The Shared Strengths & Challenges of Children Born of Conflict-Related Sexual Violence and Their Mothers in Post-Genocide Rwanda
Bibliographic record
Abstract
OBJECTIVE: Given the prevalence of conflict-related sexual violence, tens of thousands of children are estimated to have been born from wartime mass rape campaigns, sexual violence, and forced pregnancy in conflicts around the globe. Despite their vital interconnection, the existing empirical literature has tended to examine either the realities of women survivors of conflict-related sexual violence, or children born of war rape. Much less literature has addressed the realities of both mothers and children and their shared and interrelated experiences. This paper explores the shared post-conflict experiences and realities of children born of conflict-related sexual violence and mothers in post-genocide Rwanda. METHODS: The paper draws on a case study of one mother and her now adult child living in Rwanda. The case study draws from a larger qualitative study using in-depth interviews with 44 mothers and 60 adult children born of conflict-related sexual violence in Rwanda. RESULTS: Participants revealed their shared, long-term post-conflict challenges, which included family and community stigma, marginalization, poverty and health issues. Participants also highlighted their shared strengths and the ways in which they drew enormous strength from one another, facilitating empathy, pride and hope for the future. CONCLUSIONS: Given their shared realities, service provision should aim to engage both mothers and children together, enabling both parties to draw upon shared strengths and mutual support. Moreover, interventions that are community-driven, family-oriented, and culturally-attuned should be adapted to mothers and children, addressing the complexities, and potential ambivalences in their relationship.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".