Peacekeepers and Local Women and Girls: A Comparative Mixed-Methods Analysis of Local Perspectives from Haiti and the Democratic Republic of Congo
Bibliographic record
Abstract
The UN may sanction peacekeeping operations (POs) to neutralize armed groups and promote democratization. This research presents perceptions from beneficiaries of assistance related to POs and relations between local women/girls and peacekeepers within two post-colonial contexts: the DRC and Haiti. Using cross-sectional, mixed-methods data collected in Haiti (2017) and the DRC (2018), we performed a comparative secondary analysis to better understand similarities and differences by country and gender in how participants perceived peacekeepers. Congolese participants were more likely to perceive foreign UN personnel as ‘able to offer financial support’, compared to Haitian participants who were more likely to perceive the UN personnel as ‘in a position of authority’ and ‘able to offer protection’. Overall response patterns indicated that both Haitian and Congolese perceived the peacekeeper as responsible for initiating interactions with local women/girls. However, some variations were noted: Congolese male participants were most likely to perceive UN personnel as the initiators of interactions with local women and girls, compared to Haitians and Congolese females, who were more likely to perceive local women and girls as the initiators. Our research presents a locally grounded understanding of how locals perceive POs and peacekeepers relative to their communities and women and girls.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".