How Languages Constructs Gender Roles and Reflects Power Dynamics in Literature, Exploring Speech Acts, Conversational Dominance, and Stereotypes
This research study analyses how language constructs gender roles and reflects power dynamics in literature, through speech acts, conversational dominance, and gendered stereotypes. Applying qualitative research methods and thematic analysis supported by NVivo software, this study examines how language in selected literary works mirrors societal gender norms and power structures. Works, such as by Toni Morrison, Virginia Woolf, Margaret Atwood, and William Shakespeare, show that the fact is that the language occupies a crucial place in a play of the power relation both in disrupting and in maintaining it. There is a realization of how the linguistic activity of the male character subordinates and, while women often turn to relatively servile speech forms within the dominant social hierarchies of gender. However, literature itself becomes a source of resistance through the language that upsets the norms. This is manifested in several works that challenge traditional norms through the language used and overturning conventional gender roles. The study leads to a broader implication based on the findings in illuminating the complex relationship of language with gender and power views in society. It concludes the research but suggests more avenues for further study such as non-Western literature, contemporary texts and also the intersectionality of gender with other social identities.
Authors
- Sana
- Zaibunnisa Zaibunnisa
- S. M. Hafeez Zaidi
- Sahar Zahoor
Institutions
- University of the Punjab (PK)
- Muhammad Nawaz Shareef University of Agriculture (PK)
- Institute of Southern Punjab (PK)
- University of Management and Technology (PK)
Publication Details
- Journal
- Review of Applied Management and Social Sciences
- Published
- 2024-12-06
- DOI
- https://doi.org/10.47067/ramss.v7i4.411
- Citations
- 1
- Primary Topic
- Gender Studies in Language
- Type
- article
- Field-Weighted Citation Impact
- 0.93