Bibliographic record
Abstract
Abstract Even though they represent almost 50% of all reported cases before the European Court of Human Rights (ECtHR), settlements of human rights violations escape scholars’ attention. While victims are increasingly expected to resolve their disputes amicably, it is unclear whether applicants will be better off accepting settlement offers rather than proceeding to litigation. The article charts the practice of friendly settlements before the Court from the 1980s to today, mapping a shift in approach from seeking bilateral solutions to the proactive role of the Registry as mediator encouraging states and applicants to settle their cases to relieve the Court of the heavy workload. The study of 10,500 cases reveals how strategies adopted by the Registry—from procedural changes to how and when consent is given to settlement, to the framing of settlement offers, and a close relationship with representatives of the respondent state—have favored the most frequent violators of the European Convention on Human Rights and sidelined the interests of the applicant. The analysis uncovers that the imbalance between parties and lack of enforcement are very much present in the ECtHR settlement system and that the active role of the Registry has reinforced, rather than redressed these concerns. The findings expose the dangers of pursuing en masse settlement in the human rights context and raise concerns about achieving long-term justice for victims of human rights violations through other means than adjudication.
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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 | 0.070 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".