Global Perspectives on Violence Against Women
Bibliographic record
Abstract
Published course description: The course will review the international effort to resist violence against women; the challenge of the discourse of human rights and different cultural narratives; the efforts of organizations, including the United Nations, the World Council of Churches, and other NGOS; the history of resistance and complicity of the church; and pastoral issues in responding. Methods include some lecture, seminar discussion, research, case study. Commentary: The 20th century saw numerous efforts to resist violence against women and empower women’s participation in all forms of society. In the first half of the century these efforts included women’s suffrage, the legal enfranchisement of women, and increased rights for women in situations of domestic violence. Many of these changes occurred in the context of women’s religious activism. The second half of the century saw increased international efforts, including two United Nations protocols that specifically prohibited violence against women and activism by the World Council of Churches, in efforts such as the Decade to End Violence Against Women. Violence against women remains a sensational topic in the media, especially when that violence occurs among exotic populations. Honor killings among Canadian Muslim populations, or rape as a weapon of war in African populations, draw media attention. These stories carry a double edged effect: they
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".