THE DARKER SIDE OF TRANSITIONAL JUSTICE: THE POWER DYNAMICS BEHIND RWANDA'S<i>GACACA</i>COURTS
Bibliographic record
Abstract
ABSTRACT In this article, I argue that the praise of legal and political analysts who perceive Rwanda'sgacacacourts as a model of locally grounded and culturally relevant transitional justice is unfounded without consideration of the broader power dynamics in which justice is delivered. Drawing on life history interviews with 37 Rwandan peasants resident in the south-west of the country, I argue that the claims of the Rwandan government that itsgacacacourts are promoting peace and reconciliation must also assess the impact of local justice mechanisms on those subject to its demands, namely ordinary people. In the case of Rwanda'sgacacacourts, local-level analysis illuminates a darker and largely unexamined aspect of transitional justice – the playing out of local power dynamics and the social and political inequalities masked by the pursuit of justice and reconciliation. My study cautions against a wholesale endorsement of thegacacacourts as an effective and legitimate form of transitional justice. Instead, it is a mechanism of state power than works to reinforce the political power of the ruling RPF and to ply international audiences with the idea that Rwanda is ‘a nation rehabilitated’ from ‘the scourge of genocide’.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".