Financial situation and social support received by families with children in Ulyanovsk oblast
Bibliographic record
Abstract
The aim of this work is to analyze the financial situation and the level of social support received by families with children in Ulyanovsk oblast on the basis of the data from household survey conducted in Ulyanovsk oblast. The study showed that the poverty rate among the surveyed households with children under 18 is significantly higher than the total poverty rate among the households. Many families with children cannot afford buying goods and services they need. In order to maintain their consumption level, a significant proportion of families with children have to take out loans. Analysis of the survey data shows that the coverage of families with children by social benefits is quite high. At the same time, even among the poor households with children, one quarter of households does not receive any social benefits. The surveyed families with children noted difficulties faced by them in obtaining information about social benefits and in collecting necessary documents, long waiting in queues when applying for benefits. The calculations show that provision of regional benefits, including targeted regional benefits, reduces the poverty rate among households with children only slightly. In general, regional benefits are more likely to reduce the extreme poverty of household with children. The analysis presented in the article allows determining possible directions for improving the social support system in Ulyanovsk oblast.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".