The Socio-Economic Role of Entrepreneurial Universities in Development of Innovation-Driven Clusters: The Russian Case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, Russia has to build its foreign policy in the difficult conditions of aggravation of internationalrelations with the traditional economic partners, imposing sanctions on leading domestic enterprises andrestricted access to resources such as capital in world markets. Of course, all these factors have the negativeimpact on the Russian economy as a whole. So it requires rapid business-process reengineering in the existingeconomical system and more effective organization of domestic industry. Restriction on actions in accustomedmarkets, in familiar environment provokes a pre-crisis situation. It creates a strong motivation to innerdevelopment of the national economy, commitment to internal business needs and diversification of priorities forlong-term cooperation. So, it is very important for Russia to find the effective tools of real socio-economicalimprovements in home markets and revitalize its business climate. The good alternative to raw-based orientationis advancement of manufacturing industry and high-tech production. Unfortunately, it happens not so often inmany brunches of Russian economy. For an isolated case to become a national trend, it is necessary to create anintertwined system of stable relations between enterprises and institutional organizations in different regions ofthe country. This article analyzes the prospects for creation of regional innovation-driven clusters in specificRussian conditions. The special role in formation of such clusters belongs to entrepreneurial universities, whichare not only able to generate new technologies and innovative products, but also serve as a source of institutional,organizational, cultural and communication innovations that are useful for the business community.
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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 | 0.000 |
| 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 | 0.001 |
| 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 it