The Rape Epidemic: The Weaponization of Sexual Violence in African Conflicts
Bibliographic record
Abstract
Rape and sexual violence in an African context have transitioned from opportunistic individual attacks in the background of weak states and judiciaries, to systematized, strategic tools of war targeting specific civilian communities – so much so that “during wartime, it’s often more dangerous to be a woman than to be a soldier”. This sexual violence includes rape, mutilation, molestation, forced incest, and sexual enslavement, and is being employed not only by rebel groups, but also by state security forces and militia. The victims of sexual violence include all genders and age groups targeted as a result of various political, economic and social objectives. These objectives have included: boosting troop morale; and political intimidation or humiliation; revenge; ethnic cleansing and disruption of reproduction; spreading terror to induce a community into leaving its land. The emotional, physical, psychological and social affects of the rape pandemic are staggering. This presentation will examine three pertinent case studies of conflicts where sexual violence was/is widespread – the civil war in Sierra Leone; the Second Congo War in the Democratic Republic of Congo (DRC); and the current genocide occurring in the Darfur region of Sudan. These examinations will illuminate how sexual violence has been and can be used for different ends and the ramifications of this new weapon. This presentation will also review the role the international community has played in discouraging sexual violence. Based on these analyses, this presentation will demonstrate that sexual violence is a potent tool of war because of its versatility of use and devastating ramifications, and that its prevalence in Africa is promoted by the subordinated position of females in both African culture and the international system generally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".