Speaking for themselves: The importance of enabling Ugandan women to share their story through photography and community dialogue
Bibliographic record
Abstract
“Agriculture is the backbone of the country,” is a commonly heard phrase in Uganda. With agriculture making up nearly a quarter of Uganda’s GDP, and nearly 70 percent of the country’s population working in this sector, this is true. However, the muscle operating said backbone is exercised daily by Ugandan women. Not only do significantly more women work in the agriculture sector than men in Uganda, but women’s contribution is also typically under-estimated and under-appreciated. Usually charged with child-rearing, home-keeping, cooking, and a host of other responsibilities, women often take charge of the farm and garden in smallholder farming families. In addition to these unbalanced and gendered responsibilities, women do not often retain financial control over the money earned from their labor and suffer from physical and emotional abuse from their male counterparts. There is increasing awareness of, and efforts to end, the vast disparities women face within this sector, namely the United Nations’ Sustainable Development Goal No. 5, Gender Equality. This lecture will focus on the independence and self-identity women agriculturalists have as farmers, and how that identity, coupled with their responsibilities to their families, make them a unique and strong powerhouse for agricultural development and social change. Through photovoice methodology, groups of women living in two different communities in Uganda allowed a researcher to conduct a study aimed at delving into their lives as women agriculture producers, and specifically the changes they face in agriculture due to their gender. A surprising phenomenon occurred within this study, wherein all participants decided to take self-portraits of themselves as part of their photovoice. The study resulted in themes that supported these harsh realities, including technical challenges, patriarchal society, physical fatigue, and varied agriculture practices, but also, through their self-portraits, gave evidence of self-identity and independence as “women farmers.” The personal identity and independence felt by these women provide evidence of the responsibility felt towards their family, children, and duties as a farmer.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".