Same-sex sexual violence in the military: A scoping review
Bibliographic record
Abstract
Introduction: Sexual violence (SV) is a globally prevalent issue, and the majority of research focuses on the historical view of SV as an act perpetrated by men against women. Same-sex sexual violence (SSSV) incidents represent a small proportion of recorded sexual offences, and therefore prevalence and consequences of this have received little attention. Male-dominated occupations, such as the military, are associated with higher rates of SV and data points to a particular vulnerability to SSSV of male service personnel (SP). Methods: This review aims to map the literature pertaining to SSSV in the military. A comprehensive scoping review methodology was adopted, following a rigorous accepted framework. Four databases were searched for English language, peer-reviewed, original research papers that were focused on SSSV in the military context. Results: Eleven papers were identified that met the criteria for inclusion; 10 originated from the United States and one from South Korea. Themes identified included prevalence and nature of SSSV in the military, characteristics of survivors and perpetrators, barriers to reporting, and the outcomes associated with SSSV in the military. Discussion: The evidence that does exist suggests that male SP are particularly at risk of SSSV, and experience poorer psychological and social outcomes due to SSSV compared to female SP and those who experience opposite-sex sexual violence (OSSV). More research is required internationally to provide accurate and up-to-date estimates of prevalence, and to account for cultural and structural differences in military organizations.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".