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Record W4387976370 · doi:10.2478/bjals-2023-0011

Magical Thinking and Appearance-based Recusal

2023· article· en· W4387976370 on OpenAlexaboutno aff
Zygmont Pines

Bibliographic record

VenueBritish Journal of American Legal Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsImpartialityPreceptJurisprudenceLawObligationNorm (philosophy)SociologyMoral obligationPolitical scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article is a critical analysis of a fundamental judicial ethic, the appearance of impartiality, an increasingly important public issue that is poorly understood and woefully underexamined in jurisprudence and academic literature. The ethic is pivotal to the determination of judicial disqualification, a/k/a recusal, and the public's fragile trust in the rule of law. The article explains how a mysterious metaphorical device, the “reasonable observer” (a descendant of the common law's “reasonable man”) has been subjectively applied in a confusing and inconsistent manner in judicial disqualification cases. The unexamined approach has unwittingly undermined the plain text and the mandatory ethical obligation of recusal (i.e., a judge must disqualify when his or her impartiality might reasonably be questioned). The discussion: (a) analyzes the theoretical underpinnings of the reasonable person-observer analytical tool (“heuristic”); (b) explains how American jurisprudence has glibly transmogrified the appearance-recusal precept; (c) provides a unique and starkly contrasting analytical perspective demonstrating how select common law-based jurisdictions (Australia, Canada, Singapore, South Africa, United Kingdom) have painstakingly examined and applied the widely-recognized norm of appearance-based impartiality; and (d) synthesizes the preceding theoretical and jurisprudential information to support a proposal for a recalibrated metric and a pragmatic, clarifying heuristic. The article concludes with a model provision, in the form of a guiding “commentary,” that summarizes the essential aspects of the appearance of bias precept. The article provides an interpretative approach that attempts to be faithful to the letter and spirit of the foundational judicial ethic.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.039
GPT teacher head0.356
Teacher spread0.317 · 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 designOther design
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
Published2023
Admission routes1
Has abstractyes

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