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Record W4243395557 · doi:10.1177/0002764220941238

Party Positions, Income Inequality, and Voter Turnout in Canada, 1984-2015

2020· article· en· W4243395557 on OpenAlexaboutno aff
Matthew Polacko

Bibliographic record

VenueAmerican Behavioral Scientist · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsTurnoutInequalityEconomic inequalityDemographic economicsRedistribution (election)ManifestoEconomicsIncome distributionPoliticsPolitical scienceDevelopment economicsVotingLaw

Abstract

fetched live from OpenAlex

Scholars have focused on the relationship between income inequality and voter turnout cross-nationally and within the United States. However, rising inequality and declining turnout has afflicted Canada to a greater extent than most other Western countries. As turnout in Canadian federal elections began to decline appreciably in the 1990s, inequality began to rise. With multilevel pooled analysis utilizing Canadian Election Studies from 1984 to 2015, party manifesto data, and measures of inequality at the subnational level, this article tests the effects of income inequality on turnout in Canada, and whether the relationship is conditioned by party policy programs. In line with relative power theory, mixed-effects regressions indicate that inequality is negatively associated with turnout, especially for low-income earners. However, latent conflict is manifested when political parties propose greater redistribution, as the negative effects of inequality on turnout are then significantly alleviated.

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)0.003
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 score0.024
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.048
GPT teacher head0.371
Teacher spread0.323 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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