Constitutional Law and Precedent : International Perspectives on Case-Based Reasoning
Bibliographic record
Abstract
This collection examines case-based reasoning in constitutional adjudication; that is, how courts decide on constitutional cases by referring to their own prior case law and the case law of other national, foreign, and international courts. Argumentation based on judicial authority is now fundamental to the resolution of constitutional disputes. At the same time, it is the most common form of reasoning used by courts. This volume shows not only the strengths and weaknesses of such argumentation, but also its serious methodological shortcomings. The book is comparative in nature, with individual chapters examining similar problems that different courts have resolved in different ways. The research covers three types of courts; namely the civil law constitutional courts of Germany, Italy, Poland, Lithuania, and Hungary; the common law supreme courts of the United States, Canada, and Australia; and the European international courts represented by the European Court of Human Rights and the Court of Justice of the European Union. The authors are distinguished scholars from various countries who specialise in constitutional justice issues. This book will be of interest to legal theorists and practitioners, and will be especially insightful for constitutional court judges. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".