Characteristics and Impacts of Conflict-Related Sexual Violence against Men in the DRC: A Phenomenological Research Design
Bibliographic record
Abstract
There is increased evidence of the existence of sexual violence against men and boys in many war-stricken areas. Yet, there are still discrepancies in understanding male victims’ experience in depth. Furthermore, limited research on sexual violence against men in the context of the war in the Eastern Region of the Congo has been undertaken to date. As part of addressing this knowledge gap, a phenomenological study was conducted to shed light and understand the experience of male survivors of sexual violence. The participants were males who experienced sexual violence in the war. Individual semi-structured interviews were conducted. The findings show that participants experienced a wide range of psychological and physical wounds other than rape. Their experience during the event (s) falls under the umbrella term gender-based violence (GBV) which encompasses other forms of harmful acts against one’s will including sexual assault, genital mutilation, acts of penetration with different objects, and cultural inappropriate actions with intention to sexually harass and humiliate. The results show a wide and complex range of short and long-term impact on multiple levels. The findings add clarification and understanding to the controversial and taboo subject around conflict-related sexual violence against men in the Congo. They shed light on how the understanding of gender impacts participants’ masculine identity, their sexual victimization experience, and healing journey.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".