“We Have No Influence”: International Discourse and the Instrumentalisation of Transitional Justice in Burundi
Bibliographic record
Abstract
Burundi may soon reach yet another crossroads in its tumultuous history and on its path towards transitional justice. A contentious draft law for a Truth and Reconciliation Commission awaits approval by the country’s National Assembly, which has raised a number of concerns about the independence of the eventual commission, the likelihood of popular participation in the process and the prospects for criminal justice. But as this practice note seeks to demonstrate, the international community in Burundi may in fact be contributing to the instrumentalisation of the process. The note highlights how the discourse of the international community may partially facilitate a certain duplicity among influential figures within the current government, particularly with respect to compliance with stipulations under the 2000 Arusha Peace Agreement. Two common rationalisations in particular are explained that have seemingly emerged among the international community with respect to transitional justice and that are at the heart of this instrumentalisation: outside imposition and a lack of influence. The note argues that since transitional justice can no longer be regarded as an apolitical process, international actors must be more cognisant of their actions and discourse with a view to ensuring credible transitional justice processes in contexts like Burundi.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.058 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| 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".