Attitudinal and Behavioral Legacies of Wartime Violence: A Meta-Analysis
Bibliographic record
Abstract
Understanding the legacies of wartime violence is essential for explaining postwar dynamics and informing policy. I present a meta-analysis of 172 quantitative studies across more than 50 countries, assessing the effects of wartime violence on 22 outcomes spanning four broad areas: (a) civic and political engagement, prosociality, and trust; (b) attitudinal hardening toward wartime enemies; (c) identification with one’s own wartime-aligned group; and (d) generalized attitudinal hardening. The analysis reveals mixed effects on engagement, prosociality, and trust: while violence increases some forms of participation, it does not promote voting, trust, or altruism. In contrast, wartime violence consistently heightens hostility toward former adversaries and strengthens in-group identification and favoritism. However, I find little evidence of broader hardening toward actors not directly involved in the conflict. These results challenge optimistic claims that war fosters cohesion and underscore the need for interventions that reduce intergroup hostility, rebuild cross group-trust, and support reconciliation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| 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".