Does female ratio balancing influence the efficacy of peacekeeping units? Exploring the impact of female peacekeepers on post-conflict outcomes and behavior
Bibliographic record
Abstract
The UN. has intensified efforts to recruit female peacekeepers for peacekeeping missions. From 2006 to 2014, the number of female military personnel in UN peacekeeping missions nearly tripled. The theory driving female recruitment is that female peacekeepers employ distinctive skills that make units more effective along a variety of dimensions. Yet skeptics argue that deeper studies are needed. This paper explores the theoretical mechanisms through which female military personnel are thought to increase the effectiveness of peacekeeping units. Using new data, we document variation in female participation across missions over time, and we explore the impact of female ratio balancing on various conflict outcomes, including the level of female representation in post-conflict political institutions, the prevalence of sexual violence in armed conflict, and the durability of peace. We find evidence that a greater proportion of female personnel is systematically associated with greater implementation of women’s rights provisions and a greater willingness to report rape, and we find no evidence of negative consequences for the risk of conflict recurrence. We conclude that the inclusion of more female peacekeepers in UN peacekeeping does not reduce the ability to realize mission goals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".