Theory and Practice of Sanctions Strategy and Effectiveness: Influencing Russian Foreign Policy
Bibliographic record
Abstract
This master thesis will review the case of contemporary sanctions implemented against Russia by the European Union (EU) with regards to its Ukraine foreign policy, which will assist in determining the effectiveness of sanctions imposed in this specific case.This thesis considers sanctions implemented against Russia as not effective thus far, since sanctions have yet to force a change in Russia's foreign policy.The lack of effectiveness lies with Russia's unique capabilities that enable it to resist sanctions, in combination with major gaps in the EU's sanctions strategy towards Russia.This thesis utilizes the contemporary EU-Russia case to test and expand on Nephew's and Miyagawa's framework of sanctions effectiveness, and discovers a new realm of sanctions categorization beyond the traditional spectrum of 'effective' and 'ineffective' sanctions regimes -sanctions regimes with inadvertent effects. 1 implementation of sanctions is one tool of diplomacy that states and diplomats can utilize, and can be used solitarily or in congruence with other means, such as military tools.Parallel to 2 diplomacy's early beginnings, the practice of sanctions have been in known use since ancient Greece creating a foundational historical basis for the study of sanctions.With globalization, interdependencies, international institutions, and international law, sanctions have multiplied in the recent century, and more so in the recent decade.Although sanctions were previously 3
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".