Large Cities as Epicenters of Social and Economic Dynamics of Southern Macroregion: The First Quarter of the 21st Century
Bibliographic record
Abstract
The article analyzes demographic, migration, social and economic dynamics of large cities in the South of Russia at the beginning of the 21st century. It is concluded that this period was characterized by the process of metropolization of the urban network, associated with the rapid development of administrative centers, which concentrated a significant share of financial resources, investments, trade, services, and the real estate market in their regions. The share of the regional capitals in these segments of social and economic activity significantly exceeded their share in the population. Other large cities took an intermediate position between the administrative centers and the rest of the territory of their regions, although in many respects they were noticeably closer to the latter. The exception was the large resort centers of the Black Sea region, whose rapid growth was determined their general recreational potential. The largest social and economic centers of the South of Russia are currently Rostov-on-Don and Krasnodar, which together account for about 25–35% of fixed assets, investments, retail trade turnover, and commissioned housing in the macroregion. Volgograd remains the leading industrial center of the South of Russia. However Volgograd has not fully realized its significant social and demographic potential in modern economic clusters. Among the other large cities of the macroregion, two subgroups can be distinguished. They unite centers comparable in their complex social and economic potential. The first group includes Astrakhan, Sochi, and Sevastopol. The second one consists of Novorossiysk, Simferopol, Volzhsky, and Taganrog. The spatial asymmetry of the group of large cities in the southern macro-region continued to increase in the post-Soviet period: 2/3 of them are currently located at a distance of up to 100 km from the coast of the Black or Azov Seas, forming a zone of advanced development of the South of Russia. In the next 10–15 years, only the Black Sea centers of the Kuban and Crimea, Krasnodar and Rostov-on-Don with the satellite city of Bataysk, can show demographic growth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".