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Record W4394725099 · doi:10.1093/ajcl/avae006

Reasonableness as Responsiveness in Administrative Law in the United States, United Kingdom, and Canada: Kant and Arendt on the Role of the Community in Deferential Judicial Review

2023· article· en· W4394725099 on OpenAlexaboutno aff
Graham Mayeda

Bibliographic record

VenueThe American Journal of Comparative Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsJudicial reviewPolitical scienceAdministrative lawLawKingdomPublic administration

Abstract

fetched live from OpenAlex

Abstract When conducting judicial review of administrative decisions using a deferential standard of review, courts should give a greater role to the decision maker’s responsiveness to the interests of the community of judgment—those directly affected by the decision. This Article uses a theory of judgment developed by Immanuel Kant in the Critique of Judgment, and elaborated by Hannah Arendt, to justify why consideration for the community is essential to deciding reasonably. It also reviews the approach to deferential review in the case law of the United States, the United Kingdom, and Canada to determine what the effect would be of this new approach to assessing the reasonableness of a decision. While reviewing courts usually consider the rationality of the decision for achieving the decision maker’s statutory policy goals and the appropriateness of the decision maker’s appreciation of the relevant facts, they do not generally probe the responsiveness of their reasons to the concerns of those affected by it. This Article suggests that courts should do so. The result is that administrative law will in future require better quality reasons from decision makers. Probing the responsiveness of reasons to the concerns of the community of judgment will require courts to compare the weight that the decision maker has placed on facts and arguments to the weight given to them by community members. This will be a significant change in how courts conduct judicial review, but it should enhance the legitimacy of deferential judicial review.

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)0.040
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.610

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.099
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0150.069
Scholarly communication0.0200.006
Open science0.0020.006
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.128
GPT teacher head0.393
Teacher spread0.265 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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