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Record W7109543175 · doi:10.25071/1916-0925.40486

The “Jewish Vote” in Canada’s 2025 Federal General Election

2025· article· en· W7109543175 on OpenAlexvenueaboutno aff

Bibliographic record

VenueCanadian Jewish Studies / Études juives canadiennes · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicJewish Identity and Society
Canadian institutionsnot available
Fundersnot available
KeywordsJudaismVotingOpposition (politics)Group voting ticketGeneral electionRanked voting systemFederal election

Abstract

fetched live from OpenAlex

Contrary to widespread opinion, Jewish voting patterns are not governed solely or sometimes even mainly by the commonly perceived ethno-religious interests of Jews, such as support for Israel and opposition to antisemitism. They are also governed by the same social forces that affect all Canadians. Focusing exclusively on the presumed Jewish interest underlying the Jewish vote prevents us from fully understanding why Jews vote the way they do. This paper seeks to demonstrate as much by examining trends in Jewish voting patterns over time and analyzing the factors that led some Jews to vote Liberal and others to vote Conservative in the 2025 federal general election. Along the way, it also examines the degree to which the Jewish vote helped to elect Jewish candidates. The paper is based mainly on the 2025 Canadian Jewish Voter Study, a web panel survey of five hundred Canadian Jewish adults that was in the field April 15–25, 2025, days before the April 28, 2025, federal general election.

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 score0.027
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.001
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 head0.017
GPT teacher head0.264
Teacher spread0.246 · 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
Published2025
Admission routes2
Has abstractyes

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