The Socio-Economic Role of Entrepreneurial Universities in Development of Innovation-Driven Clusters: The Russian Case
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".