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Record W4315646184 · doi:10.15688/re.volsu.2022.4.11

Large Cities as Epicenters of Social and Economic Dynamics of Southern Macroregion: The First Quarter of the 21st Century

2022· article· en· W4315646184 on OpenAlexaboutno aff
Ekaterina Suschaya

Bibliographic record

VenueRegionalnaya ekonomika Yug Rossii · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsPosition (finance)Quarter (Canadian coin)Real estatePopulationGeographyRecreationDemographic economicsEconomyEconomic geographyEconomic growthBusinessPolitical scienceEconomicsDemographySociologyFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.241
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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