“You Feel Like You Belong Nowhere”: Conflict-Related Sexual Violence and Social Identity in Post-Genocide Rwanda
Bibliographic record
Abstract
Globally, the systematic use of sexual violence in modern warfare has resulted in the birth of thousands of children. Research has begun to focus on this often invisible group and the obstacles they face, including stigma, discrimination and exclusion based on their birth origins. Although sexual violence during the Rwandan genocide has been documented on a massive scale, little research has focused on the relational dynamics between mothers who experienced genocide rape and the children they bore. This paper explores the post-genocide realities of these two under-explored populations, revealing two key tensions in relation to identity-building and belonging. Drawing upon in-depth interviews conducted with 44 mothers and 60 youth, we examine how youth participants’ quest for the truth in forming their own identities is often in conflict with mothers’ efforts to disassociate their identities from sexual violence and genocide. Furthermore, both mothers’ and children’s identities remain ‘caught’ in the rigid ethnic politics of the genocide at the national level. Ultimately, this article highlights that the distinction between the self and the larger politics of post-genocide Rwanda are not easily disentangled, as challenges faced by these families exist at the nexus of the personal and the national, the individual and structural.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".