Epidemiological crisis of 2020: financial situation of the population and social support
Bibliographic record
Abstract
The paper analyzes the dynamics of the financial situation and social support coverage of various socio-demographic groups in Russia in 2020 during the coronavirus pandemic. The study was based on the data of three population surveys conducted in May, October, and December 2020. The spread of coronavirus had a negative impact on the welfare of the population: almost half of the respondents reported worsening of the financial situation of their families in 2020. Over half of the respondents indicated the need for cash assistance, and almost a quarter of the respondents—the need for food packages. More than a quarter of the respondents who tried to apply for social benefits in 2020 faced with some problems. In early December 2020, more than 40% of the respondents had already received the state social support in connection with COVID-19, mainly as cash payments. However, almost 60% of the respondents, including more than a half of the poor had not received any social assistance related to the pandemic. The respondents rather critically assessed the sufficiency of the state social support: almost 60% of the respondents believed that the state had not taken sufficient steps to support the population. The coronavirus epidemic has shown the importance of the social support efficiency improving through digitalization and better targeting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".