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Cited by 184

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Corpus Linguistics and Critical Discourse Analysis: Examining the ideology of sleaze

Orpin, Debbie·2005·International Journal of Corpus Linguistics

Abstract

Critical Discourse Analysis (CDA) has often proved fruitful in providing insights into the relationship between language and ideology. However, CDA is not without its critics. Constructive criticism has been offered by Stubbs, who suggests bolstering CDA by using a large corpus as the basis on which to make reliable generalisations about language use. Taking up that suggestion, this paper reports on a study of a group of words semantically related to corruption. In the study, corpus methodology is used to manipulate the data: concordances and collocational tools are used to provide semantic profiles of the words and highlight connotational differences, and to identify the geographical locations that the words refer to. It is argued that words with a noticeably negative connotation tend to be used when referring to activities that take place outside of Britain, while less negative words are used when referring to similar activities in British contexts. CDA theory is drawn on to interpret the ideological significance of the findings.

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Metadata

Authors
Orpin, D.
Venue
International Journal of Corpus Linguistics
Published
2005-03-11
Volume
10
Issue
1
Pages
37-61
ISSN
  • 1384-6655 (ISSN-L)
  • 1569-9811
OpenAlex
W1984422976
Cited by
184
References
0
FWCI
104.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Corpus Linguistics and Critical Discourse Analysis: Examining the ideology of sleaze. https://app.cochat.ai/papers/10.1075/ijcl.10.1.03orp
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Orpin, D. (2005). Corpus Linguistics and Critical Discourse Analysis: Examining the ideology of sleaze. International Journal of Corpus Linguistics. https://doi.org/10.1075/ijcl.10.1.03orp

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.