A Research on Violence Against Women: Are the Trends Growing?
Bibliographic record
Abstract
Objectives: Violence against women is a global public health problem. Although there has been much research done on violence against women, there are few studies that provide the current scientific production. Methods: In this study, bibliometric analysis has been used to evaluate the 1984 documents from 1986 to 2020 based on the Scopus database. These documents were analyzed quantitatively by the Bibliometric R Package and the VOS viewer software. In addition, the 20 top-cited papers were analyzed qualitatively. Results: The research findings show that the United States is a leader in this field with the most highly cited articles and also the greatest number of publications followed by the United Kingdom, Canada, Australia, and South Africa. A total of 1984 documents were collected from the Scopus database and were analyzed in the Bibliometric R Research Package and the VOSviewer software. The results demonstrated that the average citations per year for each document were 23.39% and the annual scientific production growth rate was 16.86%. The keywords analysis indicates that most articles focus on “sexual violence”, “sexual assault”, “intimate partner violence”, “violence against women”, “sexual abuse”, “domestic violence”, “child sexual abuse”, “prevention”, and “rape.” Sources such as the “Journal of Interpersonal Violence”, “Journal of Violence Against Woman”, “Journal of Violence and Victims”, “Psychology of Women Quarterly”, “Journal of Adolescent Health”, “Journal of Consulting and Clinical Psychology”, “American Journal of Public Health”, “Journal of Consulting and Clinical Psychology”, and “American Journal of Public Health”, and “The Lancet” are the top most productive in this field. Discussion: Examining the articles showed that the vast majority of women have experienced verbal, sexual, intimate partner violence, cyber harassment, and so on.
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 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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".