Shrinking cities in post-Soviet Russia
Bibliographic record
Abstract
The paper is aimed at assessing scale and trends of urban shrinkage in post-Soviet Russia both at national level and by its major regions. Based on the calculation of average annual index of population loss according to population censuses (1989–2021) data, almost half of Russian cities in total have been shrinking for at least one of three intercensal periods. At the same time, in one of three centers the average annual depopulation exceeded 1% at the end of the entire period. In 1989–2002, the number of shrinking cities was not significant (less than a quarter in total), while increasing dramatically in subsequent inter-census periods to over than 1/3 of all urban settlements of the country by 2021. Study of spatial spreading of urban shrinkage phenomenon unveiled that its progress at different stages was mainly contributed either by resource-based cities of the northern and eastern parts of the country, or by urban settlements in old-developed regions, primarily the Non-Chernozyom areas. Absolute majority of all shrinking cities (87%) are minor units with a population under 50,000 inhabitants. Taking into account the general unfavourability of depopulation and the instability and variability of trends, six types of urban shrinkage trajectories with various combinations and alternations of depopulation phases were identified based on the sequence of depopulation phases within each of the three intercensal periods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".