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Record W1873752839 · doi:10.22329/wyaj.v28i2.4502

Politics and Policy Change in American Administrative Law

2010· article· en· W1873752839 on OpenAlexvenueno aff
Richard Murphy

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeAgency (philosophy)PoliticsLawPolitical scienceArbitrarinessAbandonment (legal)Law and economicsAdministrative lawPosition (finance)SociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

This essay uses Justice Scalia’s and Breyer’s dueling opinions in FCC v. Fox Television Stations, Inc. (2009), as a vehicle for exploring the contested relationship between politics and policy change in administrative law. In Fox, a five – justice majority led by Justice Scalia insisted that an agency’s abandonment of an old policy position in favor of a new one should survive review for arbitrariness so long as the agency explains why its new position is reasonable. A different five – justice majority (yes – that adds up to ten) led by Justice Breyer thought that Justice Scalia’s stance left too much room for politicization of policymaking. To curb such influence, Justice Breyer insisted that an agency, to justify abandoning an old policy, must explain why it was reasonable to change from its old policy to the new one. Neither of these two approaches in Fox hits quite the right note. Justice Scalia’s view unduly minimizes the problem of politicization. Justice Breyer’s solution seems formalistic and easy to evade. A better way forward may lie in combining Justice Scalia’s simpler framework with Justice Breyer’s more suspicious attitude. Taking a cue from Justice Frankfurter in Universal Camera, the courts should respond to the potential for excessive politicization of agency policymaking not with more doctrinal metaphysics but with a suspicious “mood.”

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.419
Teacher spread0.346 · 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 teacher head, 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

Citations0
Published2010
Admission routes1
Has abstractyes

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