Understanding creative, information and knowledge determinants of the economic growth of the EU regions within smart development strategies
Bibliographic record
Abstract
The article substantiates the relevance and necessity of involving creativity, information and knowledge-based capital while forming and implementing the smart specialization policy of the EU regions. The scientific views on the relationship between the processes of economic growth, the use of creative, information and knowledge approaches, smart-oriented spatial and territorial planning are generalized. A new approach for assessing the creative, information and knowledge determinants of the EU regions’ economy transformations with the use of the multivariate regression analysis, a composite method, and strategic structural and functional design is developed. The scores of the sub-indices of the Global Innovation Index, the Global Talent Competitiveness Index and the World Digital Competitiveness Ranking are selected as the initial parameters of regression analysis. The relationships between these factors and the change in the GDP volume per capita, the share of GDP used for gross investment, high-tech exports, and the Global Quality of Life Index are revealed. The composite indicators of the concentration of creative and digital (ICT) industries in the EU regions are calculated (based on the level of enterprise concentration in an industry, the share of the employed in the field and the share of an industry in the regional economy in terms of wages). The priorities of smart specialization strategies of the EU’s individual regions, which are related to creative, information and knowledge factors, are identified. The calculations have confirmed sufficient closeness of the relationship between the use of creative, information and knowledge factors and the fulfillment of the tasks of smart specialization strategies in the EU regions. The sequence of the formation of tools and means for the implementation of the strategy of the regions’ smart specialization in the context of attraction and effective use of the determinants grouped by three directions (creativitization, digitalization and new knowledge) is presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".