The Influence of Regional Elites on the Optimization of Intraregional Space in a Federal State
Bibliographic record
Abstract
The relevance of this study stems from the need to find effective models for managing spatial development in federal states, where balancing the interests of the center and the regions is a key challenge. Despite the extensive body of research devoted to various aspects of federalism, the influence of regional elites on spatial optimization processes in rapidly changing political and economic conditions remains underexplored. This article aims to identify the key factors, mechanisms, and models of regional elite influence on spatial development policy in a federal state. The study utilizes a combination of methods: historical and genetic analysis to identify the evolution of the role of elites, an institutional approach to studying the formal and informal practices of their interaction with the center, and a comparative analysis to compare case studies from the Russian Federation, the United States, Germany, Canada, Brazil, and Nigeria. The empirical basis of the study is comprised of sources including federal and regional regulatory acts, statistical data, analytical reports from governmental and non-governmental organizations, as well as materials from scientific conferences and publications in specialized journals. The study revealed that regional elites exert a dual influence on the spatial development of federal states. On the one hand, they can act as drivers of innovation, initiators of promising projects, and “growth points” contributing to increased regional competitiveness and improved quality of life. On the other hand, regional elites often become a source of conservatism, hindering necessary reforms and supporting outdated economic models. Furthermore, the clash of interests between various regional elites can lead to conflicts that hinder the implementation of national goals and objectives. Key factors determining the effectiveness of regional elites’ influence on spatial development include the mechanisms for their integration into decision-making systems (including party and advisory ones), the incentive system for regional leaders aimed at encouraging innovation and attracting investment, and the degree of elite circulation between the center and the regions, which ensures the exchange of experience and the dissemination of best management practices. The scientific novelty of this study lies in its comprehensive interdisciplinary analysis of the role of regional elites through the prism of three methodological approaches. The results of the study can be used by federal and regional authorities to improve interaction mechanisms, as well as by scholars and experts in the field of federalism and regional policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".