EFFICIENCY OF STATE SUPPORT MEASURES OF POPULATION INCOME DURING THE PERIOD OF CONSTRAINTS: A COUNTRY APPROACH
Bibliographic record
Abstract
This article is devoted to the research of the COVID-19 pandemic affected the economy of the Russian Federation and other countries of the world and its consequences on society. Today, the social policy of the Russian Federation and the whole world is experiencing great stress. The crisis, which arose due to the imposed restrictive measures to ensure the isolation regime in order to prevent the spread of COVID-2019 by foreign governments, revealed previously existing gaps in the provisions of social protection. The ways of formation and improvement of state support of incomes of the population during a crisis situation all over the world are considered. In the conditions of the crisis, the load on the social system has increased many times over, due to the increase in the number of poor citizens. Funding has been introduced for various measures, methods and ways to improve livelihoods and prevent the closure of Micro-Enterprises, SMEs of all types, self-employed and workers, in order to prevent unemployment caused by the global situation. The analysis of the gross domestic product and the effectiveness of the implemented additional measures of state support of the population’s income has been carried out. For example, the leading countries of the world were considered, such as: Russia, Austria, Canada, France, Germany, Israel, Italy, Japan, Spain, Sweden, United Kingdom, USA.
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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 | 0.001 | 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) | 0.001 | 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".