POVERTY IS NOT A VICE, BUT HOW TO OVERCOME IT IN RUSSIA?
Bibliographic record
Abstract
An issue of the population poverty is one of the most urgent to- day, both in Russia and around the world. The article considers the statistics of poverty in foreign countries as well as in Russia. In that rating, Russia ranks the 64th. The number of citizens falling under the category of poor in 2020 in- creased to 19.9 million people, which in relative terms is 13.5% of the country’s population. The causes of poverty are revealed, the sequence, forms and methods of overcoming poverty in Russia are formulated on the basis of foreign experi- ence in combating poverty. The decline in the income of the Russian popula- tion according to Rosstat in the 2nd quarter of 2020 in annual terms was 8%. GDP declined by 8%, while Canada’s GDP – 13.5%, Germany – 11.7%, and the United States – 9.5%. It is because since the beginning of the pandemic, many developed countries have implemented large-scale material support for the population. The article analyzes a variety of specific ways and methods of combating poverty in the United States, Great Britain, Spain, India, Finland and other countries. Also it presents results of the experiment with application of the method of using unconditional income, support of the population of the Neth- erlands, Canada, Mongolia, Iran, Kenya, and Germany. The article presents the experience of supporting the population in Russia, where that activity was focused on supporting the families with children. The results prove that such a support option cannot be called large-scale and effec- tive, since, as summing, the real incomes of citizens, unlike in other countries, oddly enough, decreased. Poverty and unemployment continue to grow in the context of the current pandemic. Therefore the conclusion contains proposals on how to overcome the poverty and unemployment in our country.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".