The Distribution and Consequences of Sexual Misconduct Perpetrated by Peacekeepers in Haiti: An Exploratory Cross-Sectional Analysis
Bibliographic record
Abstract
During the United Nations Stabilization Mission in Haiti (MINUSTAH), reports of sexual abuse and exploitation and children fathered by peacekeepers were brought forward to the UN. In 2017, a cross-sectional mixed-methods survey was administered by Haitian research assistants using SenseMaker® (Cognitive Edge, Singapore), a rapid data collection tool that allows participants to share a narrative on a topic of interest. In total, 2541 self-interpreted narratives in relation to the experiences of Haitian women and girls vis-à-vis peacekeepers were collected from a convenience sample of Haitian males and females across Haiti. This exploratory secondary data analysis analyzes whether narratives about sexual misconduct perpetrated by MINUSTAH peacekeepers were associated with rural, semi-urban, or urban locations and investigates the relationship between sharing narratives about sexual misconduct and the desire to engage with the UN/MINUSTAH. After adjustment, narratives addressing sexual misconduct were more likely to be shared in rural locations, compared to urban locations (RRrural: 1.19; 95% CI: 1.03, 1.38). Personal experiences of sexual misconduct were more likely (RRsex: 4.52; 95% CI: 3.34, 6.12) to be associated with rejection of the UN/MINUSTAH, compared to personal narratives of positive/neutral experiences. This research is an empirical steppingstone to understanding the distribution and consequences of peacekeeper-perpetrated sexual abuse and exploitation in Haiti.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".