Linguistic Sexism In Fiction: A Corpus-Based Study Of Paired Terms In Selected Pakistani English Novels

Abstract

Going a step further from the amalgam of corpus linguistics and corpus stylistics, the present study embarks on the exploration of sexist language used in literary texts of female and male Pakistan writers from a feminist stylistic perspective. This study is a corpus based feminist stylistic analysis of two corpora of Qaisra Sheraz’s novels corpus (QSNC) and Nadeem
Aslam’s novels corpus (NANC) to analyze the sexist use of language at word level. From within the toolkit provided by Mills (1995) in her Feminist Stylistics, the study examines the data that is in the form of words. From within different categories at word level, the present study focuses on only one feature i.e., paired terms for a fine grain analysis. The data are collected through purposive sampling by 1corpus tool AntConc (3.2.1) utilizing its Clusters and Concordance programs. Following a mixed method approach, the paired terms with fe/male term at initial position are tabulated and analyzed frequency wise and with reference to their context of occurrences as well. The findings of the study indicate that both the novelists have used paired terms in their texts with a discriminative use of language. Sheraz has used binaries with male terms at the initial position for 81% times while only 19% binaries take female terms at initial position. Aslam, too, uses binaries with male term at initial position in maximum cases i.e., 65 % while only 35 % cases are there with female terms at initial position. The contextual analysis has also supported this linguistic discrimination based on the frequencies. It has been observed that male terms appear at initial position only when the context is depicting a positive situation or propagating a serious theme. All the binaries with female terms at initial position are occurring in negative contexts. Such a discriminative treatment of male and female terms highlights the sexist use of language in the analyzed corpora.