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Record W7117305961 · doi:10.1017/s000712342510121x

Reducing Gender Gaps in Political Participation with Efficacy Promotion: Evidence from a Civic Education Experiment in Zambia

2025· article· en· W7117305961 on OpenAlexfundno aff
Gwyneth McClendon, Elizabeth Sperber, O’Brien Kaaba

Bibliographic record

VenueBritish Journal of Political Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
FundersYork UniversityPrinceton UniversityUniversity of DenverMassachusetts Institute of TechnologyUniversity of Notre DameHarvard University
KeywordsGender gapPoliticsCivic engagementInformation gapField (mathematics)Political efficacy

Abstract

fetched live from OpenAlex

Abstract In many countries, women participate in politics at lower rates than men. This gap is often most pronounced among young adults. Civic education programs that provide non-partisan political information are commonly used to try to close this gender gap. However, information alone rarely reduces the gap and sometimes exacerbates it. We extend the literature emphasizing the psychological resources women need to participate by evaluating whether embedding efficacy-promoting messages within civic education reduces gender disparities in participation. In collaboration with Zambian civic organizations, we implemented a field experiment before national elections that randomly assigned urban young adults to an information-only course or the same course with efficacy-promoting messages. We find that the efficacy-promoting course substantially increased young women’s political interest and participation, narrowing gender gaps across a wide range of behavioral and attitudinal outcomes. We discuss the study’s implications for theories of political participation and the design of civic education.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.416
Teacher spread0.353 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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