Measuring Violence Against Women: A Global Index
Bibliographic record
Abstract
Violence against women (VaW) is a widespread crime and violation of the rights of women. It is present in every country without exception and crosses boundaries of culture, class, education, income, and race. Despite the magnitude of the literature and the abundance of publications on this problem, the field lacks a comprehensive and homogeneous way to measure and compare the extent of VaW across countries. Proper quantification of this problem is needed to develop preventive policies and strategies to reduce it. This article develops an index of VaW (VAWI) with global scope and multidimensional approach for 102 countries. It is an original index that calculates the total level of VaW by capturing information from the main VaW types (physical, sexual, psychological, and economic violence) in a single value between 0 and 1, where 0 denotes complete absence of violence and 1 the highest level of violence in a country. The proposed index is easy to compute and is comparable across countries. Our main results show that the nations with the highest levels of global VaW are Yemen, Senegal, Oman, Cameroon, and Uganda. The countries with the lowest levels are the Northern European Countries, Canada, and Malta. This VAWI makes a novel and important contribution to the study of gender issues. It can be used not only to monitor the statistics on VaW data within countries over time but also to make comparisons among countries. Further, it could be useful in designing new policy initiatives to reduce VaW.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".