‘MINUSTAH is doing positive things just as they do negative things’: nuanced perceptions of a UN peacekeeping operation amidst peacekeeper-perpetrated sexual exploitation and abuse in Haiti
Bibliographic record
Abstract
Haiti’s instability at the turn of the millennium demanded unprecedented changes towards community-based peacekeeping strategies. While deemed successful by some in reducing actualised violence, the UN Peace Support Operation, MINUSTAH, was wrought with allegations of sexual exploitation and abuse (SEA) and mired by the inadvertent introduction of cholera. To understand the host community’s experiences with MINUSTAH, data was collected around seven UN bases from 10 locations in Haiti between June and August 2017. We find that Haitian perceptions on reporting, justice and responsibility for SEA are in juxtaposition with MINUSTAH’s efforts towards stabilisation and security. While participants identified positive perceptions of MINUSTAH that aligned with the novel community violence reduction strategy employed in Haiti, outstanding concerns around SEA remain. We recommend the UN addresses its environment of impunity, alters its practices and policies to be victim/survivor-centred and improve transparency and communication with host communities. The UN must make the systemic changes necessary to address impunity or provide reparations for peacekeeper-perpetrated SEA.
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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 | 0.003 | 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 | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".