Speaking Up: Social Interaction, Disclosure, and Meaning-Making by Youth Born of Conflict-Related Sexual Violence in Rwanda
Bibliographic record
Abstract
Abstract Children born of wartime rape have long been invisible, ostracized, and silenced. This predicament explains blind spots in transitional justice discussions and practice that have neglected, until recently, both their victimhood and agency. Explanations range from (the lack of) international protective norms, persistent stigma in their local societies and communities, and children’s de-agentification owing to them being perceived through their mothers rather than as actors in their own right. Arguably, the inability of these children to articulate their many familial and community challenges has contributed to their exclusion from mainstream discourses and civic participation. Drawing on focus group data with a sample of youth born of conflict-related sexual violence during Rwanda’s genocide against the Tutsi, we analyse how their concerns are made visible in an interactional environment of a peer-led focus group. We highlight the role of fellow youth as moderators and treat focus group data as interactional data where meaning is co-created through social interaction. Applying conversation analysis, which focuses on how speakers interact with each other, and thematic analysis, which examines the content thus produced, we reveal how the youth ‘speak up’ disclosing intimate harms, navigate a sense of groupness and individuality, and find agency in the self.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".