Migration processes between Russia and the Asia-Pacific countries: the sanctions context and implications for the situation of Russians
Bibliographic record
Abstract
This article explores the most recent wave of emigration from Russia to key countries in the Asia-Pacific region, namely the United States, Canada, the Republic of Korea, and Japan. It focuses on the consequences of the Ukrainian crisis and Western-imposed sanctions, assessing their impact on migration trends and the socio-economic status of Russian emigrants. Based on statistical data and sociological surveys, the study demonstrates a general increase in emigration from Russia to the selected destinations, with the exception of Japan, where numbers have declined. The article also highlights how the Special Military Operation and media narratives initially led to a deterioration in public attitudes toward Russian-speaking communities, though by mid-2023, this negative sentiment had largely subsided. The analysis underscores that economic and social factors remain the primary drivers of emigration, despite external restrictions. Moreover, the article details the challenges faced by highly skilled Russian professionals abroad, particularly concerning labor market integration, often hindered by institutional and cultural barriers. These findings contribute to a deeper understanding of ongoing emigration dynamics and offer relevant insights for decision-makers shaping migration policy and strategies for supporting Russian citizens abroad. The study enhances knowledge on how geopolitical shifts influence regional migration in the Asia-Pacific.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".