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Record W4290629774 · doi:10.1017/s000842392200052x

Government Preferences, Vote Choice and Strategic Voting in Canada

2022· article· en· W4290629774 on OpenAlexaffabout
Alex B. Rivard, Mackenzie Lockhart

Bibliographic record

VenueCanadian Journal of Political Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGovernment (linguistics)VotingSingle non-transferable voteGroup voting ticketPolitical scienceLiberal PartyDemocracySplit-ticket votingContingent voteSpoilt voteSurvey data collectionPublic administrationPolitical economyEconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

Abstract Do voters use information about and preferences over who will form government in their vote choices? Voters might have preferences over both which party wins the most seats and what type of government that party can form, which they can use to inform their vote choice. To answer this question, we examine the influence of preferences over government types and compare them to trends in party support in the 2019 Canadian federal election. Using rolling, daily cross-sectional survey evidence from the Canadian Election Study, we find that preferences over government type are strongly related to vote choice and that this relationship depends on the perceived viability of the preferred party. We also find that this relationship differs outside and within Quebec: outside Quebec, only the Liberal Party suffers among voters preferring minority governments, while within Quebec, the Liberal Party, New Democratic Party and Conservative Party all struggle to hold on to voters who prefer minority governments.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.040
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.051
GPT teacher head0.304
Teacher spread0.253 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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