Migration Policy: Characteristics of the Regional Dimension of Migration in Modern Russia (Volga Federal District)
Bibliographic record
Abstract
The relevance of the research topic is determined by the importance of the problem and the need to study migration issues in the public administration practice. The article aims to study migration processes and state migration policies in the Russian Federation during the period from the end of the 20th to the beginning of the 21st centuries. The institutional approach is used as the main approach in this research. The study identifies socio-political, socio-economic, and legal factors which influence the effectiveness of migration policies: administrative, legislative, executive, target-oriented, regional, and consumer. The article presents qualitative changes in the internal and external migration flows in modern Russia, the Volga region, and shows the need to create a new migration policy: migration flows go from the north and the east to the central and south-western parts of the country, forced migration has been replaced by labor migration of the indigenous population from the countries of the near and far abroad. The article also examines the trends in the development of migration legislation, shows the influence of the political situation in the country on the changes in legislation regulating migration processes, and elaborates proposals and recommendations for their optimization. It defines the stages and the main directions of the new migration policy in modern Russia, describes the effect that reforming of the bodies of state power has on the effectiveness of migration policy at the federal and regional levels, and suggests measures for improving migration policies. The materials of the article may be useful in making management decisions and taking specific organizational measures in order to improve the management of migration processes, as well as in the development of training courses in political science studies:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".