Geography and form of settlement of Russians of the near abroad (Baltic states, Belarus, Moldova): main trends of the first quarter of the 21st century
Bibliographic record
Abstract
The article identifies the main trends of the territorial dynamics of the Russian population of the Baltic states, Belarus, Moldova and Transnistria during 2000s-2010s. It was found that, correlating with the general decline, the geography of Russian population also decreased. However, in all the studied countries the fragmentation of the settlement system occurred at the expense of settlements of lower taxonomic ranks (farms, small villages). In larger settlements, Russian groups persisted even in Moldova and Lithuania, which demonstrated the highest rates of demographic decline of Russian communities in the post-Soviet period. The largest concentrations of the Russian population in all the considered countries remained in the capitals. Moreover, they were also the epicenters of assimilation, the main factor of Russian depopulation in the 2000s and the first half of the 2010s. The level of urbanization of Russian communities remained very high in all the countries (80-90%), but at the beginning of the 21st century it decreased. This process was related not so much to the increased rootedness of Russian villagers, but rather to the migration of the Russian population from peripheral regions to the rural outskirts of metropolitan agglomerations. Being a form of deurbanization, it represented a variant of the geodemographic metropolization of Russian communities (the increasing concentration in capital regions). Only in Moldova the increased demographic stability of rural Russians was determined by a small group of old believer villages amidst a high level of derussification of the rest of the settlement network. Only the Russian community of Transnistria demonstrated an increased territorial and quantitative stability in the 2000s and 2010s. It clearly illustrated the significant role of geocivilizational orientation of post-Soviet states in the demographic dynamics of the local Russian population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".