“We can make it better you and I” : How Ugandan girls raised awareness of sexual and gender related violence
Bibliographic record
Abstract
Sexual and gender related violence (SGRV) against girls is a dark stain on the fabric of life and the most pervasive breach of human rights worldwide. In spite of laws to combat violence, weak enforcement and discriminatory social norms remain significant problems globally, and urgent action employing ‘novel and innovative’ solutions is called for by the World Health organization and United Nations International Children’s Fund. Using knowledge of African society gleaned from work in school-based health promotion programs in Uganda we engaged girls, gave them a voice, and raised national awareness about the impact of SGRV through a celebrity recorded music video that highlighted the SGRV priorities the girls identified. While the unique power of the combination of images, illustrative scenarios, lyrics and music in the video engaged and informed, still photographs were also integral to the success of this call from girls ‘to make things better”. Our photographic record of this project captured many ethnographic elements of this initiative during its creation; selected images were central to the success of the promotional campaign to disseminate the messaging of the video nationwide; sharing photographs helped to maintain the engagement of team members, especially those unable to be in Uganda; and, our image archive provides a uniquely valuable element for knowledge transfer of ‘what worked and why’ in this initiative. Received: 16 October 2024 | Revised: 09 November 2024 | Accepted: 15 November 2024.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".