Specific Reparation for Specific Victimization: A Case for Suitable Reparation Strategies for War Crimes Victims in the DRC
Bibliographic record
Abstract
The vast number of victims as well as their tremendous needs have to be taken into consideration by the International Criminal Court (ICC) that is dealing with some of the war criminals from the DRC. However, while many international instruments provide war victims with rights to reparation, the ICC is limited in terms of who it considers a victim and what it can offer in terms of reparation. The Trust Fund for victims, however, does not suffer these same limitations. Nevertheless, the Trust Fund is grossly underfunded. Thus, it should be supplemented by a national compensation fund for war victims financed by the international community, the DRC as well as States involved in Congolese armed conflict. As we will see later on, although this research is focused to victims of the DRC armed conflict, many of its lessons might have broader implications and apply to other situations involving war-induced victimization.
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.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".