Innovative Industrial Clusters in the Context of Digitalization and Sustainable Competitiveness
Bibliographic record
Abstract
To ensure Russia's sustainable competitiveness, it is necessary to create an infrastructure that allows creating globally competitive technologies and products. The authors suggest that industrial clusters can drive the country’s sustainable economic and innovative development. The research methodology is based on the content of the concept of sustainable competitiveness and cluster theory. The study was carried out using the methods of regression analysis. The systematization of foreign and Russian researchers’ ideas contribute to the conclusion that the cluster approach for ensuring sustainable development and competitiveness in the digital era is regarded as absolutely reasonable. In order to test the hypothesis of the study using regression analysis, a model has been built to assess the dependence and influence of the number of clusters in Russia Federal Districts on the main indicators of economic innovative development of these territories. The regression models constructed by the authors demonstrate a clear dependence of region economic and innovative development indicators on the clustering level. The authors of the present research recognize that clustering should be considered one of the basic elements in the system of national and regional sustainable competitiveness.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".