Regional Economic and Financial Interconnectedness and the Impact of Sanctions: The Case of the Commonwealth of Independent States
Bibliographic record
Abstract
The war in Ukraine and the direct and indirect political, economic and financial involvement of many countries worldwide in this conflict demonstrates the difficult process of developing the new world order. Over 10,000 sanctions have already been imposed on Russia by the United States, the European Union and their allies. Many countries are significantly affected by sanctions regardless of whether they are imposing them, being targeted by them, or have economic and trade partnerships with either—or both—of the sides. Commonwealth of Independent States (CIS) countries have been significantly affected by sanctions related to the Russian–Ukrainian war. Seasonally adjusted real quarterly time series, including gross domestic product and external trade, monthly nominal exchange rate time series, exogenous dummy variables for sanctions, and a combination of the vector autoregressive model and the Granger causality test were used in the estimations. We demonstrate how sanctions have affected the Russian economy and foreign exchange market and how their impact may spill over to the economies and foreign exchange markets of other CIS countries. Based on the research findings and contemporary political and economic conditions in the region and the world, we make suggestions helpful for improving the international economic and trade policies of the CIS countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".