanding of disciplinary writing practices, with implications for teaching and improving scholarly communication. By tailoring hedging strategies to field-specific expectations, students and researchers can enhance clarity, credibility, and audience engagement in their academic work.

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