THE RUSSIAN GOVERNMENT’S MEASURES TO SUPPORT THE RUSSIAN ECONOMY DURING COVID-19 CRISES
Bibliographic record
Abstract
The pandemic COVID-19 has plunged the world economy into the deepest recession since World War II. Despite additional policy support, GDP of world in 2020 decreased by 5.2 percent, then followed by an increase of 4.2 percent in 2021. The pandemic has deeply interrupted livelihoods, with the termination in nearly 500 million full-time jobs in second quarter of 2020 alone. And this situation pushed about 150 million people into poverty by 2021. The revival of pandemic is blowing out a shadow over the global restoration as countries are forced to pull tight social-distancing measures, but trust has been picked up by news that various vaccines have shown high effectiveness in clinical experiments. The pandemic is forecasting to have long-lasting scarring effects on productivity and prospective growth, as investment loses strength further and human capital accumulation slows for reason of prolonged school lock-downs and extended unemployment. Financing situations in Emerging Market and Developing Economies (EMDEs) have s amid gathering speed in COVID-19 cases. EMDEs suffering of higher debt burdens or financing needs are especially vulnerable to acute rise in borrowing costs and to restrictions in their access to financing. Foreign Direct Investment (FDI) flows to EMDEs dropped by about 32 percent in 2020 among stalling investment and poor corporate profit. Keywords: pandemic, fiscal assistance, government measures.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".