Scientific Evolution Through Computing: A Dual Lens on Social and Pure Sciences

Abstract

The accelerating integration of computational systems has catalyzed a fundamental transformation in the ways scientific knowledge is produced, validated, and disseminated. While pure sciences such as physics, biology, and chemistry have long embraced high-performance computing and algorithmic modeling to simulate complex phenomena, social sciences are increasingly adopting machine learning, agent-based modeling, and natural language processing to interpret dynamic human behaviors and societal trends. Despite this shared trajectory, the epistemological foundations, methodological challenges, and ethical implications of computational integration vary significantly across these domains.

This study investigates the evolution of scientific inquiry through the lens of computing, offering a comparative analysis between pure and social sciences. Drawing upon mixed methods with bibliometric analysis, case research on major systems (e.g., AlphaFold, NetLogo, LIWC), and cross-disciplinary epistemological mapping, the investigation illuminates aspects of computing that transform not only investigative instruments but also theory formation, evidence evaluation, and scientific explanation. A new framework for analysis is thereby introduced, which focuses on dimensions like interpretability, data dependence, scalability, and human judgment in computational inference, aimed at assessing the profundity and scope of the computational influence.

While these observations imply that computing constitutes both a prime convergence-inducing force and a source of domain-specific challenges, including transparency, reproducibility, and governance from an ethical standpoint, the research has contributed to a more integrative understanding of computational epistemology by situating these insights within more general philosophies of science as well as STS discourses. Hence, this duality enriches the field of interdisciplinary research practice and offers a strategy for science policy, funding, and schooling à la algorithmic discovery.