Geodemographic and Migration Processes in Astrakhan Region: Trends of the First Quarter of the 21st Century
Bibliographic record
Abstract
The article analyzes the main geodemographic trends of the Astrakhan region in the first quarter of the 21st century. In the quantitative and spatial dynamics of the population in the region, three major periods with significant differences in indicators of natural reproduction and migration activity (early – mid 2000s; late 2000 – mid 2010s; the second half of the 2010s – early 2020s) were identified. The features of the demographic dynamics of various districts of the region are shown, due to the national structure and the dominant vector of migration circulation in the local population. Throughout the post-Soviet period, there was a stable inverse correlation between the share of Russians in specific territorial communities and the level of their natural decline. The scale and vector of migration dynamics in the population of cities and districts were determined by the distance to the regional capital and the general standard of living in the region. It was found that a long period of demographic equilibrium (fluctuations from 980 thousand to 1,020 thousand people) was replaced by a steady depopulation of regional territory. This process is associated with the gradual shift of the Astrakhan region in the 2000s–2010s to the group of demographically disadvantaged regions of southern Russia. The central role in this trend is played by a combination of growing natural losses and large-scale migration outflows outside the region due to the reduced standard of living and social and economic development of the Astrakhan region. Accelerated rates of depopulation in the region are likely to continue in the near and medium term. By 2035, its demographic potential may be reduced to 860–875 thousand people. This negative trend implies a significant increase in the measures taken by regional authorities to mitigate the demographic problem.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".