What's Participation Got to Do with it? Visual Methodologies in ‘Girl-Method’ to Address Gender-Based Violence in the Time of AIDS
Bibliographic record
Abstract
This article uses a retrospective approach to looking at participatory visual work with girls, in relation to addressing gender violence in and around schools in sub-Saharan Africa. Drawing on a variety of work focusing on the visual, including Jo Spence's innovative work from the 1990s (‘What can a woman do with a camera?’), this article seeks to extend and elaborate the idea of feminist visual methodologies in order to uncover the critical issue of girls' safety and security. Participatory work with girls, the article argues, as part of what is referred to here as ‘girl-method’, can be an effective way to reveal the perspectives of girls. At the same time, the use of the visual (and in particular, visual artefacts such as photos, videos, drawings, and digital archiving) invites researchers and communities (including the girls themselves) to re-visit the data and in so doing to explore it further. The article concludes with a call for new and longer-term increased levels of participation when it comes to working with girls, by highlighting the use of the participatory digital archive as a feminist visual tool.
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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 | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".