Bibliographic record
Abstract
Transitional justice has shifted from its primary use in addressing past atrocities of authoritarian regimes to those acts of violence committed during civil wars. Yet the use of transitional justice mechanisms in this new context is not well understood. Drawing from the existing transitional justice literature, this article generates a set of testable hypotheses to explore which factors influence the use of particular mechanisms during and after conflict. It then tests those hypotheses in 151 cases of civil war by using a cross-national data base of all countries in the world and their adoption of transitional justice processes from 1970-2007. This article further provides a preliminary analysis of the success of those mechanisms in obtaining and securing peace. The article concludes that amnesties remain more prevalent than trials during and after conflict, particularly in Africa and Asia. During conflict, higher death tolls are associated with the use of trials and amnesties, and longer wars with the use of all types of mechanisms. After conflict ends, however, longer wars and higher death tolls are associated with accountability, and the presence of international peacekeepers is associated with all types of mechanisms. Finally, we find that transitional justice—regardless of the particular form it takes—does not jeopardize the peace process, and that amnesties may be an effective tool to help end conflict.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".