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Record W4285589628 · doi:10.1017/s0008423922000439

The Changing Nature of Class Voting in Canada, 1965–2019

2022· article· en· W4285589628 on OpenAlexaffabout
Matthew Polacko, Simon Kiss, Peter Graefe

Bibliographic record

VenueCanadian Journal of Political Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsMcMaster UniversityWilfrid Laurier UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsVotingCleavage (geology)Political sciencePolitical economyDemocracyWorking classPoliticsImmigrationClass (philosophy)Social classDevelopment economicsSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Much attention has been paid to the changing class cleavage that structured political conflict in the twentieth century. In contrast to most advanced democracies, class voting has traditionally been weak in Canada. Using the entire series of the Canadian Election Study (1965–2019), we find the historic pattern of working-class voting for the New Democratic Party (NDP) held outside of Quebec in recent elections, before suddenly falling off in 2019. Starting in 2004, we find a clear and distinct trend where the working class has increased its support for the Conservatives. Although greater partisan sorting is occurring over the economy, cultural issues such as moral traditionalism and anti-immigration are now significant drivers of working-class Conservative support. The findings carry important implications for class-party relationships and reveal that Canada, despite its comparatively weak nativist cleavage, is not immune from the tensions social democratic parties have recently experienced in maintaining cross-class coalitions amid socio-economic structural changes.

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)0.004
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 score0.052
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0040.001
Scholarly communication0.0030.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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

Citations16
Published2022
Admission routes2
Has abstractyes

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