The Practical Construction of Precedent in the Jurisprudence of the European Court of Human Rights
Bibliographic record
Abstract
What is a legal precedent? How are precedents formed and how do they shape legal outcomes? Over the last decades, a number of studies have appeared that take a socio-legal perspective on the practices of the use of precedents by national and international courts and that have both renewed and extended previous doctrinal discussions on the topic. Based on a conference, this edited volume brings together contributions with different approaches to the study of precedents as both “rules” and “practice”. Rather than studying the binding effect of precedent, the chapters investigate the various conditions of its formation, its forms, and its functions. In so doing, they employ a broad range of methods and add new perspectives to the discussion. Thus, the book not only offers, inter alia, an exploration of the legal actors of precedents and their environment but also gives insights into recent developments in legal methodology for using and studying precedents that is relevant for legal practice and academia alike.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".