Bibliographic record
Abstract
The European Court of Human Rights (ECHR) has been criticized for issuing harsher judgments against developing states than it does against the states of Western Europe. It has also been seen by some observers as issuing increasingly demanding judgments. This paper develops a theory of judicial decision-making that accounts for these trends. In order to obtain higher compliance rates with the judgments that promote its preferences, the ECHR seeks to increase its reputation. The court gains reputation every time a state complies with its judgments, and loses reputation every time a state fails to comply with its judgments. Not every act of compliance has the same effect on the reputation of the court, however. When the judgment is costlier, the court will gain more reputation in the case of compliance. In an effort to build its reputation, in some cases the court will issue the costliest judgment with which it expects the state to comply. Since the ECHR receives high compliance rates, its reputation increases, which leads it to issue costlier judgments. The court restrains itself when facing high-reputation states that can severely damage its reputation by noncompliance or criticism, so it demands more from low-reputation states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.039 |
| 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 | 0.014 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.007 |
| 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".