Healing Psychosocial Trauma in the Midst of Truth Commissions: The Case of <i>Gacaca</i> in Post-Genocide Rwanda
Bibliographic record
Abstract
Post-conflict governments and multilateral organizations have advocated truth commissions since the end of the Cold War. The mandate of truth commissions has been to combine the rule of law with psychosocial goals in the hope that they will break systemic cycles of violence and facilitate reconciliation. While these commissions emphasize the dimensions of truth telling, apology, forgiveness, and reconciliation, in practice, they are often challenged to fulfill the mandate of healing psychosocial traumas through these dimensions in countries that suffer not only from the traumatic experience of wars and genocide, but also from the multiple psychosocial issues that result from these forms of mass violence. The present article examines the psychosocial role of gacaca, a form of truth commission that was introduced in post-genocide Rwanda in 2002, and argues that relying on gacaca alone to heal psychosocial trauma in Rwanda underestimates the depth of suffering that genocide created both at the individual and collective levels in Rwandan communities. Writing as a Rwandan community-based mental health researcher and practitioner concerned with the mental well-being of individuals and communities that survive mass violence and genocide, I suggest that well-assessed models adapted to the issues at hand should be considered to promote the healing of psychosocial wounds and supplement the work of gacaca in the rebuilding of peace and reconciliation in the country and in similar contexts elsewhere. Mental well-being is central to the sustainable rebuilding and development of countries recovering from wars and 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.025 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".