Стимулирование инновационной деятельности промышленного производства в условиях выхода российской экономики из кризиса
Bibliographic record
Abstract
The article considers the question of state support for innovation and supporting industries in exit time of Russian economy from the financial crisis. The state policy in economic recovery, aimed at supporting innovative activities of industrial enterprises is one of the components of the moments going out of the recession. Stimulation of innovations enterprise provides many benefits. The country's economy through the financial support by consistent steps goes to pre-crisis level. In 2010, economic recovery, that began in the second half of 2009, continued. After the end of recession, which lasted four quarters in a row in the second half of 2008 and the first half of 2009, when Russia's economy has declined by 11 %, economic growth continued through the next four quarters. By the second quarter of 2010 almost half of the recession was offset. GDP in the II quarter 2010 was by 5.2% above the level of the II quarter of 2009 the maximum point of the recession. Recovery growth occurred in the sectors most affected by the crisis in the manufacturing industry, focused on investment demand the machine-building industries. Drought and fires led to a pause in economic growth in the third quarter. In July, according to the Economic Development of Russia's GDP fell from seasonally adjusted 0.4 % and overall growth in January-July is estimated at 3.9 %. However, overall trend in the recovery in the coming months, is scheduled to resume. It will be supported by growth in consumer demand, recovery stocks, and expected by year-end increase in investment activity. This allows us to estimate the annual GDP growth of around 4 percent. At the same time, there remain risks associated with the reaction of the economy at large-scale losses in agriculture, continued stagnation in the construction and weak investment demand, which can limit the growth of 3.5-3.7 percent in 2010. The main factors of economic growth in the late last year were increase of exporters' revenues and the revival of consumer demand, supported by the improvement in the labor market. State policy has played a huge role in supporting the economy.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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; both teacher heads agree on what is shown here.
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".