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Volume IV, Issue 1(7), 2025
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This paper presents two synthetic estimations of the Gini coefficient at a municipality level for Colombia in the years 2000-2020. The methodology relies on several machine learning models to select the best model for imputation of the data. This derives in two Random Forest models where the first is characterised by containing Dominant Fixed Effects, while the second contains a set of Dominant Varying Factors. Upon these estimations, the Synthetic Gini Coefficients for both models are inspected, and public links are generated to access them. The Dominant Fixed Effects models is rather “stiff” in contrast to the Varying Factor model. Hence, for researchers it is recommended to use the Synthetic Gini Coefficient with Varying Factors because it contains greater variability across time than the Dominant Fixed Effects models.
© The Author(s) 2025. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.
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This article aims to discuss the evolution over the centuries of the role and social position of those mastering the technologies of their time. We suggest that the Industrial Revolution, the rationalization of technical and managerial processes, then the rise of IT, the ascent of cryptocurrencies and finally the emergence of the neoliberal state have lifted a fringe of these individuals to the top of the social hierarchy. Among the “technology masters”, we distinguish three families: those who remain at the service of the State and the established order, those who have exploited, consciously or not, the withdrawal of the neoliberal State to offer services and innovations formerly assumed by the public sector, and finally those who have consciously taken advantage of this same withdrawal and the recognition they enjoy in society to propose other models (free software, open source, crypto anarchism, technical alternatives, etc.).
© The Author(s) 2025. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.
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The domination of cyberspace technologies in inter-human communications is obvious because of their ultra-rapidness and enormous data capacity. Human-intensive use of cyberspace increased the magnitude of streamed data through its nodes, created by two sources: human users and AI. While humans can control their generated data, it proves impossible to control AI due to its super intelligence along with their self-developing abilities, enabling it to produce unlimited volumes of data. It is known that cyberspace depends on physical infrastructure, which is inherently limited. Despite investments to expand capacity, overloading this infrastructure with unlimited data creates critical functionality issues. Additionally, the presence of uncontrollable AI elements leads to unpredictable outcomes. Ultimately, this results in AI dominating cyberspace, a phenomenon known as cyber singularity.
The ultimate consequences of AI cyber singularity motivated the study to recall a similar phenomenon in astrophysics: gravitational singularity. Using general relativity theory, the research analyses the dilemma of data overload in cyberspace and its effects, drawing parallels between outer space and cyberspace. It aims to illustrate AI's acquisition of cyber singularity according to astrophysics laws on gravitational singularity, providing an innovative perspective for scientists and scholars studying cyberspace.
© The Author(s) 2025. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and URL DOI.
