Analysis of the State of the Russian Economy and Prospects for its Development During the COVID-19 Pandemic
Bibliographic record
Abstract
At present, during a pandemic, an important role is played by the state and prospects for the development of the Russian economy. An increase in the level of economic development creates conditions for the stability of the national economic system, and also provides the foundation for systematic economic growth, even during a pandemic. The Russian economy is characterized by uneven economic development. So, in 2020, the Russian economy has undergone a severe crisis caused by the corona virus pandemic. The difficult epidemiological situation provoked several notable shocks that hit the economy, especially in certain sectors. In 2020, there was a decrease in Russian GDP by 3.1%. Let us turn to the level of Russia’s GDP for the first half of 2021, it increased by 4.6% in relation to the first half of 2020. According to the Ministry of Economic Development, in June, Russia's GDP reached the level of the fourth quarter of 2019. It should be noted that the economic component plays a crucial role in any state, since all spheres of human activity are linked by economic relations. In our opinion, the development of theoretical provisions and recommendations on promising areas of development of the Russian economy will allow the state to create a powerful economic base necessary for a stable and sustainable development of the economy.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".