Rural Population of Southern Russian Regions: Peculiarities of Age Structure
Bibliographic record
Abstract
The current attention towards the problems of rural communities results from a number of negative trends as follows: the rural population decline, increase in the share of poor households, high migration. So, it is urgent to study the demographic potential of rural communities. The article considers peculiarities of the age structure of populations of Astrakhan Oblast, Volgograd Oblast and the Republic of Kalmykia. According to statistical data, over half of Kalmykia’s residents live in rural areas, in Astrakhan Oblast the share is a little over a third, and in Volgograd Oblast - less than a quarter of the region’s population. The analysis of age structure of Astrakhan and Volgograd Oblasts’ populations shows that the share of children and adults aged 36 to 59 is higher than that in urban populations, while the share of young and elderly people is lower. The age structure of Kalmykia’s rural population is characterized by features as follows: the share of children, young and elderly people in rural areas is lower than that within urban communities, and the share of adults aged 36 to 59 is higher. A comparative analysis of demographic indices from the three regions testifies that, in terms of age structure, there is a small share children and a large share of elderly people in Volgograd Oblast. Thus, from this perspective, Volgograd Oblast has a most unfavorable demographic structure. Further consideration of data from the three regions of Southern Russia helps us conclude that there are certain peculiarities in the age structure of rural populations. Special attention should be given to attracting the youth to villages for the development of rural territories and improvement of the demographic situation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".