Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it