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Record W2541920064 · doi:10.1111/spsr.12226

Electoral Linkages in Federal Systems: Barometer Voting and Economic Voting in the German Länder

2016· article· en· W2541920064 on OpenAlexaff
Lori Thorlakson

Bibliographic record

VenueSwiss Political Science Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVotingFederalismInterdependenceCompetition (biology)PoliticsBarometerPolitical economyEconomicsPolitical scienceGermanGovernment (linguistics)Economic systemPublic economicsPublic administrationLawGeography

Abstract

fetched live from OpenAlex

Abstract Federal systems create political competition at multiple territorial levels. While models of vertical bargaining conceptualise federal‐subnational relations as occurring between parties with exogenously defined interests, federalism also structures forms of interdependence between the federal and subnational levels. Political competition in multi‐level systems is marked by interdependence between the federal and subnational levels through barometer and second order voting effects. Findings of a more ‘autonomous’ form of political competition at the subnational level, through state‐level economic voting, are less common. This article examines Germany, a highly interdependent federation, to assess the extent to which voting in Land elections responds to Land level economic performance and whether political and institutional factors affect this. I find evidence that in Land level elections, voting for the federally incumbent party is responsive to federal economic performance. Alongside this, there is evidence of ‘uncoupled’ electoral behaviour at the Land level, with Land level economic voting. This is enhanced by single party government.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.057
GPT teacher head0.401
Teacher spread0.344 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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