A New Typology of Electoral Violence: Insights from Indonesia
Bibliographic record
Abstract
Existing literature on election violence has focused on how violence suppresses voter participation or shapes their preferences. Yet, there are other targets of election violence beyond voters who have so far received little attention: candidates and government agencies. By intimidating rival candidates into dropping out of the race, political hopefuls can literally reduce the number of competitors and increase their likelihood of winning. Likewise, aspiring candidates can target government agencies perceived to be responsible for holding elections to push for electorally beneficial decisions. In this paper, we introduce a new typology of electoral violence and utilize new data of election violence that occur around executive elections in Indonesia from 2005 through 2012. The types of violence we identified differ in these ways: a) Of all cases of electoral violence observed in this article, most incidents were targeted towards candidates and government bodies; b) candidates are generally targeted before elections, whereas voter-targeting incidents are spread out evenly before and after elections and government-targeted violence tends to occur afterwards; c) pre-election violence is concentrated in formerly separatist areas, but post-election violence is more common in districts with prior ethnocommunal violence. These distinctions stress the importance of examining when and why different strategies are adopted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".