“When It’s a Girl, They Have a Chance to Have Sex With Them. When It’s a Boy…They Have Been Known to Rape Them”: Perceptions of United Nations Peacekeeper-Perpetrated Sexual Exploitation and Abuse Against Women/Girls Versus Men/Boys in Haiti
Bibliographic record
Abstract
Peacekeeping missions have been marred by reports of sexual exploitation and abuse (SEA) against local community members. However, there is limited research on how SEA against women/girls versus men/boys is perceived in peacekeeping host societies. In 2017 we collected micro-narratives in Haiti and then conducted a thematic analysis to understand how peacekeeper-perpetrated SEA was perceived by local community members comparing SEA against women/girls versus SEA against men/boys. Both male and female participants used language which suggested the normalization, in Haitian society, of both transactional sex with and rape of women/girls by UN personnel. In contrast, peacekeeper-perpetrated SEA against men/boys was viewed as unacceptable and was associated with homosexuality and related stigmatization. Overall, our results suggest that in Haiti, inequitable gender norms, the commodification of female sexuality, and homophobia result in SEA against males being recognized as a wrong that elicits outrage, while SEA against women/girls has been normalized. It is important to address the normalization of SEA against women/girls to prevent future violence and to recognize that SEA is also perpetrated against men/boys. Survivor-centered programs, sensitive to the needs of both male and female survivors, are required.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".