Foreign agents or agents of justice? Private foundations, backlash against non-governmental organizations, and international human rights litigation
Bibliographic record
Abstract
Abstract The premise of Russia's 2012 “Foreign Agents” Law, one of the first such laws restricting foreign funding for non-governmental organizations (NGOs), is that foreign monies equal foreign agendas. Since then, over 50 countries have adopted similar laws using a similar justification. This paper interrogates this claim of foreign donor influence through examining legal mobilization by human rights NGOs at the European Court of Human Rights (ECtHR). We track donor support for litigation by providing an overview of all foundation grant flows relating to strategic litigation for 2013–2014, and then matching the granting activities of two major U.S. foundations over 14 years to human rights NGO participation in cases before the ECtHR. Further, through case studies of Russian NGOs, we assess the causal role that donor support has played in facilitating their increased involvement in ECtHR litigation. The combined analysis indicates broad patterns of private foundation support to litigating NGOs, but uncovers no evidence that foreign donors were “pushing” NGOs toward litigation as a strategy, but instead more evidence suggesting that NGOs convinced donors to support human rights litigation. Despite the inaccuracy of the justification underpinning Russia's foreign agent law, the law threatens the survival of human rights organizations.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".