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Record W6902920425 · doi:10.7910/dvn/29167

Replication data for: The international diffusion of electoral systems: The spread of mechanisms tempering proportional representation across Europe

2015· dataset· en· W6902920425 on OpenAlexaff

Bibliographic record

VenueHarvard Dataverse · 2015
Typedataset
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsProportional representationRepresentation (politics)Fragmentation (computing)Electoral systemLimit (mathematics)Replication (statistics)Focus (optics)

Abstract

fetched live from OpenAlex

There is an assumption in much of the electoral engineering literature that domestic episodes of electoral system choice occur in a vacuum isolated from international influences. Yet this assumption remains largely untested, despite the comparative focus of much of that literature. This article focuses on part of this gap by considering two electoral mechanisms seeking to limit party system fragmentation under proportional representation – low district magnitudes and high electoral thresholds – and shows that the mechanisms spread across many European countries during the post-1945 period. Analyses reveal that national legislators are more likely to adopt one of these electoral mechanisms when a large number of peer countries have made similar choices within the last two or three years. This effect is robust to various model specifications and to the inclusion of multiple controls. We also offer some qualitative evidence from case studies and parliamentary debates.

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)0.020
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 score0.073
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0730.052

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 head0.125
GPT teacher head0.405
Teacher spread0.280 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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