Reducing Gender Gaps in Political Participation with Efficacy Promotion: Evidence from a Civic Education Experiment in Zambia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".