This article presents a societal level cross-cultural analysis that explores the relationship between division of labor and stratification, as well as the effect that information systems and transportation have on this relationship. It is reasoned that social stratification and political hierarchy, as integrative forces are, in part, a function of the division of labor, information systems and transportation. The analysis finds a positive relationship between occupational specialization and political and economic inequalities in 186 small-scale societies, as well as an increased centralization of political and economic power as the cost of exchanging information and resources decrease resulting from innovation in information systems and transportation. In other words, concordant with the conflict view of technology, information systems and transportation innovations seem to have historically benefited mostly the individuals at the top of these political and economic stratification systems as they have an amplifying effect on the division of labor and stratification relationship. The results are discussed in terms of understanding the role that information systems, and transportation play in the de-centralizing or centralizing of resource as well as the decision-making. The article closes with a discussion of possible implications for contemporary societies.

This dissertation presents an investigation into the structural dynamics of scientific semantic networks, examining these networks across 16 scientific fields at three distinct levels of analysis: individual terms, communities of terms, and broader fields. Utilizing Exponential Random Graph Models (ERGM), the research explores critical local structures within these networks, comparing the semantic structures of the hard and soft sciences. Contrary to expectations, the findings reveal minimal differences between these disciplines, although some notable structural features are identified. The analysis extends to track the evolution of term communities over time, employing Singular Value Decomposition (SVD) to identify core and peripheral semantic communities. Again, contrary to expectations, the findings reveal reveal minimal differences between different types of fields. Finally, the study delves into the genealogy of these semantic communities, exploring patterns of split and merge events that contribute to the formation of semantic lineages. The findings reveal patterns of semantic inheritance, with some lineages enduring over long periods, others fading or disappearing, some undergoing division, and others merging or being subsumed under new terminological frameworks. Additionally, certain terms remain isolated, demonstrating the varied trajectories of semantic evolution within these networks. The insights garnered from this research not only shed light on the static and dynamic aspects of scientific semantic networks but also contribute to a deeper understanding of the interdisciplinary similarities and evolutionary trends in scientific terminology.