THE PROBLEM OF POVERTY IN THE REPUBLIC OF BASHKORTOSTAN (RESULTS OF SOCIOLOGICAL STUDIES)
Bibliographic record
Abstract
The paper is devoted to the analysis of subjective poverty in the Republic of Bashkortostan. The limitations of monetary and the advantages of subjective approaches in measuring poverty are well founded. Based on the data of sociological surveys conducted by the Institute of Strategic Research of the Republic of Bashkortostan in 2015-2020, a higher level of subjective poverty has been determined as well as an absence of positive dynamics in the reduction of this indicator. Four surveys showed comparable poverty rates, confirming the objectivity of the differences with official statistics. At the same time, the socio-demographic profile of the recipients of targeted social assistance is fully correlated with the profile of social poverty derived from the sociological survey. It has been shown that the high level of subjective poverty is due to the displacement of economically active population groups into it, following the deterioration of their material situation. The highest incidence of poverty was the low level of wages and the inability to find better jobs. The level of demand and the actual material situation in the social strata of the data leads to widespread poverty. It is argued that sex and age characteristics, place of residence, level of education, presence of children in the family are factors that contribute to the risk of falling downward social mobility among the poor. The study made concrete proposals to reduce poverty in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".