MétaCan
Menu
Back to cohort

Voters Under Pressure

2022· book· en· W4317368653 on OpenAlexaff
Ruth Dassonneville

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsVotingArgument (complex analysis)Volatility (finance)Positive economicsGroup decision-makingEmpirical evidencePolitical scienceEconomicsEconometricsPoliticsLawEpistemology

Abstract

fetched live from OpenAlex

Abstract In many established democracies, vote choices are growing more volatile over time. This book assesses how changes in voters’ decision making process have contributed to this change. The first part of the book examines the evidence for the claim that the increase in volatility results from a shift in weight from long-term to more short-term determinants of the vote choice. This overview and the analyses that are presented highlight the limitations of existing theories of electoral change and call for novel explanations for voter volatility. The second part of the book makes the argument that group-based cross-pressures are an important source of volatility. Such cross-pressures, that results from the fact that citizens’ socio-demographic characteristics and group-memberships pull them in different partisan directions, imply voters’ decision making process lacks constraint, ultimately making the vote choice more volatile. The book tests this argument by means of longitudinal election survey data from eight established democracies, which allows tracing changes in the vote choice process since the 1950s. The over-time increase in levels of group-based cross-pressures provide a first indication of their importance for explaining over-time changes in voting behaviour. The empirical analyses that are presented next provide more evidence that is in line with this theoretical argument. The results show that group-based cross-pressured voters are less likely to be partisan, are less guided by short-term determinants when choosing a party, make their vote choice later and switch parties more. Analyses that make use of panel survey data confirm these key findings.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.626
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.000

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 head0.058
GPT teacher head0.352
Teacher spread0.294 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations28
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicElectoral Systems and Political ParticipationFrench-language works237,207