Gender-Stratified Analysis of Haitian Perceptions Related to Sexual Abuse and Exploitation Perpetrated by UN Peacekeepers during MINUSTAH
Bibliographic record
Abstract
Feminist scholarship has analyzed the gendered dynamics of national- and international-level risk factors for peacekeeper-perpetrated sexual exploitation and abuse (SEA); however, the gendered dynamics within the host country have not been adequately considered. Using the United Nations Stabilization Mission in Haiti (MINUSTAH) as a case study, this research analyzes gender differences within community-level perceptions of SEA. Using SenseMaker® as a data collection tool, cross-sectional qualitative and quantitative data were collected by Haitian research assistants over an 8-week period in 2017. Participants first shared a narrative in relation to MINUSTAH and then self-interpreted their narratives by noting their perceptions, attitudes, and beliefs on a variety of questions. The self-coded perceptions were analyzed quantitatively to determine patterns, and this was complemented with a qualitative analysis of the narratives. Women/girls were more likely to perceive the sexual interactions as “relationships” compared to Haitian men/boys. Furthermore, women/girls were more likely to perceive the peacekeeper as “supportive”, whereas men/boys conceptualized the peacekeeper as “authoritative”. SEA-related policies/programs, such as the UN Trust Fund in Support for Victims of SEA, should engage with local Haitian actors and consider such nuanced and gendered perceptions to maximize community trust and program efficacy.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".