The Estimation of Losses of the Russian Economy from Population Migration to Developed Countries in 2000–2017
Bibliographic record
Abstract
The problem of emigration of Russian citizens to other countries remained quite acute during 2000-2017. This poses a threat to national security, as there are many economically active young people with a high level of education among the emigrants. Therefore, it is required a comprehensive study of these processes and the creation of conditions for the preservation of human capital in Russia. The authors developed a methodology for assessing the losses of the Russian economy in value terms as a result of emigration of citizens abroad. It is based on the determination of the “cost” of human life and the individualization of this indicator in accordance with the level of economic development of the host country and with the subjective factors of the emigrant, as well as in specifying the number of citizens who left the Russian Federation in accordance with the official data of foreign migration services. As a result of the calculations, it was determined that the losses of the Russian economy from this phenomenon for the period 2000-2017 amounted to more than 545.85 billion USD. Such a situation is unacceptable in the conditions of the country’s unfolding competition with other states for the positions of leaders in the new industrial revolution. It is necessary to carry out systematic work to reduce the scale of outgoing flows of international labour migration from Russia.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".