Revisiting the effectiveness of economic sanctions in the context of Russia’s invasion of Ukraine
Bibliographic record
Abstract
This paper addresses the relevant and ongoing debate surrounding the effectiveness of economic sanctions. In light of recent sanctions imposed by Canada, the United States, Europe, and other Western states in response to Russia’s invasion of Ukraine, the topic has garnered renewed attention. To assess the effectiveness of these sanctions thus far, it is important to revisit key contributions in the existing literature. We begin by defining economic sanctions and describing their most common forms. Next, we explore the question of whether sanctions are effective, by examining different conceptions of the term “effectiveness.” Then, we address the skeptics to understand why many scholars have argued that sanctions tend to be ineffective or have adverse consequences. Finally, we examine the key question of the effectiveness of economic sanctions thus far in the context of Russia’s invasion of Ukraine, utilizing a five-dimensional framework devised by Lindsay (1986. Trade sanctions as policy instruments: A Re-examination. International Studies Quarterly, 30(2), 153–173). We find evidence that the sanction regime on Russia has been mostly effective thus far in dimensions of deterrence, international symbolism, and domestic symbolism, partially effective in terms of compliance, and mostly ineffective in terms of subversion. We conclude by arguing that future research should take a broader, more interdisciplinary approach when assessing sanction effectiveness.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".