THE SOCIALLY DISCONNECTED KNOWLEDGE PARADOX: AGENTIC SCIENCE, POWER, CRIMINAL GOVERNANCE, AND SOCIETAL VALUE

Mario Guadalupe López Ayala; Israel Osuna Flores

doi:10.32351/rca.v11.462

Authors

Mario Guadalupe López Ayala Autonomous University of the West https://orcid.org/0000-0001-5377-6257
Israel Osuna Flores Autonomous Indigenous University of Mexico https://orcid.org/0009-0001-0199-2983

DOI:

https://doi.org/10.32351/rca.v11.462

Política de Crossmark DOI: https://doi.org/10.32351/politica-crossmark

Keywords:

Socially Disconnected Knowledge Paradox, agentic artificial intelligence, scientific knowledge, authority–control–value disconnection, criminal governance, societal value

Abstract

The expansion of agentic artificial intelligence is transforming scientific discovery, yet growing scientific capabilities do not necessarily yield corresponding improvements in societal well-being. This article develops the Socially Disconnected Knowledge Paradox (SDKP) as a conceptual framework examining how the application and societal value of scientific knowledge depend on policy authority, legal entitlement, effective operational control, and institutional capacity. Integrating innovation studies, institutional economics, science policy, research impact assessment, and criminal-governance scholarship, SDKP introduces authority–control–value disconnection as its central construct. It distinguishes implementation disconnection, in which governance or resource constraints impede the use of scientifically supported knowledge, from distributional disconnection, in which an effective application does not deliver its intended breadth of benefits because of identifiable access or appropriation arrangements. The article formulates six conditional propositions, proposes an analytical protocol, and identifies directions for comparative research. Organized criminal interference is included only when evidence establishes its relevance to a particular scientific application. The proposed distinctions are conceptual and require independent empirical assessment.

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Author Biographies

Mario Guadalupe López Ayala, Autonomous University of the West

Mexican university professor and researcher. He holds doctoral degrees in Educational Innovation and Administration and in Psychopedagogy. He is affiliated with Universidad Autónoma de Occidente and Universidad Autónoma de Sinaloa, Mexico. His scholarly work integrates human-centered artificial intelligence, socio-technical systems, applied psychology, educational innovation, and AI governance. His research examines the intersections of technology, cognition, human behavior, and institutional transformation, with particular attention to their social and ethical implications. He is an author and co-author of scientific publications, leads the Human-Centered AI & Socio-Technical Systems Lab, and develops research and science communication initiatives through Phoenix24, fostering knowledge generation and its transfer to society.
Israel Osuna Flores, Autonomous Indigenous University of Mexico

Dr. Osuna is currently a Professor-Researcher at the Autonomous Indigenous University of Mexico (UNAM), affiliated with the Biotechnology Engineering Program at both the Virtual Unit and the Mochicahui Unit. He recently received an honorary doctorate from the International Organization for Inclusion and Quality Education (OIICE). He has held various positions, including Administrative Analyst and Reviewer at the National Aquaculture and Fisheries Commission; advisor to the company Acuícola Gilberto, S.C. de C.V., R.L.; and columnist for the online newspaper Phoenix Medios. He is currently the Coordinator of the Biotechnology Engineering Program (Virtual Unit) at UNAM. He teaches courses in Biology, Social and Solidarity Economy, Introduction to Biotechnology, Community Engagement Awareness, and Information Management Tools. He was Coordinator of the Projects Department at the Technology Transfer Office of the Autonomous Indigenous University of Mexico and is a member of the Sinaloa System of Researchers and Technologists. He has also been a Professor-Researcher at the same institution, where he taught courses in the Bachelor's Degree in Forestry Engineering and Sustainable Development (Forest Entomology, Biodiversity, Thesis Seminar, Environmental Projection, and Project Formulation and Evaluation), as well as in the Graduate Program in Sciences in Sustainable Development of Natural Resources (Aquaculture Production, Environmental Pollution, and Ecotoxicology). He has directed and advised two undergraduate theses and monographs and three graduate theses and dissertations. He was a member of the Academic Group on Sustainable Development. His research experience focuses on adding value to agroforestry products, pollution, and environmental toxicology. He has published articles in JCR-indexed, peer-reviewed, and popular science journals. In addition, he participates in various scientific societies and has served as a project evaluator at events such as the Pacific Regional Science Fair and the National Science Fair. He was a member of the National System of Researchers (SNI) as a Candidate from July 2002 to December 2005 and received recognition from the CONACYT Repatriation Program in 1999. His professional experience includes leading several research and outreach projects funded by COSNET and SECIHTI, as well as holding various academic and administrative positions, including: Head of the Toxic Waste Laboratory at the Center for Research in Food and Development, A.C. (Delicias Unit), and Director of Research at the Autonomous Indigenous University of Mexico (UNAM). He has been a candidate for Rector of UNAM in Los Mochis, Sinaloa, Mexico, on three occasions (2017, 2021, and 2025). He has also worked at the National Technological Institute of Mexico (Los Mochis Campus), the University of the Sea, the Autonomous University of the West, the University of Guadalajara and the Center for Research in Food and Development, A.C. (Delicias Unit), where he has developed research projects and taught courses related to Fisheries Biology, Fisheries Technology, Ecology, Zoology, Aquaculture, Environmental Toxicology and Ecotoxicology.

References

Bianchini, S., Di Girolamo, V., Ravet, J., & Arranz, D. (2026). AI in science: When and where it makes a difference. Research Policy, 55(6), Article 105478. https://doi.org/10.1016/j.respol.2026.105478

Bianchini, S., Müller, M., & Pelletier, P. (2025). Drivers and barriers of AI adoption and use in scientific research. Technological Forecasting and Social Change, 220, Article 124303. https://doi.org/10.1016/j.techfore.2025.124303

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553

Di Cataldo, M., & Mastrorocco, N. (2022). Organized crime, captured politicians, and the allocation of public resources. The Journal of Law, Economics, and Organization, 38(3), 774–839. https://doi.org/10.1093/jleo/ewab015

Farrell, H., & Newman, A. L. (2019). Weaponized interdependence: How global economic networks shape state coercion. International Security, 44(1), 42–79. https://doi.org/10.1162/isec_a_00351

Feng, B., Walther, P., Zhang, L., Coyle, R. A., Kim, S., Mandal, A., Zhang, Z., Papidocha, S. M., Kelmanson, A., Segura, D. M., Thakur, S., Pal, S. C., Bernales, V., Gao, W., Yang, X., Cong, L., Schwaller, P., Aspuru-Guzik, A., & Zheng, Z. (2026). Agentic AI systems for scientific discovery. Nature Reviews Methods Primers, 6, Article 81. https://doi.org/10.1038/s43586-026-00534-y

Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Research Policy, 31(8–9), 1257–1274. https://doi.org/10.1016/S0048-7333(02)00062-8

Ghareeb, A. E., Chang, B., Mitchener, L., Yiu, A., Szostkiewicz, C. J., Shved, D., Gyimesi, G. J., Laurent, J. M., Wright, S. M., Razzak, M. T., White, A. D., Finnemann, S. C., Hinks, M. M., & Rodriques, S. G. (2026). A multi-agent system for automating scientific discovery. Nature, 655, 497–505. https://doi.org/10.1038/s41586-026-10652-y

Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10, 18–26. https://doi.org/10.1007/s13162-020-00161-0

Jasanoff, S. (Ed.). (2004). States of knowledge: The co-production of science and the social order. Routledge.

Laplane, A., & Mazzucato, M. (2020). Socializing the risks and rewards of public investments: Economic, policy, and legal issues. Research Policy: X, 2, Article 100008. https://doi.org/10.1016/j.repolx.2020.100008

Lazonick, W., & Mazzucato, M. (2013). The risk–reward nexus in the innovation–inequality relationship: Who takes the risks? Who gets the rewards? Industrial and Corporate Change, 22(4), 1093–1128. https://doi.org/10.1093/icc/dtt019

Lessing, B. (2021). Conceptualizing criminal governance. Perspectives on Politics, 19(3), 854–873. https://doi.org/10.1017/S1537592720001243

Miao, J., Davis, J. R., Zhang, Y., Pritchard, J. K., & Zou, J. (2026). Reimagining research papers as interactive and reliable AI agents. Nature, 658, 1272–1280. https://doi.org/10.1038/s41586-026-11044-y

North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. https://doi.org/10.1017/CBO9780511808678

Ostrom, E. (2010). Beyond markets and states: Polycentric governance of complex economic systems. American Economic Review, 100(3), 641–672. https://doi.org/10.1257/aer.100.3.641

Spaapen, J., & van Drooge, L. (2011). Introducing ‘productive interactions’ in social impact assessment. Research Evaluation, 20(3), 211–218. https://doi.org/10.3152/095820211X12941371876742

Sutton, R. I., & Staw, B. M. (1995). What theory is not. Administrative Science Quarterly, 40(3), 371–384. https://doi.org/10.2307/2393788

Sørensen, O. H., Jaspers, S. Ø., Garn, S. D., & Nielsen, S. B. (2026). Theorizing societal impact of research: A theory of change for societal impact of research with explicated assumptions. Research Evaluation, 35, rvag043. https://doi.org/10.1093/reseval/rvag043

Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305. https://doi.org/10.1016/0048-7333(86)90027-2

United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence. https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence

Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495. https://doi.org/10.5465/amr.1989.4308371

World Bank. (2025). Digital progress and trends report 2025: Strengthening AI foundations. World Bank. https://www.worldbank.org/en/publication/dptr2025-ai-foundations

World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. https://www.who.int/publications/i/item/9789240029200

World Health Organization. (2025). mRNA technology transfer programme. https://www.who.int/initiatives/mrna-technology-transfer-(mrna-tt)-programme

Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203. https://doi.org/10.5465/amr.2002.6587995

Zhang, Y., & Wei, S. (2026). Organized crime and firm innovation: The moderating effects of business group affiliation and export intensity. Journal of International Management, 32(2), Article 101343. https://doi.org/10.1016/j.intman.2026.101343