@article{4776,
  author = {K.Kiruthika},
  title = {Demographic Inequality and Linguistic Diversity in Indian States and Union Territories},
  journal = {International Journal of Computational Linguistics Research},
  year = {2026},
  volume = {17},
  number = {3},
  doi = {https://doi.org/10.6025/ijclr/2026/17/3/133-150},
  url = {https://www.dline.info/jcl/fulltext/v17n3/jclv17n3_1.pdf},
  abstract = {India's multilingual landscape and uneven demographic distribution present unique challenges for
governance, yet few studies integrate these dimensions at the sub-national level. This study proposes an
integrated analytical framework to examine demographic inequality and linguistic diversity across India's
36 States and Union Territories. The acquired dataset encompasses administrative identifiers, socioeconomic
metrics, and widely spoken languages. Demographic concentration is evaluated using the Gini coefficient,
Lorenz curve, cumulative distribution, and Pareto analysis. Concurrently, linguistic heterogeneity is quantified
through lexical diversity indices, including the Shannon and Simpson Diversity Indices, Type Token Ratio,
Herdan's C, and Pielou's Evenness. Additionally, network-based visualizations, such as state language
bipartite networks and co-occurrence maps, elucidate structural patterns of multilingual coexistence. Results
reveal pronounced demographic inequality (Gini = 0.6149), confirming that a small subset of highly populated
states accounts for the vast majority of the national population. In stark contrast, linguistic analysis identifies
43 distinct languages exhibiting high lexical richness and equitable distribution, demonstrating minimal
dominance by any single language. These findings highlight a critical divergence between concentrated
demographics and broadly distributed linguistic ecosystems. Ultimately, this reproducible computational
framework provides policymakers with vital insights for evidence-based governance, equitable resource
allocation, educational planning, and language preservation, demonstrating how computational analytics
can support the study of complex socio-demographic systems.},
}