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Quantifying Disciplinary Voices: An Automated Approach to Interactional Metadiscourse in Successful Student Writing
This article reports on a study that explored cross-disciplinary variation in the use of metadiscourse markers in advanced-level student writing, put forward as a realistic target for novice writers. Starting from the stance and engagement categories included in Hyland’s model, we first conducted a...
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Published in: | Written communication 2020-04, Vol.37 (2), p.208-244 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This article reports on a study that explored cross-disciplinary variation in the use of metadiscourse markers in advanced-level student writing, put forward as a realistic target for novice writers. Starting from the stance and engagement categories included in Hyland’s model, we first conducted a comprehensive quantitative analysis of interactional metadiscourse across disciplines. For this analysis, we used an automated processing tool that generates quantity scores for each metadiscourse category. We then carried out a detailed qualitative analysis of selected items that contributed significantly to these category scores. The data for our analyses come from a corpus of 829 student papers from 16 different disciplines. The results showed notable differences in students’ use of metadiscourse features across academic divisions and disciplines. We suggest that this offers evidence of advanced students’ ability to express interactional strategies that are in line with disciplinary expectations. We also found, however, that disciplines that fall into the same academic division were not necessarily similar in their use of interactional metadiscourse, which calls into question the usefulness of existing disciplinary groupings. The findings of this study offer insights into how to build an appropriate writerly stance in different academic communities. |
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ISSN: | 0741-0883 1552-8472 |
DOI: | 10.1177/0741088319898672 |