Proportionality and the rule of law: rights, justification and reasoning
Bibliographic record
Abstract
To speak of human rights in the twenty-first century is to speak of proportionality. Proportionality has been received into the constitutional doctrine of courts in continental Europe, the United Kingdom, Canada, New Zealand, Israel, South Africa, and the United States, as well as the jurisprudence of treaty-based legal systems such as the European Convention on Human Rights. Proportionality provides a common analytical framework for resolving the great moral and political questions confronting political communities. But behind the singular appeal to proportionality lurks a range of different understandings. This volume brings together many of the world's leading constitutional theorists – proponents and critics of proportionality – to debate the merits of proportionality, the nature of rights, the practice of judicial review, and moral and legal reasoning. Their essays provide important new perspectives on this leading doctrine in human rights law.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".