Exposure to Violence and Attitudes Towards Transitional Justice
Bibliographic record
Abstract
Transitional justice has emerged to address victims' needs as a means of restoring relations broken by violence. Yet we know little about victims' attitudes towards different transitional justice mechanisms. Why do some victims prioritize retributive justice while others favor other forms of dealing with the violent past? What determines victims' attitudes towards transitional justice policies? To address these questions, we offer a new theoretical framework that draws upon recent insights from the field of evolutionary psychology and links both war exposure and postwar environments to transitional justice preferences. We argue that both past experiences of wartime violence and present‐day social interdependence with perpetrators impact transitional justice preferences, but in divergent ways (resulting in greater support for retributive vs. restorative justice measures, respectively). To test our framework, we rely upon a 2013 representative survey of 1,007 respondents focusing on general population attitudes towards transitional justice in Bosnia two decades after the implementation of the Dayton Accords. Specifically, we examine the impact of displacement, return to prewar homes, loss of property, loss of a loved one, physical injury, imprisonment, and torture on attitudes towards transitional justice. On the whole, our findings confirm our two main hypotheses: Exposure to direct violence and losses is associated with more support for retributive justice measures, while greater present‐day interdependence with perpetrators is associated with more support for restorative justice measures. While acknowledging the legacy of wartime violence, we highlight the importance of the postwar context and institutional mechanisms that support victims in reconstructing their lives.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".