ROLE OF CLUSTER FORMATIONS IN THE DEVELOPMENT OF THE SPATIAL ECONOMY
Bibliographic record
Abstract
The article is devoted to analysing cluster formations as a new, unique factor in increasing the competitiveness of modern territorial systems. The authors conducted a comprehensive study of the theoretical and methodological foundations of the spatial-cluster organisation of the innovative development of the socio-economic system. We consider various approaches to the study of the essence of clusters to identify their characteristic features and determine the effects generated by them. The article analyses the fundamental scientific works of well-known economists devoted to relevant cluster topics, with a retrospective reflection of the laws and regularities revealed by them on the actual, current field of existing socio-economic problems of the territorial economy. It is noted that, in the conditions of globalisation where market forces dominate, ‘soft’ forms of integration interactions of business entities will be required, so the role of spatially localised forms of integration associations will increase. The mechanism of their emergence and evolution is of fundamental importance for separating clusters of industrial enterprises from other forms of association of economic entities. We highlight the general and specific features of clusters. By a cluster, the authors understand a set of independent, innovatively active organisations connected by territorial proximity and functional dependence. Signs of a cluster are territorial proximity, a critical mass of organisations, a high density of connections between organisations, and a high level of innovative activity. Economic clusters are highly complex multidimensional system objects, so unambiguously assigning them to a specific taxonomic type is impossible. The authors propose a definition of the concept: a cluster is an open complex system with a hierarchical, orderly structure, the basis of which is a voluntary association of firms that are interdependent and, at the same time, retain autonomy and successfully compete with each other, which leads to increased competitiveness not only of its participants but also of a particular territorial unit. We conclude that thanks to the characteristic features of clusters and the synergistic effects that arise in them, the competitiveness of individual cluster participants and the cluster as a whole, as well as the innovative level of development of the territorial system, increases. Keywords: cluster, cluster formations, localised economic system, innovation development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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; a candidate call from one teacher head, not a consensus.
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".