Digital Infrastructure and Developer Ecosystems: A Dual Dataset Framework for Cross-Domain Analysis of Technological Adoption
Hathairat Ketmaneechairat
International Journal of Web Applications, Vol. 18, No. 1, 2026.
DOI: https://doi.org/10.6025/ijwa/2026/18/1/13-24
Link: https://www.dline.info/ijwa/fulltext/v18n1/ijwav18n1_2.pdf
Abstract:
This study introduces a dual dataset framework for analyzing the co-evolution of macro level digital
infrastructure and micro level developer ecosystems. We integrate 45 years (1980-2020) of country level
telecommunications indicators spanning mobile penetration, internet adoption, and broadband diffusion
across 217 economies with a contemporary snapshot of 1,247 trending GitHub repositories, capturing realtime
developer attention across 40+ programming languages. Through fork to star ratio analysis, we identify
distinct behavioral archetypes: research oriented languages (R, Jupyter Notebook) exhibit 4-13x higher
forking rates (median ratios 0.29-0.38), reflecting academic reuse patterns for method adaptation and
reproducibility, while infrastructure languages (Rust, TypeScript) demonstrate star-dominant engagement
(ratios <0.04), indicating production tool consumption via stable APIs rather than source modification.
Critically, we document the emergence of compositional language ecosystems where polyglot architectures
deliberately stratify languages by computational layer Rust for systems safety, Python for AI orchestration,
and TypeScript for interfaces moving beyond "language wars" toward purpose driven symbiosis. These
patterns reveal programming languages function not merely as syntactic tools but as socio technical
coordination mechanisms governing community behavior. The framework enables novel cross domain
inquiries linking national infrastructure quality to global innovation patterns, with implications for digital
policy design and open source ecosystem stewardship.