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Record W4413445748 · doi:10.1016/j.crbeha.2025.100184

The lone dissenter as a non-conformist in the Supreme Court: Extension of past research, cross-cultural analysis and influence model development

2025· article· en· W4413445748 on OpenAlexaboutno aff
Nadav Goldschmied, Megan Rasich, Rebekah A. Wanic, Mike Raphaeli

Bibliographic record

VenueCurrent Research in Behavioral Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConformistSupreme courtExtension (predicate logic)LawSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Social influence is a potent force in small groups’ decision-making. Asch conducted his foundational lines study demonstrating conformity in such a setting and the difficulty in being a minority of one. His work has been used to assess conformity outside of the lab but may lack applicability to real-world situations. The current study replicates and extends earlier work by analyzing US Supreme Court (SC) jurisprudence (1945 – 2023) and includes Canadian SC voting trends (1953-2023) to investigate conformity cross-culturally. Consistent with past work, we identified unanimous outcomes (9-0) were most prevalent and a lone dissenter (8-1) least in both samples. However, unanimous outcomes were considerably more likely in Canada than in the US. The results are explained based on situational and psychological factors and used to develop a model to examine lone dissenters in small group decision-making. We highlight how the Asch paradigm is not a suitable prism to study SC adjudications and use the new model to suggest future directions for a more robust exploration of social influence in small group decision-making processes.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0040.012
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.273
GPT teacher head0.568
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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