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Record W2019107276 · doi:10.1017/s000842390277827x

Local Economies, Local Policy Impacts and Federal Electoral Behaviour in Canada

2002· article· en· W2019107276 on OpenAlexaffabout
Fred Cutler

Bibliographic record

VenueCanadian Journal of Political Science · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLocalismVotingLocal governmentEconomicsPoliticsPolitical economyLocale (computer software)Political scienceGovernment (linguistics)Electoral geographyRelevance (law)FederalismPublic economicsEconomyPublic administration

Abstract

fetched live from OpenAlex

The fortunes of local, regional and provincial economies have often been linked to geographical variation in electoral outcomes, and nowhere more so than in Canada. This article examines economic localism in Canadian voting behaviour by estimating a model of voters' decisions in the 1993 and 1997 federal elections. Individual-specific determinants of the vote measured in the Canadian Election Study are supplemented by measures of voters' local economies and of the local impacts of policy changes. Voters punish the federal government for bad times in their locale and for policy changes that hurt the local economy. This effect is independent of what voters think about their own finances and about the provincial and national economies. The electoral impact of the local economy does not depend on whether government is acknowledged as a potent economic actor, or on the voter's level of political information. However, the relevance of the local economy for national-level electoral behaviour can be "primed" by campaign events, just like any other criterion of voting choice. The response to local economic conditions is part of a broader explanation for geographic patterns of electoral support in Canada.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.301
Teacher spread0.272 · 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 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

Citations30
Published2002
Admission routes2
Has abstractyes

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