Global prevalence of non-partner sexual violence against women
Bibliographic record
Abstract
Sexual violence against women is a human rights violation and public health concern, with serious implications for women's physical and mental health. Reducing non-partner sexual violence, including rape, sexual assault and other forms of non-contact sexual abuse, is one of the main indicators of the sustainable development goals. World Health Organization estimates, based on available prevalence data from 137 countries between 2000 and 2018, showed that, globally, 6% of women aged 15-49 years reported experiencing sexual violence in their lifetime from someone other than an intimate partner, with prevalence rates varying across regions. However, the reporting, measurement and documentation of the global extent of non-partner sexual violence against women is methodologically challenging, resulting in a gross underestimation of its magnitude and impact. To prevent and respond to this issue, policy-makers must consider interventions on education, access to relevant health-care services, public awareness, and effective and comprehensive legislation. To better estimate the prevalence of both sexual violence overall and non-partner sexual violence, it is essential to continue to strengthen the measurement of non-partner sexual violence, including the types of acts asked about and the mode of interviewing. Further research is needed to understand the cumulative impact of different forms of sexual violence on the lives of women and girls, including sexual violence during childhood and its associated risk with further exposure. Funding is required for more research and implementation of interventions to prevent and reduce all forms of violence against women and girls, including sexual violence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".