Pre-election voter information interventions led by electoral management bodies can improve voter confidence
Bibliographic record
Abstract
This study investigates the effectiveness of voter information campaigns led by centralized Electoral Management Bodies (EMBs). While existing research highlights the value of voter education in decentralized systems like the United States, its applicability in contexts with centralized EMBs remains underexplored. This article addresses this gap through a survey experiment (N = 1004) conducted in partnership with Elections New Brunswick, testing a multi-topic audiovisual information campaign in a pre-electoral setting. The findings demonstrate that exposure to the intervention significantly increased citizens’ self-reported understanding of the electoral process, their confidence in the fairness and accuracy of the vote, and their comfort with voting by mail. Further analysis reveals these effects were not driven by any single video and that a lack of effect on in-person voting comfort was likely due to a pre-existing ceiling effect. The results confirm that proactive voter education is a powerful tool for centralized EMBs, offering an evidence-based model for reinforcing trust in elections and countering increasingly salient narratives challenging electoral integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".