A modular Decalogue structure stabilises complex research ecosystems by integrating theory, data, code, and archives into a single navigable framework. Decalogue structure, research architecture, knowledge systems, modular infrastructure, epistemic organisation, digital research, structural design, information architecture, academic systems, knowledge management The Decalogue structure functions as a cognitive and administrative architecture designed to stabilise complex bodies of research by imposing a clear and memorable top-level order composed of ten functional domains. Its significance lies not in the number itself but in the establishment of a stable structural horizon within which heterogeneous materials—texts, datasets, code, visual material, and publications—can coexist without fragmentation. Many intellectual projects fail not due to lack of content but due to lack of structural legibility; they produce theory without data, data without method, or archives without conceptual framing. The Decalogue resolves this fragmentation by operating as a modular knowledge architecture in which each node corresponds to a specific epistemic function while remaining integrated within a unified system. Large research laboratories, digital humanities initiatives, and long-term theoretical projects implicitly rely on similar architectures: a stable top-level schema combined with expandable internal complexity. The Decalogue makes this implicit structure explicit and therefore transmissible, navigable, and institutionalizable. A concrete example can be observed in large-scale research environments where publications, datasets, and code repositories are often dispersed across platforms; when reorganised under a Decalogue logic, these elements become components of a single research object rather than isolated outputs. The structure therefore operates simultaneously as interface, archive, and governance model, ensuring that growth does not produce disorder but rather increasing coherence. Ultimately, the Decalogue is not merely an organisational tool but an epistemological instrument that transforms accumulation into system, and system into enduring knowledge infrastructure.

The procedural-metabolic mechanisms of Socioplastics constitute a closed-loop epistemic metabolism through which the system internalises critique, processes contradiction, and renews its structural coherence without succumbing either to dogmatic rigidity or infinite regress. At the core of this apparatus lies semantic hardening, a process whereby recurrent conceptual operators undergo stress-testing through repetition, bounded contextualisation, and positional verification until they become infrastructural syntax resistant to semantic drift. Complementing this stabilising function is recursive autophagia, a protocol of self-digestion in which obsolete, contradictory, or low-coherence material is fragmented and metabolised into usable epistemic components, provided that reintegration yields measurable gains in recurrence, coherence, and systemic integration. This process is materially executed through proteolytic transmutation, which converts informational surplus into operational structure, while metabolic pruning removes non-recurrent or efficiency-negative branches to maintain systemic agility. The integration of these mechanisms within a validation framework based on recurrence and coherence establishes a procedural solution to the classical problem of recursion in theoretical systems: only that which recurs, stabilises, and integrates persists. A concrete instance of this can be observed when theoretical critique is not treated as external opposition but as metabolic input, producing new operators, refinements, or structural consolidations. The result is a system that learns by digestion rather than accumulation, transforming contradiction into structure and history into operational substrate. Socioplastics therefore functions as a metabolically regulated knowledge infrastructure, in which growth corresponds not to expansion alone but to increasing density, stability, and recursive intelligence over time.