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Record W7062757527

Who governs? Analysis of the disputed effects of regionalism on legislative careers’ orientation in multilevel systems

2016· article· en· W7062757527 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Repository and Bibliography (University of Liège) · 2016
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRegionalism (politics)PoliticsLegislatureComparative politicsParliamentComparative research
DOInot available

Abstract

fetched live from OpenAlex

The processes of regionalization and federalization are unquestionable trends in Europe considering the flow of powers from states to subnational levels.In multi-level systems, the patterns of regional and national careers reflect this structural evolution.In the literature, two positions oppose each other about the effects of regionalism.Some authors argue that it does affect career patterns while other scholars found little evidence of the regionalism hypothesis.Unclear results in the literature are partly due to the limited number of comparative research across countries and across time, bias in case selection, and the choice of the unit of analysis.For the first time, this article aims to offer such analysis assessing empirically the regionalism hypothesis based on an original comparative dataset of 4.991 regional and national political careers in Belgium (Flanders, Wallonia, and Brussels), Canada (Ontario and Quebec), Spain (Catalonia and Castilla-La-Mancha), and the UK (Wales and Scotland).The intranational and international comparison of cases of strong and weak regionalism proves that regionalism does matter -regional politics attracts more professionalized MPs where regionalism is stronger -but the national parliament remains ultimately the most attractive political arena across regions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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 teacher head, 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

Citations0
Published2016
Admission routes1
Has abstractyes

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