MétaCan
Menu
Back to cohort
Record W4411248556 · doi:10.1007/s10584-025-03957-w

The effect of environmental voter mobilization on voter turnout and environmental attitudes: evidence from a field experiment in British Columbia, Canada

2025· article· en· W4411248556 on OpenAlexaboutno aff
Geoffrey Henderson, Matto Mildenberger, Leah Stokes

Bibliographic record

VenueClimatic Change · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
FundersUniversity of California, Santa Barbara
KeywordsVoter turnoutTurnoutMobilizationPolitical scienceField (mathematics)Social mobilizationDemographic economicsGeographyEnvironmental scienceEconomicsVotingPoliticsLawMathematics

Abstract

fetched live from OpenAlex

Abstract Environmental organizations play an active role in electoral politics, yet these interventions have received far less study than the movement’s efforts at public persuasion or policy advocacy. We examine the effect of environmental voter mobilization on turnout and attitudes among supporters of a Canadian environmental organization. Through a field experiment during the 2017 British Columbia election, we evaluate two prevalent types of get-out-the-vote (GOTV) conversations – a regular GOTV conversation focused on vote plan-making, and an issue GOTV conversation that first engaged respondents in a personal discussion about environmentalism. For both GOTV interventions, we estimate a positive yet borderline significant effect on turnout. Neither GOTV intervention strengthened environmental attitudes, and the regular GOTV intervention may have even decreased en-vironmental issue salience. Our research illuminates the challenges that climate advocates face in mobilizing their constituents, while demonstrating their potential for influence on the electorate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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 teacher head, not a consensus.

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 venueClimatic ChangeSame topicEnvironmental Education and SustainabilityFrench-language works237,207