Memory Controversies in Post-Genocide Rwanda: Implications for Peacebuilding
Bibliographic record
Abstract
Intrastate wars and genocides result in devastating losses and leave deep and lasting scars on those who survive. Making space for civilians to share their experiences of violence and to have them publicly acknowledged—especially by their own governments—can be important parts of (re)knitting the social fabric. This article focuses on the experiences of ordinary Rwandans during and after their country’s civil war and genocide. It is centered on excerpts from a series of field interviews and highlights Rwandans’ memories in their own words. This article contrasts this cross-section of civilian narratives with the official memories of violence that the national government disseminates through memorials and schools. The central argument is that, in order to legitimate its rule, the Rwandan government selectively highlights some memories of violence, and represses others, and that this is likely to hinder sustainable peace.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".