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Record W2563385863 · doi:10.32469/10355/46882

Shaping the game : federalism and voting behavior in advanced industrial democracies

2015· dissertation· en· W2563385863 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismPolitical scienceInstitutionVotingScholarshipPoliticsDual federalismComparative politicsGovernment (linguistics)Political economyVoting behaviorPublic administrationLaw and economicsEconomicsLaw

Abstract

fetched live from OpenAlex

For countries that possess a federal structure, this institution is a crucial component of the constitutional arrangement of the nation. This institution arguably plays a role in nearly all aspects of a country's political environment. Although political science has much to say about federalism in the abstract, each country's federal system works differently. Unfortunately this variation has gone underappreciated in much of the scholarship on voting behavior. This dissertation seeks to inject our theoretical understanding of federalism, largely stemming from the works of William Riker, into comparative empirical analysis of voting behavior. As argued here, federalism in and of itself does not have a direct effect on behavior, instead it has indirect effects largely through the party system in place in a country. These theories will be tested for explaining differences in voter turnout cross-nationally and then again in more focused analyses of voter party choice in the three federal countries of the United States, Canada, and Germany. Ultimately the dissertation finds support for the theoretical and hypothesized effect of federalism on voters' decisions to turn out to vote, as well as how such allows for a considerable portion of the voting electorate in federal countries to cast inconsistent votes between the levels of government. These findings bring federalism back to the forefront of academic consideration in these types of studies.

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)0.010
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.158
GPT teacher head0.420
Teacher spread0.262 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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