Using a stance corpus to learn about effective authorial stance-taking: a textlinguistic approach

Abstract

Abstract Presenting a persuasive authorial stance is a major challenge for second language (L2) writers in writing academic research. Failure to present an effective authorial stance often results in poor evaluation, which compromises a writer's research potential. This study proposes a “textlinguistic” approach to advanced academic writing to complement a typical corpus approach that is oriented toward exploring lexico-grammatical patterns at the sentence level. A web-based stance corpus was developed which allowed the users to study both the linguistic realizations of stance at clause/sentence level and how stance meanings are made at the rhetorical move level. The assumptions the study tested included: (1) whether a textlinguistic approach assists L2 writers to polish their research argument particularly as a result of improved stance deployment, and (2) whether the web-based corpus tool affords a constructivist environment which prompts the learners to infer linguistic patterns to attain deeper understanding. Seven L2 doctoral students in the social sciences were recruited. The results indicate a positive relationship between writing performance and more accurate use of stance. However, the application of higher order cognitive skills (e.g., inferring and verifying) was infrequent in the corpus environment. Instead, the writers used more lower-level cognitive skills (e.g., making sense and exploring) to learn. The participants accessed the integrated “context examples” most frequently to guide their learning, followed by rhetorical “move examples” and clause-based “stance examples”. This suggests that the learning of stance is critically contingent on the surrounding contexts. Overall, the study reveals that effective authorial stance-taking plays a critical role in effective academic argument. To better assist L2 academic writers, incorporating more (con)textual examples in computer corpora tools is recommended.

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