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Record W2605280893 · doi:10.1093/ajcl/avw016

Po Jen Yap, Constitutional Dialogue in Common Law Asia (Oxford University Press, 2015)†

2016· article· en· W2605280893 on OpenAlexaboutno aff
Yvonne Tew

Bibliographic record

VenueThe American Journal of Comparative Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutionalismDialogicCommonwealthLawJudicial reviewConstitutional reviewConstitutional lawPolitical scienceHigh CourtConstitutional theoryCommon lawConstitutional courtSociologyConstitutionDemocracyPolitics

Abstract

fetched live from OpenAlex

Over the last two decades, scholars have theorized new models of constitutional review that avoid conferring on the courts the final word on constitutional understandings. Scholars have identified this form of constitutionalism variously as the “Commonwealth model of constitutionalism,”1 “weak-form judicial review,”2 the “parliamentary bill of rights model,”3 and “dialogic judicial review.”4 Discussions of this model of review, however, have focused primarily on Commonwealth systems in the West—such as Canada, the United Kingdom, New Zealand, the Australian Capital Territory, and the State of Victoria in Australia—with little attention paid to Asia. Po Jen Yap’s new book, Constitutional Dialogue in Common Law Asia, fills this void by exploring a dialogic model of judicial review in three Asian common law systems: Hong Kong, Malaysia, and Singapore. Yap argues that dialogic review is the most attractive approach to constitutional review, both normatively and pragmatically, and his work raises thought-provoking questions about the extent to which the dialogic model is the “constitutional ideal”5 for these Asian legal systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0060.011
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0150.003

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 head0.042
GPT teacher head0.316
Teacher spread0.274 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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