Post-Soviet Trajectories of Russian Shrinking Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract— The objective of the article is to assess the scale and dynamics of shrinkage of cities in Russia and its regions in the post-Soviet period. Urban shrinkage analysis, based on the average annual index of population decrease according to population censuses, showed that these processes (at least during one of the intercensal periods) in total covered more than half of Russian cities. At the same time, in less than a third of centers, the average annual population decrease over the entire period exceeded 1%. In 1989–2002, the number of shrinking cities was quite small (less than a quarter), and during subsequent intercensal periods, it increased significantly, amounting to more than a third of all cities in the country by 2021. Analysis of the spatial distribution of urban shrinkage showed that these processes occurred at different stages, both at the expense of the resource cities in the northern and eastern territories of the country, and centers of old-developed regions, primarily the Non-Chernozem zone. Most shrinking cities are represented by small centers with populations of less than 50 000 people. With the general negative nature of population dynamics, there is a multidirectionality and variability of shrinkage trends in Russian cities. The specific features of shrinkage during each of the three intercensal periods and alternating phases of depopulation formed the basis for distinguishing six types of urban shrinkage trajectories.
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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 | 0.002 | 0.001 |
| 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 it