IMPACT OF THE PANDEMIC ON THE RUSSIAN ECONOMY AND POPULATION INCOME IN 2020
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.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".