This work addresses the pressing problem of demarcating scientific and non scientific knowledge in the context of the digital transformation of scientific activity. It argues that the effectiveness of classical demarcation criteria (verifiability, falsifiability), which are oriented towards static theories, is diminishing with respect to post-non-classical science, which functions as a distributed, technologically mediated, and interdisciplinary ecosystem. As a complement, the concept of a cluster-microservice architecture (CMA) is proposed as a representation of the organization of scientific knowledge and a methodological tool for its demarcation. The concept is developed through a synthesis of the philosophy of complexity (synergetics) by V.I. Arshinov, the principles of post-non-classical rationality by V.S. Styopin, social epistemology, actor-network theory, as well as the model of the generalized "activity tetrahedron" by V.G. Budanov. Within this model, the functional roles of the CMA elements are specified: the microservice acts as a transformer of data and knowledge flows, the cluster acts as an aggregator of results within a shared problem area, and the platform acts as a meta-aggregator, providing second-order observation and interoperability for the entire scientific landscape. The key result is a system of architectural criteria for scientific validity and demarcation, which evaluates knowledge based on the quality of the network organization of its production: interoperability, recursive verifiability, process observability, and resilience to anti-patterns (hyperspecialization, monopolization of interfaces). The article substantiates the transition of the philosophy of science from the role of a meta-discourse to the status of an active "epistemic microservice," integrated into the infrastructure of research platforms to ensure conceptual clarity and ethical reflection.
philosophy of science; post-non-classical rationality; synergetics; complexity; Vladimir Arshinov; demarcation; microservice architecture; clustering; scientific platforms; second-order observer.