Bibliographic record
Abstract
Sanctions cooperation plays an important part in securing effectiveness of the special restrictive measures, especially so in view of the current global economic and political instability and russia's destructive behaviour. The article is devoted to the problem of sanctions cooperation between the leading countries. Using the data from the national sanctions lists of Australia, Canada, the European Union, Japan, New Zealand, Switzerland, the United Kingdom, Ukraine and the United States of America as the largest senders of special restrictive measures, we provide the number of unique sanctioned entities and describe the dynamics of imposed sanctions, their growth rates, and the share of sanctioned entities, related to the territory of russia during 2022–2024, noting an increase in imposed special restrictive measures both overall, and especially against russia, but also a decrease in the average number of countries sanctioning each entity, implying a decrease in sanctions coordination between the senders. We then calculate indicators, which reflect the level of sender's cooperation in the selection of entities for sanctioning, namely the shares of sanctions in the structure of sanctioned entities and the average shares of common sanctioned entities for each sender, separately for all sanctions and for sanctions against russia. This allows us to find a negative relation between the number of sanctioned entities and the level of sanctions cooperation, that suggests a downward tendency in cooperation instances that runs opposite to the increase in number of imposed special restrictive measures, which, for a lesser degree, persists for the sanctioning efforts against russia, signifying global difficulties in coordinating national sanctions policies for dealing not only with the threats to national security of individual countries, but also in generating economic pressure and fighting off the common threat of russia's armed expansion. We believe the efforts in improving sanctions cooperation are needed, so as to ensure the effectiveness of restrictive measures in dealing with current and future threats.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".