Analysis of Russia and other Countries Economic Parameters and Their Connection with the Development of Science Parks
Bibliographic record
Abstract
Economic growth factors in different countries have their own special resources and features due to the difference in development process and environment structure. The authors analyzed the influence of the science parks upon the economic indicators on the example of Russia. Although there are organizations in Russia created to support the science parks creation, existence and development, there is no efficient and common mechanism to support functioning of the science parks and to make it become oriented at the final result (improving the growth of the country’s economy). The article contains an attempt for the analysis and estimation of the Russia’s economy growth possibilities because of the science parks’ factor, the main of which is the creation of the comfortable conditions for the establishment and development of start-ups, smooth work of the small innovative and other organizations. The analysis of the Russian economy indices is performed on the basis of the statistics data of the Russian Statistics Bureau published during 1995–2012. The regressive dependencies, contained in the work and built according to the real statistics data, can provide some data, connected with the extensive component of the science parks factor effect (increase of the number of innovations, created because of the SP factor), such as the maximum possible Russian GDP in 2010, if the science parks supported all small businesses, GDP value expected in 2015 if the situation with the science parks is not changing and if the science parks fully support all small businesses and start-ups etc.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".