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Record W4398854898 · doi:10.7910/dvn/umwlmb

Replication Data for: The Generational and Institutional Sources of the Global Decline in Voter Turnout

2021· dataset· en· W4398854898 on OpenAlexaffabout
Filip Kostelka, André Blais

Bibliographic record

VenueHarvard Dataverse · 2021
Typedataset
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsReplication (statistics)Voter turnoutTurnoutPolitical scienceDemographic economicsGeographyDemographySociologyBiologyEconomicsLawVotingVirology

Abstract

fetched live from OpenAlex

Why has voter turnout been declining in democracies all over the world? This article draws on findings from micro-level studies and theorizes two explanations: generational change and a rise in the number of elective institutions. The empirical section tests these hypotheses along with other explanations proposed in the literature (shifts in party/candidate competition, voting age reforms, weakening group mobilization, income inequality, and economic globalization). We conduct two analyses. The first analysis employs an original data set covering all post-1945 democratic national elections. The second studies individual-level data from the Comparative Study of Electoral Systems and American, British, and Canadian national election studies. The results strongly support the generational change and elective institutions hypotheses, which account for most of the decline. These findings have important implications for a better understanding of the current transformations of representative democracy and the challenges it faces.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.118
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1180.086

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 head0.055
GPT teacher head0.338
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2021
Admission routes2
Has abstractyes

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