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Record W4390892399 · doi:10.1177/10780874231224707

Understanding Support for Municipal Political Parties: Evidence from Canada

2024· article· en· W4390892399 on OpenAlexafffundabout
R. Michael McGregor, Jack Lucas, Chris Erl, Cameron D. Anderson

Bibliographic record

VenueUrban Affairs Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsWestern UniversityUniversity of CalgaryToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPoliticsPolitical sciencePublic administrationPolitical economySociologyLaw

Abstract

fetched live from OpenAlex

The province of Ontario, Canada, has a longstanding history of non-partisanship in municipal elections. In this distinctive context, we report results on citizen attitudes toward municipal partisanship using a survey of eligible voters in Canada's most populous province. Using a mixed-methods approach, we focus on three interrelated research questions. First, how much does citizen support for municipal parties depend on the type of party under consideration? Second, what reasons do citizens provide for their preference for either municipal political parties or independents? Finally, what are the correlates of support for municipal parties? We find little support for municipal political parties, and that many voters have sophisticated reasons for preferring either independents or parties. We also identify several factors associated with support for parties. These results provide an in-depth picture of attitudes on municipal partisanship in Ontario, and suggest that public opinion may provide an overlooked mechanism that maintains Ontario's non-partisanship.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.049
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.011
Science and technology studies0.0040.002
Scholarly communication0.0030.001
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 head0.251
GPT teacher head0.396
Teacher spread0.145 · 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

Citations7
Published2024
Admission routes3
Has abstractyes

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