The Non-sexist Language Debate in French and English

COADY, Ann (2018). The Non-sexist Language Debate in French and English. Doctoral, Sheffield Hallam University.

[img]
Preview
PDF
Coady_2018_PhD_nonsexistlanguage_(VoR).pdf - Accepted Version
Creative Commons Attribution Non-commercial No Derivatives.

Download (10MB) | Preview
Link to published version:: https://doi.org/10.7190/shu-thesis-00133

Abstract

The field of gender and language has gradually abandoned studies of gender-fair language, perhaps considering that there is little left to say on the subject. However, the debate over gender-fair language rages on in the media. Language bodies spend a significant amount of time and money on producing guidelines, yet there have been woefully few studies on what speakers think of these reforms, and the few studies that have been carried out have tended to focus on small groups. In addition, there have been very few analyses of how sexism gets debated and defined within media texts themselves, whereas examining social evaluations of language is essential in understanding the motivating force of language change. There is also a dearth of comparative studies in gender and language, which would allow conceptions of language in general, as well as feminist linguistic reforms, to be framed in their cultural and historical perspectives. This thesis aims at filling this gap in the field of gender and language by examining discourses on feminist linguistic reform in the media from a cross-linguistic perspective. A corpus of 242 articles (approx. 167,000 words) spanning 15 years (2001-2016), whose main topic is (non-)sexist or gender-fair language was collected from British and French on-line national newspapers. Apart from the obvious fact that the media have an enormous influence on public opinion, this is where the debate on sexist language has traditionally been carried out, and thus the media play a special role in the debate. On-line newspaper texts were therefore chosen in an effort to find discourses that readers are exposed to on a regular basis, and that could be classed as widespread and familiar to the general public. A corpus-based analysis was employed as a starting point to identify traces of discourses that are used to frame arguments in the gender-fair language debate. Frequency lists, keyword lists, and word sketches were carried out in order to indicate possible directions for analysis. Hypotheses based on the literature review were also followed up with searches for particular semantically related terms relating to discourses found in other studies. Finally, a CDA analysis was carried out on relevant concordance lines. Twelve main discourses were identified in the two corpora, based on six principle ideologies of language. Findings indicated that the overwhelming majority of these discourses and language ideologies are found in both the English and the French corpus, and across the political spectrum of newspaper groups. However, differences in quantitative and qualitative use may indicate on the one hand, deeper cultural differences between the UK and France, and on the other, core political and moral values between the right and left wing. The main contribution to knowledge that this thesis makes is in helping to revitalise research on sexist language through an analysis of the discourses and language ideologies that determine the success, or failure, of non-sexist language, as well as a novel analysis of the origin of sexism in language (Chapter 3).

Item Type: Thesis (Doctoral)
Contributors:
Thesis advisor - Clark, Jodie
Additional Information: Director of studies: Dr Jodie Clark
Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
Identification Number: https://doi.org/10.7190/shu-thesis-00133
Depositing User: Colin Knott
Date Deposited: 19 Feb 2019 13:38
Last Modified: 26 Apr 2021 13:29
URI: https://shura.shu.ac.uk/id/eprint/24058

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics