If Only Justice Could Heal or Make Whole: Hard Lessons from Rwanda’s Legal Responses to Genocide and Mass Atrocity
Bibliographic record
Abstract
My reflections on the limits of legal responses in Rwanda stem from both academic and lived experience. I served as Rwanda’s Deputy Minister of Justice (1996–1999), Prosecutor General (1999–2003), and Deputy Chief Justice (2003–2004) during the period after the genocide. In those roles, I had access to a large volume of information drawn from policy- making roles, case files of members of my staff, as well as my own fieldwork relating to the suffering of victims of the genocide. While I obviously believe that justice for those crimes is not in vain, my work with survivors left me with an acute sense of the inadequacy of legal responses to mass atrocity
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.045 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".