Comparative Study of Mythological Women in the Fiction of Chitra Banerjee Divakaruni and Kavita Kane for Gender Inclusive Learning

Abstract

This study develops a computationally grounded comparative framework to examine mythological female representations in the fiction of Chitra Banerjee Divakaruni and Kavita Kane, addressing a measurable gap in gender-inclusive literary pedagogy. A corpus-driven analysis integrating natural language processing, sentiment modelling, and narrative agency indexing reveals that only 28% of prior curriculum-linked studies quantify female narrative centrality. The findings indicate statistically significant differences (p < 0.05) in agency distribution, voice intensity, and resistance constructs across the two authors’ works. Divakaruni’s narratives demonstrate higher dialogic plurality, while Kane’s texts exhibit deeper internalized agency reconstruction. The study proposes a novel Gender-Inclusive Literary Analytics Model (GILAM) that operationalizes inclusivity through quantifiable discourse metrics. This model enables scalable curriculum integration, offering a replicable methodological contribution to digital humanities and gender studies, and advancing evidence-based inclusive learning design.