Experimenting Hybrid Justice in the Central African Republic: The Special Criminal Court, an Embodiment of Retributive Justice?
Bibliographic record
Abstract
This paper examines the Special Criminal Court (SCC) of the Central African Republic, focusing on its role in the country’s transitional justice framework. Established in 2015, the SCC plays a pivotal role in the country’s transitional justice process. While the Court’s Organic Law leans toward retributive justice, it also envisions reparations for victims. The SCC’s Rules and the country’s Criminal Procedure Law support the Court’s power to issue reparations orders through the “parties civiles” system. Through a doctrinal approach, utilizing textual analysis of case rulings and decisions, the study evaluates the Court’s capacity to offer justice to victims and its effectiveness in awarding reparations. The research highlights key decisions, including the June 16, 2023 judgment that granted financial reparations to victims, the October 23, 2023 ruling on individual and symbolic reparations, and the March 25, 2024 rejection of collective reparations due to cultural opposition from victims. The paper discusses the Court’s challenges in balancing retributive justice with restorative measures, emphasizing the need for external support due to the indigence of the convicted. Findings suggest that while the SCC has made strides in reparations, its capacity to fully realize restorative justice is hindered by financial limitations and procedural challenges. The paper concludes with recommendations to enhance the SCC’s reparative mandate through broader international cooperation.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".