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Record W2168592881 · doi:10.1111/1468-2508.00126

Selecting Appeals for Judicial Review in Canada: A Replication and Multivariate Test of American Hypotheses

2002· article· en· W2168592881 on OpenAlexaboutno aff
Roy B. Flemming, Glen Krutz

Bibliographic record

VenueThe Journal of Politics · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralizability theoryAppealSupreme courtTest (biology)Political scienceCertiorariReplication (statistics)LawPsychologyBiology

Abstract

fetched live from OpenAlex

Comparative research of agenda setting by high courts in other countries that replicates American research on the certiorari process is rare. This is the first study of the leave to appeal process in the Supreme Court of Canada. It also is the first multivariate model that operationalizes the jurisprudential indicia Perry identified as important when the U.S. Supreme Court "decides to decide." This study finds that endogenous institutional norms in Canada produce a decentralized process in which legal cues or signals are emphasized but in diverse and varied ways. Institutional differences across high courts therefore affect the generalizability of American findings.

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)0.015
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.056
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0100.006
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.066
GPT teacher head0.327
Teacher spread0.261 · 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 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

Citations39
Published2002
Admission routes1
Has abstractyes

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