Banning sex: who pays the price? The effects of zero-tolerance policies on female peacekeepers
Bibliographic record
Abstract
The contested zero-tolerance policy of the United Nations (UN) regulates sexual relations between peacekeepers and civilians while on mission. Though the policy is intended to protect civilians from sexual exploitation and abuse (SEA), many have argued, conversely, that it exacerbates their precarity and undermines female sexual agency. This study pushes these debates further by examining how sexual regulatory frameworks endorsed by the UN directly and indirectly impact female peacekeepers. Drawing on interviews conducted with police officers, soldiers, and gendarmes, as well as elite decision makers across four countries (Ghana, Zambia, Uruguay, and Senegal), we argue that strict regulation of sexual behaviors can limit women’s ability to meaningfully participate in peacekeeping operations in two ways. First, it incentivizes and legitimizes domestic security institutions’ decisions to extend “protectionist” zero-tolerance policies to female peacekeepers. When taken to the extreme, these policies can be enforced through gender segregation models that marginalize women in the workplace. Second, banning sex with civilians can inversely channel sexual demands toward female peacekeepers. This can contribute to a hypersexualized work environment in which SEA and harassment is rife. These findings reinforce the need to reconsider policy frameworks governing sexual relations and raise urgent questions regarding the sexual agency of female peacekeepers.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".