Bibliographic record
Abstract
Violence against women and girls is one of the most widespread violations of human rights. It can include physical, sexual, psychological and economic abuse, and it cuts across boundaries of age, race, culture, wealth and geography. It takes place in the home, on the streets, in schools, the workplace, in farm fields, refugee camps, during conflicts and crises. It has many manifestations from the most universally prevalent forms of domestic and sexual violence, to harmful practices, abuse during pregnancy, so-called honour killings and other types of femicide. Violence against women and girls has far-reaching consequences, harming families and communities. For women and girls 16–44 years old, violence is a major cause of death and disability. In 1994, a World Bank study on ten selected risk factors facing girls and women in this age group, found rape and domestic violence more dangerous than cancer, motor vehicle accidents, war and malaria. Studies also reveal increasing links between violence against women and HIV and AIDS. A survey among 1,366 South African women showed that women who were beaten by their partners were 48 percent more likely to be infected with HIV than those who were not. Gender-based violence not only violates human rights, but also hampers productivity, reduces human capital and undermines economic growth. A 2003 report from the US Centers for Disease Control and Prevention estimates that the costs of intimate partner violence in the United States alone exceeds US$5.8 billion per year: US$4.1 billion are for direct medical and health care services, while productivity losses account for nearly US$1.8 billion due to absenteeism. Key words: Violence; Gender; India; Consequences
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".