“Stop the War on Women’s Bodies”: Facilitating a Girl-Led March Against Sexual Violence in a Rural Community in South Africa
Bibliographic record
Abstract
Internationally, there is increasing recognition that girls and young women are engaged in various forms of resistance and activism to address social issues that impact their lives. However, in contexts that are hostile to girls and young women due to unequal gender norms, girls are often silenced, and activism is met with disapproval and even violence. Where activism does occur, the voices of girls and young women engaged therein are often ignored. It is for this reason that adult activists often team up with and support girls and young women’s activism at the community level. In a rural community in South Africa, adult researchers and activists from a university worked with participating girls and young women to address sexual violence in the community. Within this collaborative project to commemorate the annual National Women’s Day, and with the assistance of the adult researchers, the participants organized an awareness march against sexual violence in their community. This article examines this intergenerational collaboration and the role of adult feminists in enabling girls’ voices to be heard regarding their experiences of sexual violence and how it might be addressed. The article concludes that in contexts where unequal gender norms produce high rates of gender-based violence, including sexual violence against girls and young women, girl-led activism is likely to occur with the support of adult activists with access to resources, and whose skills and relative authority gives them access to decision-makers and policy makers in communities and organizations.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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, 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".