Bibliographic record
Abstract
Any country which attempts to establish accountability for past abuses of human rights during the process of democratization faces political, judicial, and ethical problems. With regard to politics, the question of which transitional justice measures are appropriate, functional, and feasible has to be decided for every individual case. A judicial approach has to decide which judicial standards to apply and how to justify prosecution. Finally, the ethical dilemmas of dealing with historical injustices have to be understood. There are no ready-made concepts to define guilt and justice. In many cases it is even difficult to tell the victims from the perpetrators. This study examines the different strategies subsumed under the term ‘transitional justice’ used by emerging democracies to deal with a legacy of human rights abuses. It explores the problems and challenges posed by different mechanisms of reconciliation and societal reintegration. While existing analyses of the contribution that transitional justice measures make to the process of social re-integration stress the importance of consensus among citizens and social groups for the emergence of trust and solidarity, this study suggests also thinking about how conflicts over competing ‘truths’ can help to build social capital and reconciliation. Noting a global diffusion of international legal norms, which means at least formal universal acceptance of basic rights and judicial procedures, it is argued that international justice cannot be a substitute for transitional justice measures taken by the domestic regime itself.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".