Hedging in Academic Writing: Cross-disciplinary Comparisons in the Michigan Corpus of Upper-Level Student Papers (MICUSP).

Xue Wang
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Abstract

Hedging has been a long-standing challenge for English learners. Emerging from the research on hedging in academic writing is the natural/social science dichotomy that hedging is more common in social sciences than in natural sciences. Yet, this line of research has been primarily based on a limited number of disciplines. To bridge this gap, this study compares sixteen disciplines to uncover the cross-disciplinary variation in hedging based on successful student writing captured by the Michigan Corpus of Upper-level Student Papers (MICUSP). Five types of hedging devices were investigated. The results suggest that hedging is more common in argumentation-driven disciplines than in the data-driven ones. Cross-disciplinary differences were also found between disciplines under the same division. The findings challenge assumptions and raise questions about the natural/social science dichotomy in academic writing, calling for discipline-specific instruction on hedging in teaching English for academic purposes. The study also demonstrates the affordances of corpus tools for data-driven teaching and computer-assisted language learning in remote learning during the COVID-19 pandemic.
学术写作中的模棱两可:密歇根大学高级学生论文语料库(MICUSP)的跨学科比较。
模糊取舍一直是英语学习者面临的难题。从学术写作中的模糊措辞研究中出现了自然/社会科学的二分法,即模糊措辞在社会科学中比在自然科学中更常见。然而,这方面的研究主要是基于有限的几个学科。为了弥补这一差距,本研究比较了16个学科,以密歇根高级学生论文语料库(MICUSP)捕获的成功学生写作为基础,揭示了对冲的跨学科差异。研究了五种类型的套期保值工具。结果表明,对冲在论证驱动的学科中比在数据驱动的学科中更常见。同一部门的学科之间也存在跨学科差异。研究结果挑战了假设,并提出了关于学术写作中自然/社会科学二分法的问题,呼吁在学术英语教学中针对学科进行限制语教学。该研究还展示了语料库工具在2019冠状病毒病大流行期间用于数据驱动教学和计算机辅助语言学习的远程学习中的功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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