This paper addresses the issue of institutional heterogeneity across the regions of the Russian Federation as a major barrier to balanced innovationdriven development. The need for a transition to typologically differentiated models of spatial regulation is substantiated. The methodological framework of the study is based on cluster analysis, implemented using Kmeans and agglomerative hierarchical clustering (complete linkage), as well as regression modeling and machine learning techniques. Additional methods include determination of the optimal number of clusters (elbow method, silhouette analysis), feature standardization, and Random Forest classification to identify the most significant determinants of cluster structure. Key indicators comprise investment activity, intramural R&D expenditures, the number of small enterprises per capita, the volume of innovative products, and characteristics of scientific and technological development. As a result, five stable regional clusters were identified, each differing in terms of institutional maturity and innovation potential. The clusters were interpreted, and targeted development strategies were formulated for each group. Instruments for intercluster knowledge and policy transfer are proposed, including the development of network platforms, educational consortia, and the adaptation of best governance practices. The study lays the foundation for a systematic, adaptive, and empirically validated model of strategic planning in the context of achieving technological sovereignty and enhancing the effectiveness of regional policy.
Зайцев et al. (Wed,) studied this question.
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