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Record W3195366346 · doi:10.1093/publius/pjab031

Federal Constitutional Values and Citizen Attitudes to Government: Explaining Federal System Viability and Reform Preferences in Eight Countries

2021· article· en· W3195366346 on OpenAlexaboutno aff
A. J. Brown, Jacob Deem, John Kincaid

Bibliographic record

VenuePublius The Journal of Federalism · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
FundersAustralian Research CouncilGriffith University
KeywordsFederalistFederalismDevolution (biology)Public administrationPoliticsPolitical scienceGovernment (linguistics)Political systemConstitutional amendmentPolitical cultureLawSociologyDemocracy

Abstract

fetched live from OpenAlex

Abstract This study presents a measure of federal constitutional values as a dimension of federal political culture derived from four key features of federal systems. Tested in six federal and two non-federal countries, we find the measure is stable and taps enduring values, including confirmation that citizens who support devolutionary reform have stronger federal constitutional values. Defining federalism success as a system where citizens have strong federal constitutional values and high satisfaction with their current polycentric system, our results find Switzerland and Canada being the most viable, followed by the United States, Australia, and Germany, while Belgium is not very successful. In the non-federal countries, substantial support for devolution and possibly federalism is found in France, but devolution is more contested in the United Kingdom. The results affirm the importance of public attitudes and political culture in understanding the performance of federal political systems and public support for federalist-type reforms.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.282
Teacher spread0.260 · 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
Published2021
Admission routes1
Has abstractyes

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Same venuePublius The Journal of FederalismSame topicPolitical Systems and GovernanceFrench-language works237,207