Globalisation of knowledge: Paradoxes of openness, unequal access and new horizons of scientific knowledge

Serhii Matiash
Abstract

The globalisation of scientific knowledge is one of the key processes shaping the transformation of contemporary science under conditions of digitalisation, the development of network infrastructures, and the implementation of open science policies. The study of knowledge globalisation is particularly relevant in the context of the rapid growth of digital technologies, the expansion of openness in scientific research, and the emergence of new forms of epistemic inequality that transform the contemporary scientific landscape. The purpose of this study was to analyse the paradoxical nature of knowledge globalisation and to identify the mechanisms through which epistemic inequality was formed in the context of digital and networked science. The methodological framework of the research was based on a complex of scientific analytical methods. The study employed phenomenological analysis, the historical and analytical method, the interpretative methodology of social epistemology, critical discourse analysis, and a structural and functional approach. The results included an analysis of the interaction between openness, digitalisation, and the global scientific infrastructure, and the identification of “shadow zones” – algorithmic, economic, and political mechanisms of unequal access to knowledge. It was shown that artificial intelligence has emerged as a new epistemic agent that simultaneously expanded research capabilities and deepened technological and infrastructural imbalances. It was generalised that open access, linguistic asymmetry, and algorithmic selection have formed new models of epistemic visibility and neo-colonial forms of knowledge production. The role of cultural diversity as a resource for inclusive global science was clarified, and the need for an ethics of transparency was emphasised. The practical significance of the study lay in the fact that its results can be used to shape open science policies, develop inclusive infrastructures, regulate digital platforms ethically, and enhance epistemic justice in the global scientific space

Keywords

Open Science; epistemic inequality; digital epistemology; intersubjectivity; communicative trust; cultural richness; ethics of transparency

Suggested citation
Matiash, S. (2026). Globalisation of knowledge: Paradoxes of openness, unequal access and new horizons of scientific knowledge. Humanities Studios: Pedagogy, Psychology, Philosophy, 14(1), 102-114. https://doi.org/10.31548/hspedagog/1.2026.102
References
  1. Anderson, W. (2020). Decolonizing histories in theory and practice: An introduction. History and Theory, 59(3), 369-375. doi: 10.1111/hith.12164.
  2. Bhambra, G.K. (2021). Decolonising critical theory?: Epistemological justice, progress, reparations. Critical Times, 4(1), 73-89. doi: 10.1215/26410478-8855227.
  3. Biagioli, M. (2022). Ghosts, brands, and influencers: Emergent trends in scientific authorship. Social Studies of Science, 52(3), 463-487. doi: 10.1177/03063127221095046.
  4. Borgman, C.L. (2023). Knowledge infrastructures: The invisible foundation of research data. Retrieved from https://escholarship.org/content/qt5wc9v7cf/qt5wc9v7cf.pdf.
  5. Bowker, G.C. (2018). Sustainable knowledge infrastructures. In N. Anand, A. Gupta & H. Appel (Eds.), The promise of infrastructure (pp. 203-222). New York: Duke University Press. doi: 10.1515/9781478002031-010.
  6. Castells, M. (2021). From cities to networks: Power rules. Journal of Classical Sociology, 21(3-4), 260-262. doi: 10.1177/1468795X211022054.
  7. Chatterjee, I., Kunwar, J., & den Hond, F. (2019). Anthony Giddens and structuration theory. In S. Clegg & M.P. Cunha (Eds.), Management, organizations and contemporary social theory (pp. 60-80). London: Routledge.
  8. Couldry, N., & Mejias, U.A. (2023). The decolonial turn in data and technology research: What is at stake and where is it heading? Information, Communication & Society, 26(4), 786-802. doi: 10.1080/1369118X.2021.1986102.
  9. Crawford, K. (Ed.). (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press. doi: 10.12987/9780300252392.
  10. Dent, C.M. (2025). Trade risk society – understanding trade policymaking in the 2020s. Social Sciences, 14(6), article number 338. doi: 10.3390/socsci14060338.
  11. Floridi, L. (2023). The ethics of artificial intelligence: Principles, challenges, and opportunities. Oxford: Oxford University Press. doi: 10.1093/oso/9780198883098.001.0001.
  12. Floridi, L. (Ed.). (2021). Ethics, governance and policies in artificial intelligence. London: Springer. doi: 10.1007/978-3-030-81907-1.
  13. Fuller, S. (2018). Post-truth: Knowledge as a power game. London: Anthem Press.
  14. Galison, P., & Newman, W.E. (2021). Interview with Peter Galison: On method. Technology|Architecture + Design, 5(1), 5-9. doi: 10.1080/24751448.2021.1863659.
  15. Goldman, A.I. (2021). How can you spot the experts? An essay in social epistemology. Royal Institute of Philosophy Supplement, 89, 85-98. doi: 10.1017/S1358246121000060.
  16. Habermas, J. (2022). Reflections and hypotheses on a further structural transformation of the political public sphere. Theory, Culture & Society, 39(4), 145-171. doi: 10.1177/02632764221112341.  
  17. Jasanoff, S., & Simmet, H.R. (2021). Renewing the future: Excluded imaginaries in the global energy transition. Energy Research & Social Science, 80, article number 102205. doi: 10.1016/j.erss.2021.102205.
  18. Latour, B. (2021). The anthill and the beam: A response to Elden. Dialogues in Human Geography, 11(2), 200-202. doi: 10.1177/20438206211001033.
  19. Leonelli, S. (2020). Scientific research and big data. In E.N. Zalta, U. Nodelman, K. Allen, H. Kim & P. Oppenheimer (Eds.), Stanford encyclopedia of philosophy. Stanford: Stanford University.
  20. Mastrokola, F., & Cernoiu, E. (2023). Epistemological frontiers: Examining Kuhn's paradigms and Popper's falsificationism in the arena ideals. In International multidisciplinary scientific conference on the dialogue between sciences & arts, religion & education (pp. 48-54). Târgoviște: Ideas Forum International Academic and Scientific Association. doi: 10.26520/mcdsare.2023.7.48-54.
  21. Nowotny, H. (2021). In AI we trust: Power, illusion, and control of predictive algorithms. Hoboken: Wiley.
  22. Smith, L.T. (2021). Introduction to the third edition. In Decolonizing methodologies: Research and indigenous peoples (pp. 11-33). London: Zed Books. doi: 10.5040/9781350225282.0004.
  23. Turek, K. (2025). Accelerating social science knowledge production with the coordinated open-source model. Quality & Quantity, 59, 767-795. doi: 10.1007/s11135-024-02020-7.
  24. UNESCO. (2021). UNESCO recommendation on open sciencedoi: 10.54677/MNMH8546.