MétaCan
Menu
Back to cohort

Who Controls the Content of Supreme Court Opinions?

2011· article· en· W2141053676 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmerican Journal of Political Science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
FundersYork University
KeywordsSupreme courtConcurrenceMajority opinionPolitical scienceConcurring opinionEconomic JusticeJudicial opinionPower (physics)LawDissenting opinionEmpirical researchSupreme Court DecisionsCourt of recordOriginal jurisdiction

Abstract

fetched live from OpenAlex

Conventional arguments identify either the median justice or the opinion author as the most influential justices in shaping the content of Supreme Court opinions. We develop a model of judicial decision making that suggests that opinions are likely to reflect the views of the median justice in the majority coalition. This result derives from two features of judicial decision making that have received little attention in previous models. The first is that in deciding a case, justices must resolve a concrete dispute, and that they may have preferences over which party wins the specific case confronting them. The second is that justices who are dissatisfied with an opinion are free to write concurrences (and dissents). We demonstrate that both features undermine the bargaining power of the Court's median and shift influence towards the coalition median. An empirical analysis of concurrence behavior provides significant support for the model.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.884
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.019
Scholarly communication0.0000.000
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 head0.080
GPT teacher head0.334
Teacher spread0.254 · 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