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Record W4362615328 · doi:10.1080/13510347.2023.2191191

Voting behaviour under doubts of ballot secrecy: reinforcing dominant party rule

2023· article· en· W4362615328 on OpenAlexaff
Kai Ostwald, Guillem Riambau

Bibliographic record

VenueDemocratization · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBallotSecrecyVotingPolitical scienceOpposition (politics)IncentiveDemocracyBullet votingSecret ballotLaw and economicsDisapproval votingSocial psychologyEconomicsPsychologyLawMicroeconomics

Abstract

fetched live from OpenAlex

Ballot secrecy is a cornerstone of electoral democracy, since its real or perceived absence can make voters reluctant to express their true preferences. Through survey data from Singapore, we show that doubts over ballot secrecy can alter voting behaviour even when the vote is secret and there are no individually-targeted punishments or incentives; specifically, they lead a small subset of Singaporean voters to support the dominant party, despite a preference for the opposition. We also examine individual-level correlates of doubting ballot secrecy: a tendency towards belief in conspiracies and distrust of the mass media are the strongest predictors. Finally, a counterfactual exercise demonstrates the sensitivity of election outcomes to marginal vote swings; it suggests that doubting ballot secrecy can secure the dominant party a small number of additional parliamentary seats, thereby buttressing dominant party rule without requiring any concerted action or overtly repressive measures.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.047
GPT teacher head0.343
Teacher spread0.296 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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