Bibliographic record
Abstract
The results of the analysis of Russia’s role in the world system of remittances for the period from 2010 to 2018 have been presented in the article. The volumes of cash outflow from Russia and their inflow to Russia have been determined. The features of cross-border cash flows with the Commonwealth of Independent States countries and foreign countries have been revealed, which consist in the fact, that Russia is characterized by an extremely high volume and rate of outflow of funds in the form of cross-border transfers, along with a low volume of their inflow. It has been established, that the exchange of funds with the Commonwealth of Independent States countries and with foreign countries are independent flows with their own characteristics. The main foreign and CIS countries – Russia’s partners in cross-border money transfers-have been defined. The growth dynamics and the target structure of remittances have been assessed. It has been revealed, that cross-border remittances from Russia are characterized by seasonality: a steadily recurring growth of remittances in the fourth quarter and a decrease in the first quarter of each year. The results of the forecast of the volume of remittances of individuals for 2019 have been presented. In accordance with the forecast, the growth of remittances from the Russian Federation will continue in 2019. According to the forecast, 21,755 million dollars USA will be transferred abroad in the second half of 2019. In general, in 2019, the volume of money transfers abroad will be less than the volume of 2018 and will amount to 42,804 million dollars USA. In the first half of 2020, 17,635 million dollars USA is expected to be transferred abroad.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".