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Record W2108214397 · doi:10.1093/jla/2.1.227

The Easy Core Case for Judicial Review

2010· article· en· W2108214397 on OpenAlexaff
Alon Harel, Tsvi Kahana

Bibliographic record

VenueThe Journal of Legal Analysis · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsInstrumentalismJudicial reviewGrievanceLaw and economicsDutyLawPolitical scienceJudicial activismDemocracyEconomicsPoliticsPhilosophy

Abstract

fetched live from OpenAlex

This paper defends judicial review on the ground that judicial review is necessary for protecting “a right to a hearing.” Judicial review is praised by its advocates on the basis of instrumentalist reasons; i.e., because of its desirable contingent consequences such as protecting rights, promoting democracy, maintaining stability, etc. We argue that instrumentalist justifications for judicial review are bound to fail and that an adequate defense of judicial review requires justifying it on non-instrumentalist grounds. A non-instrumentalist justification grounds judicial review in essential attributes of the judicial process. In searching for a non-instrumental justification, we establish that judicial review is designed to protect the right to a hearing. The right to a hearing consists of three components: the opportunity to voice a grievance, the opportunity to be provided with a justification for a decision that impinges (or may have impinged) on one's rights, and the duty to reconsider the initial decision giving rise to the grievance. The right to a hearing is valued independently of the merits of the decisions generated by the judicial process.

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)0.030
metaresearch head score (Gemma)0.092
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: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.092
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.025
Scholarly communication0.0140.017
Open science0.0020.008
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0090.002

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 head0.355
Teacher spread0.316 · 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
GenreEmpirical

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

Citations38
Published2010
Admission routes1
Has abstractyes

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