Moving Up: Migration between Levels of the Settlement Hierarchy in Russia in the 2010s
Why this work is in the frame
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Bibliographic record
Abstract
For the first time on Russian data for 2011–2020 the flow of population between 7 levels of the settlement hierarchy is estimated. Levels of the settlement hierarchy are represented by cities of different population sizes and their suburbs, other urban and rural settlements. Indicators of migration icrease (decrease) of the population and indicators of demographic efficiency in the form of matrices 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 automatic return of migrants to their place of permanent residence after the end of the period of registration at the place of residence. The beneficiaries of the population “vertical migration” are cities with over 250 thous. 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 (USA, Canada, the Netherlands, etc.), there is no population flow from top to bottom in Russia, and upward flows have a very high efficiency; it is especially high for Moscow, St. Petersburg, and their suburbs. Despite the population movement between neighboring settlement hierarchy levels, its demographic effect is not as great as in irregular migrations. The research calculations are based on the migrants’ individual depersonalized data, which allow detailing migration flows to individual settlements in Russia. Spatial data referencing was carried out based on 15-digit 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 the influence of the features of accounting for migration on the population flow between the selected groups of settlements in the 2010s.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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