Regional Economic and Financial Interconnectedness and the Impact of Sanctions: The Case of the Commonwealth of Independent States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it