Repairing transgenerational harm in the Ongwen case before the International Criminal Court: The next frontier in reparative justice for international crimes?
Bibliographic record
Abstract
The Ongwen case marks a turning point in international criminal justice in several respects. It presented an opportunity for the International Criminal Court (ICC) to clarify the concept of transgenerational harm and reassess the standard of evidence required to prove this type of harm. One of the novel and fundamental issues refers to repairing transgenerational harm. The concept of transgenerational harm is undertheorised in the international (criminal) law literature. It remains a novel question for the ICC), being first addressed in the Katanga case in 2017. The limited jurisprudence and scholarship on this matter place the ICC in uncharted territory, requiring it to decide on and develop a coherent and consistent understanding of reparative justice concerning transgenerational harm. This article focuses on transgenerational harm in the specific context of the Ongwen case, its reparation orders, and in light of the evolving jurisprudence of the ICC. As this is unlikely to be the last case where the Court is called upon to assess reparations for this kind of harm, the Ongwen case presents a unique opportunity to reflect on the implications of repairing transgenerational harm in relation to international crimes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| 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".