Women’s testimony and collective memory: Lessons from South Africa’s TRC and Rwanda’s <i>gacaca</i> courts
Bibliographic record
Abstract
This article uses a comparative approach to elucidate the ways in which women’s testimony operated in South Africa’s Truth and Reconciliation Commission (TRC) and in Rwanda’s gacaca courts, to draw out some important lessons for future mechanisms of transitional justice. The author argues that while restorative justice mechanisms allow more space for including women’s own experiences of human rights violation than conventional trials, they may pose greater danger for those who testify. A significant problem resulting from the narratives of both gacaca and the TRC is the way in which a ‘singular woman victim’ emerges that elides the complexity of women’s experiences in collective memory. It is feared that what has emerged from the official discourse of these two truth-seeking mechanisms is a one-dimensional female victim subject – in South Africa, she is of secondary importance, in Rwanda, she can only be Tutsi, and in both cases she is stripped of all agency, where rape becomes definitive of her experience.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".