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Deference in Human Rights Adjudication

2024· book· en· W4399922939 on OpenAlexaboutno aff
Cora Chan

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAdjudicationDeferenceHuman rightsPolitical scienceLaw and economicsJudicial deferenceLawSociology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.007

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.

Opus teacher head0.039
GPT teacher head0.220
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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