Advocacy beyond litigation: Examining Russian NGO efforts on implementation of European Court of Human Rights judgments
Bibliographic record
Abstract
This article examines the ways in which various Russian NGOs, involved in litigation at the European Court of Human Rights (ECtHR), have worked to advocate for improved domestic implementation of rulings made by the Court. The paper traces these advocacy activities in four key problem areas for Russia’s implementation of the Convention: (1) domestic judges’ knowledge and citation of the European Convention or ECtHR case law; (2) the execution of domestic court judgments by Russian state bureaucratic bodies; (3) extrajudicial disappearances and killings in anti-terrorist military operations in the North Caucasus; and (4) torture or inhumane treatment in police detention. The author finds that the impact Russian NGOs can have upon domestic implementation depends greatly upon the professional cultures and incentives of the actors involved as well as whether or not prevention of violations is compatible with other high-level Russian government agendas.
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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 | 0.045 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".