Prospective Mechanisms of Peripheral Areas Investment and Innovation Potential Formation
Bibliographic record
Abstract
In this study conducted a comprehensive study of investment and innovation processes in conjunction with the problems of balanced economic development mesa-level, modify the basic economic proportions and the speaker on the one hand, the factor, and the other - the result of a regional self-development and interregional cooperation, is of particular relevance is the backbone target benchmark regional social and economic policy. A very need innovation-oriented development of the regional investment potential of paramount importance for Russia as a whole, and for its regional components, the prospects of dynamic development which largely involve the presence of an effective, articulating the functional components and hierarchical levels, the mechanism of activation of innovation-oriented investment and support of innovative activity of economic entities. Last in the current economic realities becomes as important competitive advantage and sustainable operation of the main factors of economic and reproductive system of the region. The effectiveness of the efforts and actions towards the formation of a favorable investment environment and innovative qualities of the regional economy, which are understood as its ability to self-renew, change adaptation and generation of scientific and technological progress, sustainable development, production and maintenance of its competitiveness in the long term, depends not only on the available resource capabilities as the availability and effectiveness of regional investment mechanism regulating and coordinating the development of innovative sphere of the region.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".