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Record W2045876479 · doi:10.1080/14662043.2013.805540

The courts/parliament trade-off: Canadian attitudes on judicial influence in public policy

2013· article· en· W2045876479 on OpenAlexaffabout
Elizabeth Goodyear‐Grant, J. Scott Matthews, Janet L. Hiebert

Bibliographic record

VenueCommonwealth and Comparative Politics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsMemorial University of NewfoundlandQueen's University
Fundersnot available
KeywordsParliamentLegislaturePoliticsPolitical sciencePublic opinionLawPublic administration

Abstract

fetched live from OpenAlex

Do citizens have meaningful attitudes – i.e. enduring, subjectively important and psychologically consequential evaluative orientations – regarding the relative roles of courts and legislatures in resolving contentious issues of public policy? If so, what explains these preferences? Using data from the Canadian Election Study, the authors find that Canadians possess meaningful attitudes on what they term the ‘courts/parliament trade-off’. They also find significant heterogeneity across levels of political knowledge in the nature of these attitudes. Further, most determinants of attitudes on the courts/parliament trade-off can be understood to reflect evaluations of political outcomes under the courts or Parliament, rather than assessments of processes within these institutions. Attitudes on the trade-off are largely interpretable as responses to dynamic features of party politics.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.041
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0100.007
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.087
GPT teacher head0.357
Teacher spread0.270 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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