How frontline states tackle sanctions against Russia: Implementation and enforcement dynamics in Poland and the Baltics
Bibliographic record
Abstract
Russia’s invasion of Ukraine in February 2022, reshaped the EU’s security landscape, prompting sanctions aimed at weakening Russia’s war capabilities. These sanctions also redefined the roles of public authorities and the private sector, introducing new challenges in a shifting geopolitical context. Public authorities, including financial intelligence units, customs, state security agencies, law-enforcement agencies, etc., must identify, prevent, and investigate sanctions evasion and circumvention. This requires robust legal frameworks, adequate resources, and expertise in sanctions evasion typologies. Similarly, businesses and financial institutions operate in legal ambiguity, often asking, “Who am I dealing with in this transaction?”, as they navigate complex compliance requirements. Both the public and private sectors need a strong framework for domestic and cross-border sharing of financial intelligence, trade data, and knowledge of sanctions evasion typologies, as well as insight into the corporate structures of sanctioned entities. However, the EU's decentralized approach of independently designed national enforcement models may hamper cooperation and cross-border financial intelligence sharing. This paper examines how Poland, Lithuania, Latvia, and Estonia that are post-Warsaw Pact EU countries bordering Russia, implement and enforce those sanctions. It explores who "does what" and whether national authorities are adapting their modi operandi to enforce sanctions effectively. The findings reveal distinct national approaches. Latvia’s FIU became Europe’s first sanctions authority, integrating intelligence and enforcement functions. Estonia’s FIU plays a significant role but shares responsibilities with other agencies. Lithuania’s FIU adopts a collaborative model, leveraging a public-private partnership with the Center of Excellence in Anti-Money Laundering. Poland has a fragmented enforcement structure and regulatory framework but is unique in implementing its own autonomous 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".