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Record W4416727698 · doi:10.1017/s0003055425101299

Attitudinal and Behavioral Legacies of Wartime Violence: A Meta-Analysis

2025· article· en· W4416727698 on OpenAlexfundno aff
Joan Barceló

Bibliographic record

VenueAmerican Political Science Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersUniversity of CambridgeYork UniversityNew York University Abu Dhabi
KeywordsHostilityCohesion (chemistry)Psychological interventionPoliticsIdentification (biology)Spanish Civil War

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.011
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.505
Teacher spread0.400 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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