Sexual exploitation, abuse and harassment in humanitarian contexts
Bibliographic record
Abstract
Considerable investment has been made in recent years to address sexual exploitation, abuse and harassment by aid workers in the humanitarian sector. However, such sexual misconduct remains a persistent, complex challenge with wide-ranging impacts, including on sexual health, for individuals and communities hosting humanitarian responses. This article considers the state of research regarding sexual exploitation, abuse and harassment in humanitarian contexts, and identifies gaps in the evidence base necessary for reinforcing prevention and response efforts. We first report what we know about sexual exploitation, abuse and harassment, including its impacts on sexual health, risk factors and the permissive enabling organizational cultures. We then identify several critical knowledge gaps that must be addressed for more effective future strategies and approaches to prevent and respond to sexual exploitation, abuse and harassment. We discuss system-wide knowledge gaps, such as lack of evidence on programming approaches and effectiveness of prevention and accountability mechanisms. We explore potential options that health-care programming provides for preventing and responding to sexual exploitation, abuse and harassment. We also describe population-level knowledge gaps, including in patterns of perpetration and specific challenges faced by marginalized groups. We conclude with reflections for a future integrated research and policy agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".