Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it