MIGRATION FROM CENTRAL ASIAN COUNTRIES TO RUSSIA DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The article is devoted to the study of the dynamics of migration from Central Asian countries to Russia during the pandemic of the new coronavirus infection COVID-19. Statistical data of the Ministry of Internal Affairs of the Russian Federation were used. They reflect the dynamics of migration registration and removal of foreign citizens. It is difficult to judge the number of migrants in our country based on these data, but nevertheless it is possible to assess the dynamics and intensity of migration processes. The study revealed that the scale of migration flow from Central Asian countries to Russia in the second quarter of 2020 decreased by 1.5-2 times compared to the first quarter of 2020.The largest reduction is noted among tourist and labor migration. The COVID-19 pandemic has changed the migration activity of population of Central Asian countries in the direction of its decline and transformed the structure of the migration flow from this region. The Russian labor market is experiencing a shortage of labor in some sectors of production. However, the paradox is that it is felt against the background of rising unemployment in the country. This deficit is only partially compensated by Russian workers, so employers are waiting for the opening of borders and the influx of foreign labor.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".