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Record W3027489367 · doi:10.1017/s0003055420000143

The Electoral System, the Party System and Accountability in Parliamentary Government

2020· article· en· W3027489367 on OpenAlexaff
Christopher Kam, Anthony M. Bertelli, Alexander Held

Bibliographic record

VenueAmerican Political Science Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAccountabilityGovernment (linguistics)Power (physics)Electoral systemPublic administrationMulti-party systemCompetition (biology)Political sciencePolitical economyPoliticsEconomicsLawDemocracy

Abstract

fetched live from OpenAlex

Electoral accountability requires that voters have the ability to constrain the incumbent government’s policy-making power. We express the necessary conditions for this claim as an accountability identity in which the electoral system and the party system interact to shape the accountability of parliamentary governments. Data from 400 parliamentary elections between 1948 and 2012 show that electoral accountability is contingent on the party system’s bipolarity, for example, with parties arrayed in two distinct blocs. Proportional electoral systems achieve accountability as well as majoritarian ones when bipolarity is strong but not when it is weak. This is because bipolarity decreases the number of connected coalitions that incumbent parties can join to preserve their policy-making power. Our results underscore the limitations that party systems place on electoral reform and the benefits that bipolarity offers for clarifying voters’ choices and intensifying electoral competition.

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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0020.007
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.364
Teacher spread0.323 · 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 designTheoretical or conceptual
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

Citations41
Published2020
Admission routes1
Has abstractyes

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