Typology of Core Cities of Urban Agglomerations on the Basis of Demographic Development (on the Example of European Russia)
Bibliographic record
Abstract
The paper analyzes the features of the demographic development of cities-centers of urban agglomerations of the European part of Russia for the period 2012–2019; 54 cities with populations of at least 250 thousand people were considered. The only exception was the city-centers of the polycentric Kavminvodskaya urban agglomeration, the total population of which is more than 450 thousand people. The analysis was carried out on the basis of open data from the official statistics of Rosstat using traditional methods of summarizing and grouping. Cities with the corresponding parameters were entered into the developed three-tier typological table, which made it possible to consider the resulting groupings as a dynamic typology of core cities by the nature of demographic development. As a criterion for identifying types, we used the rate of increase/decrease in the population size; the selection of groups and subgroups within the types was made taking into account the ratio of the coefficients of natural or migration increase/decrease, the prevalence of international, interregional, intraregional migrations or their combinations. Six types of cities with a predominance of population increase or decrease were identified. Migration is the dominant source of population increase, and natural increase is declining everywhere. Stable indicators of demographic development are typical for a limited number of cities and are manifested in those that have vivid competitive advantages – a favorable geographic location, economic growth, a higher quality of the urban environment, image attractiveness. About a quarter of cities do not have the ability to overcome negative trends and transition to demographic growth. The cities close to the Russian capital are losing a competition for the labor resources. Unfavorable manifestations in the demographic situation of the largest cities, including some million plus cities, are found. A comparative analysis of the dynamics of the population size at the beginning and end of the study period revealed the tendencies of the growth of negative symptoms in the demographic development, practically in all the cities under study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".