THE IMPACT OF SANCTIONING RUSSIAN CENTRAL BANK ASSETS ON GLOBAL CENTRAL BANKS AND INTERNATIONAL TRADE AND INVESTMENT
Bibliographic record
Abstract
The deployment of sanctions as a deterrence and compellence tool has massively increased in recent years, leading to increased uncertainty and turbulence in the global economy. The weaponization of the Russian Central Bank's foreign reserves by Western powers is already causing a significant shift in the international financial order. Central banks outside Western countries have begun diversifying their external reserves away from assets denominated in Western currencies. In recent years, firms' internationalization has become more entangled with contextual political processes, and the dimension of political intervention in international business has continued to change unpredictably. Multinational enterprises have incurred considerable losses due to the sanctions imposed on Russia by the West and the countersanctions unleashed by Russia. Building a model to predict political interventions and the necessary adaptations may be challenging due to the unpredictability of international relations, where allies can quickly become adversaries. Multinational enterprises should consider implementing risk management, flexibility and adaptability, information gathering and monitoring, and investment strategies to build resilience against the risks associated with unexpected sanctions
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".