From Free Trade to Strategic Constraints: U.S.-Led Sanctionsand Export Controls Against Russia and China
Bibliographic record
Abstract
This article examines the evolving role of sanctions and export controls led by the United States (U.S.) in shaping global trade dynamics, particularly in response to Russia’s invasion of Ukraine and China’s technological and military advancements. The U.S. has expanded its use of extraterritorial export control measures, particularly the Foreign Direct Product Rules, to regulate the global flow of strategic commodities, technology, and software. The study explores the extensive sanctions and export control regimes imposed on Russia following its 2022 invasion of Ukraine, including restrictions on energy, finance, and military-related technologies. The article also evaluates Canada’s parallel regulatory framework and compares its scope, enforcement, and effectiveness with that of the U.S. Furthermore, this work delves into U.S. efforts to curb China’s technological development through semiconductor export restrictions, targeting companies, such as Huawei. This strategy has escalated trade tensions, compelling China to develop self-sufficiency in critical industries. However, the effectiveness of U.S. sanctions and trade restrictions remains uncertain. While they have caused economic strain on targeted nations, they have also fueled closer alliances between Russia and China, encouraged retaliatory measures, and strained relationships with key U.S. trade partners. Additionally, the legality of these restrictions under the World Trade Organization (WTO) obligations remains contested, with China challenging U.S. export controls as discriminatory and trade-restrictive. If the U.S.’s coercive trade policies alienate allies, disrupt global supply chains, and violate WTO commitments, they could backfire, which could hinder American businesses, isolate the U.S. from international markets, and ultimately undermine its ability to maintain technological and military supremacy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".