Bibliographic record
Abstract
Abstract In human rights adjudication, courts sometimes face issues that they lack the expertise or constitutional legitimacy to resolve. One way of dealing with such issues is to ‘defer’, or accord a margin of appreciation, to the judgments of public authorities. Although there is a rich literature on the subject of deference, two important questions remain unresolved: what devices courts should use to exercise deference, and how deference can be made more workable for judges and predictable for litigants. This book offers the first comprehensive analysis of these questions. It introduces six devices for deference (namely, the burden of proof, standard of proof, standard of review, giving of weight, choice of interpretation, and choice of remedy), analyses how courts should choose amongst them, and proposes techniques for rendering deference practicable. The book’s arguments will enable human rights adjudication to be more principled and more in line with the rule of law and separation of powers. The book has two distinctive features. First, it engages with the jurisprudence of six common law jurisdictions that apply a structured proportionality test in rights adjudication, namely, Canada, Hong Kong, Ireland, Israel, New Zealand, and the United Kingdom. Second, the book offers guidelines for judges who wish to apply its theoretical arguments. Combining theory with practice in a broad range of jurisdictions, the book will be an important reference for researchers and students of constitutional theory, comparative constitutional law, and human rights law around the world. It will also assist practitioners, judges, and policymakers who have to grapple with issues of deference in adjudication.
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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".