Emigration from Russia to the USA and Canada in the context of the expansion of Russian-speaking communities
Bibliographic record
Abstract
The article discusses emigration flows from Russia to the USA and Canada. The host countries owe their existence to immigration due both to the economic and geopolitical situation in the modern world. Since the late 19th century a consistently high emigration flow has been recorded from Russia to these countries. The greatest outflow occurred in the last decade of the 20th century, when with the collapse of the USSR the flow of emigrants from Russia to these countries, and particularly to the USA, sharply increased. The increase in emigration has led to expansion and strengthening of the Russian-speaking community that emigrated from Russia to the United States and Canada. In the USA the largest concentration of the Russian-speaking population is in three agglomerations: New York, Los Angeles and Miami. These three agglomerations account for over 35% of all immigrants from Russia. In Canada, with a much smaller immigration flow than in the United States, the largest share of immigrants from Russia is concentrated in such agglomerations as Toronto and Montreal. Since the beginning of the COVID-19 pandemic, migration flows to the United States and Canada have decreased from all countries of the world, including Russia. This was the result of both the anti-visa restrictions and the termination by the US Embassy in Russia of issuing non-immigrant visas a first, and subsequently, all other types of visas. If in peak 2014 almost 390 thousand border crossings by citizens of the Russian Federation were recorded, then in 2021 only 77.7 thousand. A similar trend is observed in the emigration flow from Russia to Canada. The main part of the migration flow to the United States consists of Russian citizens who have a residence permit or U.S. citizenship, as well as persons who have received visas at U.S. consular offices in other countries.
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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 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".