LINGUISTIC REPRESENTATION: SPEAKER’S LEXICAL CHOICE TO OVERCOME INEQUALITY

Abstract

Linguistic representation is the representation of standard linguistic entities. Dominant linguistic representations are challenged by exposing power associations at play. Linguistic inequality is in relation with linguistic individuality and is customized by lexicon confirmed by others. Most of the linguistic inequality seems in written discourse. In written discourse, every single used word shows the language culture of that individual.  Linguistic representation in autobiographical notes refers to the acknowledgement, narrative and deliberation of gender(s) in politics. This research explores representation of gender based language differences to overcome linguistic inequality. The research uses Linguistics Inquiry and Word Count (LIWC) 2022 to discover how the language in written discourse physiques and redirects beliefs about gender and affects power dynamics in a society. The study aims at identifying how speaker’s lexical choice influences on the language to neutralize the linguistic inequality in Pakistani context and to identify how gender and social positioning influence narrative voice in political and personal autobiographies. As this study applied LIWC and SPSS to code and categorize all of the original raw data from the autobiographies of female authors like Benazir Bhutto’s Daughter of the East (1998) and My Feudal Lord (1991) by Tehmina Dolatana and of male authors as Imran Khan’s Pakistan: A Personal History (2011) and In The Line Of Fire: A Memoir (2006) by Pervaiz Musharraf performed primary analysis on the subsequent measures. The sample books were provided with a particular context for the reliability of the linguistic style. So, the sample was aggregated as text file per author, per context. The aggregation process generates 2 text files with 112607 and 111088 words count by two male and same number of female authors that represent same traditional gender roles. Standard deviation (SD) and Cohen’s d (effect size) are used to measure variation and the magnitude of differences respectively. The researcher sets certain categories such as personal pronouns (ppron), social words, affective processes, lexical diversity, gender reference, friend, family, and pronoun which were analyzed to capture how lexical choices reflect efforts to neutralize gender inequality. For the second objective about speaker’s lexical choice, the researcher took dimensions of LIWC such as first person singular, first person plural, second person, analytic thinking, clout, authenticity and emotional tone to visualize graphical demonstrations of differences across variables. The statistical findings underscore the multidimensional nature of autobiographical writing across gender and context. The large effect sizes indicate meaningful differences in linguistic focus, tone, and structure. The results contribute to understanding how gender and social positioning influence narrative voice in political and personal autobiographies. The future research may also want to do psycholinguistic analysis of the autobiographical notes of male and female writers using LIWC tool of analysis. This study has practical implications in various fields such as in journalism and editorial writing to overcome gender inequality and therefore may be able to lower linguistic prejudices in their materials.