Election violence prevention during democratic transitions: A field experiment with youth and police in Liberia
Bibliographic record
Abstract
Abstract During highly uncertain, post-conflict elections, police officers and youth-wing party activists often engage in low-intensity electoral violence, which cannot be readily explained by national-level, institutional, elite-level strategic incentives for violence. Responding to calls to examine ‘non-strategic’ election violence, this article examines both the key actors most likely to perpetrate violence on-the-ground, and the micro-level perceptions underlying their decisions. In post-conflict contexts, police and youth-wing party activists operate within uncertain, information-poor and weakly institutionalized settings. Consequently, their pre-existing attitudes towards the use of violence, democracy, electoral institutions and towards other political actors influence how and when they engage in electoral violence. We proposed two different paths for reducing this uncertainty and improving attitudes: a) civic engagement programs and b) experience with ‘crucial’ elections, which we defined as the first post-conflict election following the withdrawal of external guarantors of electoral security. We employed a unique, locally led field experiment and panel data collected during the 2017 Liberian election to demonstrate how a ‘crucial election’ improved attitudes of both police and youth activists, while civic engagement programming did not. The findings suggested that elections following major structural reforms may reinforce democratization by improving the attitudes of the actors most likely to participate in violence.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".