Stance and engagement are important rhetorical resources for writers to construct interaction with readers and ideas by marking epistemic evaluation and bringing readers into the texts. Building on previous research that suggests notable differences in the use of stance and engagement in academic discourse, this comparative study investigates the use of stance and engagement in scientific research articles. By comparing two corpora that contain 144 research articles in total across 16 scientific disciplines, this study examines if the numbers of stance and engagement differ between manuscripts (unpublished research papers) that are produced by nonnative writers and those that are published in leading scholarly journals. Further analyses are also conducted to examine four types of stance (hedges, boosters, attitude markers, and self-mentioning) and five types of engagement (reader pronouns, questions, directives, appeals to shared knowledge, and personal asides) between two corpora. Quantitative analyses indicated that manuscripts written by nonnative writers featured markedly more hedges and attitude markers than those published in leading journals; published research articles used self-mentioning and directives significantly more frequently than those unpublished manuscripts. Moreover, results revealed that unpublished and published research articles shared similar patterns with regard to the numbers of using hedges, boosters, attitude markers, and directives. In this study, research articles published in leading journals are treated as the "norm" in terms of using stance and engagement. Results are discussed by comparing patterning of using stance and engagement and presenting examples extracted from published research articles. Study limitations, pedagogical implication, and future research directions are suggested.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1728 |
Date | 01 January 2021 |
Creators | Ma, Caoyuan |
Publisher | STARS |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Electronic Theses and Dissertations, 2020- |
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