Analyzing the Language of Citation across Discipline and Experience Levels: An Automated Dictionary Approach
| Autor: | David Kaufer, Suguru Ishizaki, Xizhen Cai |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | Journal of Writing Research, Vol 7, Iss 3, Pp 453-483 (2016) |
| Druh dokumentu: | article |
| ISSN: | 2030-1006 2294-3307 |
| DOI: | 10.17239/jowr-2016.07.03.07 |
| Popis: | Citation practices have been and continue to be a concentrated area of research activity among writing researchers, spanning many disciplines. This research presents a re-analysis of a common data set contributed by Karatsolis (this issue), which focused on the citation practices of 8 PhD advisors and 8 PhD advisees across four disciplines. Our purpose in this paper is to show what automated dictionary methods can uncover on the same data based on a text analysis and visualization environment we have been developing over many years. The results of our analysis suggest that, although automatic dictionary methods cannot reproduce the fine granularity of interpretative coding schemes designed for human coders, it can find significant non-adjacent patterns distributed across a text or corpus that will likely elude the analyst relying solely on serial reading. We report on the discovery of several of these patterns that we believe complement Karatsolis’ original analysis and extend the citation literature at large. We conclude the paper by reviewing some of the advantages and limits of dictionary approaches to textual analysis, as well as debunking some common misconceptions against them. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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