Living in Yesterday’s Terror: The Impact of Civil War Violence on the Post-War Election in South Korea
Bibliographic record
Abstract
To what extent does civil war violence affect voting behaviour after the war? Evidence from South Korea after the Korean War suggests that the voter’s support or denunciation of civil war violence perpetrators on election day depends on how well the perpetrator controls the context of violence after the war. Using an original precinct-level dataset of the occurrence of civil war violence and the results of the 1950–1954 general elections in South Korea, I find that civil war violence performed by the dominating perpetrator, the South Korean government, had little effect on their vote shares, while violence performed by the opposition had a significant effect on increasing the dominating perpetrator’s vote shares. By antagonising and repressing the victims of their violence as the enemy of the nation, the South Korean government empowered the victims of opposition violence while silencing those victimised by them during the war. It was only after the collapse of the Rhee regime in April 1960 that the civil war violence victims of the South Korean government could mobilise for emancipation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".