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Record W4407208612 · doi:10.1111/1475-6765.70001

Who accepts party policy change? The individual‐level drivers of attitudes towards party repositioning

2025· article· en· W4407208612 on OpenAlexafffund
Maurits J. Meijers, Ruth Dassonneville

Bibliographic record

VenueEuropean Journal of Political Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité de Montréal
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversité Laval
KeywordsPolitical sciencePolitical economyEconomics

Abstract

fetched live from OpenAlex

Abstract Experimental research has shown that political parties often, but not always, suffer reputational costs when they change their policy positions. Yet, it is not clear who accepts and who rejects party policy change. Using newly collected observational data from five European countries (Germany, the Netherlands, Poland, Spain and the United Kingdom), we examine the individual‐level determinants of party policy change. We examine support for policy change with a new survey item that directly captures party policy change acceptance. We theorise that acceptance of party policy change varies as a function of individuals' political attitudes such as their level of interest in politics and their ideological positions, as well as their views about democratic decision‐making. Although we find that many citizens agree that change is sometimes necessary and understand the conditions and constraints that lead parties to alter their positions, we also show that populist attitudes have a strong negative effect on accepting party policy change. A textual analysis of an open‐ended survey item furthermore indicates that those who perceive party policy change negatively, associate change with opportunism and power‐seeking. Our results imply that even though parties have some leeway to change their positions when external conditions require them to do so, populist beliefs and anti‐elite sentiment make citizens rather sceptical of the motivations that parties have when they alter their positions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.414
GPT teacher head0.515
Teacher spread0.101 · 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 teacher head, 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

Citations2
Published2025
Admission routes2
Has abstractyes

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