Targeted Sanctions as a Pathway to Accountability
Bibliographic record
Abstract
Abstract The growth of ‘Magnitsky-style’ targeted sanctions has ushered in a new chapter in the history of sanctions practice that places civil society actors in an increasingly empowered position. The development of new legal and policy frameworks in several jurisdictions, led by the USA, has formalized an active role for civil society in governments’ sanction designation processes. By creating a legal framework for civil society engagement, the US Magnitsky laws enshrined the importance of civil society as a source of evidence on human rights and corruption issues into law. This article draws on the experience and observations of, and interviews with, sanctions practitioners who have witnessed the increasing role that civil society is taking in Magnitsky-style sanctions. The article begins by asking why civil society engages with targeted sanctions, before examining the legal and policy frameworks through which civil society actors engage with governments. It looks at five jurisdictions where civil society is taking an active role, the USA, UK, European Union, Canada, and Australia. The article describes the emergence of ‘sanctions clearing houses’ — organizations that act as intermediaries between global civil society and the governments with which they are trying to engage. The article considers present civil society perspectives on current barriers to engagement with sanctions regimes and concludes by emphasizing the importance of civil society engagement in this area, providing recommendations to foster this engagement.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".