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Record W4414144935 · doi:10.5206/sc.v16i1.22595

Examining Municipal Non-Partisanship: The Relationship Between Mayoral and Provincial/Federal Elections in Ontario

2025· article· en· W4414144935 on OpenAlexaboutno aff
Wade Mass

Bibliographic record

VenueThe Social Contract · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsEmpirical evidencePerceptionElectoral politicsUrban politics

Abstract

fetched live from OpenAlex

Despite the common notion that municipal politics are immune to partisan influence, empirical evidence challenges this assumption. Analyzing data from Toronto, Ottawa, Hamilton, and Windsor, this study reveals a significant relationship between mayoral candidate vote share and provincial/federal party vote share. Specifically, left-leaning mayoral candidate cote share correlates with higher NDP and ONDP support, while right-leaning candidates perform better in areas with increased CPC and PCPO backing. Furthermore, a nuanced and more complex relationship between mayoral vote share and LPC and OLP support is uncovered. These findings challenge the perception of municipal elections as solely driven by local issues, suggesting instead a substantial influence of partisan affiliations. The paper underscores the need to reassess the role of partisanship in ostensibly non-partisan municipal politics and acknowledge the role of partisanship in shaping electoral outcomes.

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)0.006
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 score0.066
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.109
GPT teacher head0.364
Teacher spread0.255 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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