MétaCan
Menu
Back to cohort
Record W4409312334 · doi:10.31219/osf.io/jydxb_v1

Geographic Proximity Dampens Ideological Disagreement on Municipal Policy Issues

2025· preprint· en· W4409312334 on OpenAlexfundno aff
Martin Horák, Jack Lucas, Shanaya Vanhooren, David Armstrong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIdeologyEnvironmental planningPolitical scienceEnvironmental sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Increasing evidence suggests that municipal elections and policy debates are structured by left-right ideological disagreement. Yet others argue that municipal politics creates non-ideological coalitions of residents and local interests, thereby strictly limiting the role of ideology. In this paper, we use a topic sampling experiment incorporating more than 40,000 policy preferences from more than 4,000 distinct survey respondents to explore the relationship between ideology and geographic proximity across 40 municipal policy issues. While we find clear evidence of ideological disagreement on municipal policy across many issues, we also find that ideological disagreement is dampened when issues are considered in a respondent’s own neighbourhood. Our findings not only help to synthesize contrasting findings in past research, but also illuminate the importance of the “local” for theories of municipal political behaviour.

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)0.009
metaresearch head score (Gemma)0.044
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.357
Teacher spread0.309 · 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

Explore more

Same topicEducational Systems and PoliciesFrench-language works237,207