IMPACT OF THE PANDEMIC ON THE RUSSIAN ECONOMY AND POPULATION INCOME IN 2020
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
The article presents dynamics of the coronavirus infection in Russia and analysis of the situation in the national economy and population living standards amid the COVID-19 pandemic. Investigation of the socio-economic situation in the country was performed on the Rosstat preliminary data for 2020. The economy has suffered serious losses from the COVID-19 resurgence already in the second quarter: GDP in constant prizes was only 92% as compared to the corresponding period of 2019, budget revenues reduced at all levels, unemployment increased from 4,7% to 6%. This was immediately reflected in the indicators of well-being. Thus, for example, nominal per capita monetary income of the RF population reduced to 94.6%, and real — to 91,7% against the second quarter of the previous year. Owing to the Government measures to curb the spread of the virus, to provide assistance to business and citizens most affected by the pandemic, the situation began to gradually improve ealready in the third quarter. It is shown in the article that the second, stronger wave of COVID-19, which began in mid-September, did not allow to radically change the socio-economic situation in the country until the close of the year, as follows from the statistics for October-December 2020. The authors make a conclusion that Russia has managed to avoid a deep crisis. They provide a comparative analysis with the crisis situation of 2016. The pandemic will continue affecting the economy and living standards of the Russian population in 2021. Already at the beginning of the year, the RF Government took a series of measures to support the economy and population and to overcome the negative consequences of the year 2020.
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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