Ethno-Demographic Dynamics of the Settlement Network of Rostov Region: Trends of the Early 21st Century
Bibliographic record
Abstract
The article analyzes the rates and scale of the ethno-demographic dynamics of the settlement network in Rostov region. The study shows a significant slowdown of the process of replacement of the Russian core population with representatives of large ethnic communities in the early 21st century. However, in the early 2010s, about 500 localities (a quarter of the total number) were already involved in this process. This process was the most intensive in localities with up to 100 inhabitants. In 2010, more than 11% of such settlements have already gone through a radical ethno-demographic transformation of their population or were close to its completion. Among the settlements with the population of 101– 300 people the number of such settlements was 3.9%; among the settlements with 301–1000 people this share was 1.2%. There were no large settlements (more than 1 thousand inhabitants) that went through a radical replacement of their core population by new one in the region. The bulk of the settlements which were involved in this transformation process were located in the southern and eastern areas of the region, where large communities of the Meskhetian Turks, Chechens and the peoples of Dagestan settled. The conclusion is made that the process of ethno-demographic transformation of the settlement network of the region for the nearest future will remain limited to the level of small settlements, and geographically to the south and east of the region. However, by 2030–2035, large groups of settlements that have almost completely lost their core Russian population and have a high level of social economic and cultural specificity may appear in the region. Local authorities will face a difficult task of a complex optimization of interaction of such territorial and ethnic areas with regional society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".