Language and Persuasion: Human Dignity at the European Court of Human Rights
Bibliographic record
Abstract
Abstract Although the concept of human dignity is absent from the text of the European Convention on Human Rights, it is mentioned in more than 2100 judgments of the European Court of Human Rights. The judges at the Court have used dignity to develop the scope of Convention rights, but also to signal to respondent states just how serious a violation is and to nudge them toward better compliance. However, these strategies reach dead ends when the Court is faced with government submissions that are based on a conception of dignity that is different from the notion of human dignity relied on by the Court. Through empirical analysis and by focusing on Russia, the country against which the term dignity is used most frequently, the paper maps out situations of conceptual contestation and overlap. We reveal how the Court strategically uses mirroring, substitutes dignity for other Convention values, or altogether avoids confrontation. In such situations, the Court’s use (and non-use) of dignity becomes less about persuading states to comply with the Convention and more about preserving its authority and managing its relationship with states.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".