Entrepreneurial Activity Self-Production Conditions within Territorial Clusters
Bibliographic record
Abstract
The role of regional entrepreneurship is becoming the key point when forming Russian economy effective competitiveness and especially in terms of current world economic challenges, which determines Russian economy turbulence. The current research focuses on self-production conditions of these territorial systems clusters. A cluster’s formation based on its members’ self-production is thoroughly investigated in the research. The authors analyze clusters, their functions, and tasks definitions of economic analysis. The features of various territorial-production systems of the Russian Federation are considered in the article. Clusters competitive nature is clarified on the grounds of the analysis by using various resources and combinations of factors. An algorithm for forming business self-production conditions within a cluster is defined in the research. The research provides the analysis results of cluster business self-production formation conditions. The key integrating resource, which plays the role of a moving force for development of other resources that are necessary for forming business self-production conditions within a cluster, is elaborated in the article. On the basis of economic territorial systems with self-production features functioning analysis, the authors suggest a new economic approach to business system development by applying new cluster organization forms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".