The Challenge to the Special Criminal Court in the Central African Republic Delivering on its ‘Defence’ Promise
Bibliographic record
Abstract
Abstract The Special Criminal Court (SCC or ‘the Court’) was created with the ambitious goal of fighting widespread impunity in the Central African Republic (CAR) while strengthening local justice capacities, including for defence lawyers. To achieve this, the SCC’s founders established a dedicated Corps spécial d’avocats (‘Special Body of Defence and Victims Lawyers’), complemented by extensive training and mentorship programmes, to align defence capabilities before the SCC with international legal standards. This article examines whether these efforts have effectively ensured strong defence capability and fair trial to the accused before the Court. The first part reviews the SCC’s dedicated Corps spécial d’avocats in the context of CAR’s structural challenges, which make it difficult to meet international judicial standards. The second part focuses on the SCC’s first completed trial (the ‘Paoua’ case) to assess whether the SCC’s promise of fair trial rights fortified by a robust defence and an effective protection of the rights of the accused has lived up to the reality. The article demonstrates a discrepancy, both in general and during the SCC’s inaugural trial, between envisioned defence standards and the reality. It reveals a series of structural challenges, as well as legal hurdles that impeded the ability of lawyers and judges to effectively ensure the highest standard of fairness to the accused. It concludes by highlighting the SCC’s challenge in balancing fair trial standards with the fight against impunity and emphasizes the need for a broader shift in how defence rights are viewed in international and hybrid courts.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| 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".