Economic effects of isolating Russia from international trade due to its ‘special military operation’ in Ukraine
Bibliographic record
Abstract
The international community has reacted with surprising speed and unity to Russia’s ‘special military operation’ on Ukrainian territory through commercial and financial sanctions to achieve its economic isolation. This military action will change the relations between Russia and most world countries in ways that cannot yet be foreseen. This study analyzes the short-term effects of international trade interruptions on the economy, considering different isolation scenarios. The hypothetical extraction method and a multi-regional input-output model are used to simulate the economic effects on the production of 189 countries. The results show that the most affected country is Russia, with a drop in production of 10.1% in the scenario with sanctions from the European Union and 14.8% when the sanctions are also applied by Australia, Canada, Japan, United States, and the United Kingdom. The European countries with the greatest geographical proximity and strong trade flow with Russia suffer a significant drop in their production, including Lithuania, Latvia, Estonia, Finland, Hungary, and Poland. In Russia, the most affected economic sectors are Re-export & Re-import and Mining & Quarrying. Finally, the estimated impacts are a lower bound since the effects associated with financial sanctions, exchange rates, commodity prices, among others, are not considered.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".