Wartime Sexual Violence: Assessing a Human Security Response to War-Affected Girls in Sierra Leone
Bibliographic record
Abstract
Abstract Wartime sexual violence continues to be widespread and systematic in contemporary conflicts. Although the problem is gaining increasing international attention, it has remained, for the most part, peripheral within the domain of security studies. However, the human security agenda may have the capacity to raise the profile of wartime sexual violence and offer a useful framework from which to understand and respond to the unique needs of war-affected girls and women. This article explores the capacity of the human security agenda, both conceptually and practically, to address the plight of girl victims of sexual violence in the aftermath of Sierra Leone's conflict. Drawing upon the perspectives and experiences of three girls formerly associated with Sierra Leone's Revolutionary United Front, the article traces the extreme forms of sexual violence and insecurity girls were forced to endure, both during and following the conflict. It also examines a number of human security efforts implemented in the conflict's aftermath and their impact on the level of empowerment, protection and security of girls. The broader implications of these human security efforts are explored in light of the girls' lived realities in post-conflict Sierra Leone.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".