Development of an Index to Measure the Exposure Level of UN Peacekeeper-Perpetrated Sexual Exploitation/Abuse in Women/Girls in the Democratic Republic of Congo
Bibliographic record
Abstract
Sexual exploitation and abuse (SEA) of women and girls by United Nations (UN) peacekeepers is an international concern. However, the typical binary measurement of SEA (indicating that it occurred, or it did not) disregards varying exposure levels and the complex circumstances surrounding the interaction. To address this gap, we constructed an index to quantify the degree to which local women and girls were exposed to UN-peacekeeper perpetrated SEA. Using survey data ( n = 2867) from the Democratic Republic of Congo (DRC), eight indicators were identified using a combination of qualitative (thematic analysis of narrative data) and quantitative variables. With further development, this index may offer a more comprehensive and nuanced perspective of peacekeeper-perpetrated SEA that can better inform SEA prevention and intervention efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".