Moving Up: Migration between Levels of the Settlement Hierarchy in Russia in the 2010s
Bibliographic record
Abstract
Abstract Based on Russian data for 2011–2020, the population flow between seven levels of the settlement hierarchy has been estimated for the first time. Levels of the settlement hierarchy are represented by cities with different population sizes and their suburbs, as well as other urban and rural settlements. Indicators of migration increase (decrease) and demographic efficiency indicators in matrix form are calculated for the hierarchy levels. It is shown that the scale of this flow is affected by changes in the system of migration registration in Russia in the 2010s, namely, the auto return of migrants to their place of permanent residence after the end of the registration period at their temporary place of residence. The beneficiaries of “vertical migration” of population are cities with over 250 000 inhabitants; the biggest winners are the urban agglomerations of Moscow and St. Petersburg. Each next settlement hierarchy level gives the population “up” and receives replenishment from the lower “layers.” In contrast to countries where similar studies were conducted (United States, Canada, the Netherlands, etc.), there are no population flows from top to bottom in Russia, and upward flows have a very high efficiency; it is particularly high for Moscow, St. Petersburg, and their suburbs. Despite population movement between neighboring settlement hierarchy levels, its demographic effect is not as great as in jumpwise migrations. The calculations of the study are based on individual depersonalized migrant data, which made it possible to categorize migration flows to individual settlements in Russia. Spatial data referencing was carried out based on Rosstat codes unique for each settlement. This made it possible to analyze migration not between administrative units, but between settlements grouped by population size. It was also possible to identify how the peculiarities of accounting for migration influence population flow between the selected groups of settlements in the 2010s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".