Peacekeeper-perpetrated sexual exploitation and abuse, and life satisfaction:A cross-sectional study in Haiti
Bibliographic record
Abstract
Peacekeepers from the UN Peacekeeping Operation ‘Mission des Nations Unies pour la stabilisation en Haïti’ (MINUSTAH) have been accused of widespread sexual exploitation and abuse throughout their time in Haiti. However, victims have not received adequate reparations, support, or justice. To date, no research has been done to quantifiably examine how peacekeeper-perpetrated sexual exploitation and abuse (PP-SEA) has affected the lives and well-being of those who have experienced it. Using multivariate linear regression analysis, this research examines the association between PP-SEA and Satisfaction With Life (SWL) among Haitian community members. Among those who shared third-person micronarratives (n=1588), experiencing PP-SEA was associated with higher average SWL scale scores compared with those who did not. There was no association between PP-SEA and SWL among individuals who shared first-person micronarratives (n=887). Potential contextual factors that may have contributed to these findings were examined, e.g. the occurrence of transactional sex, nuance in peacekeeper-civilian relationships, and other negative experiences with MINUSTAH within the unexposed group. These results highlight the complexity of the relationship between PP-SEA and SWL in the Haitian context and provide direction for future research.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".