Whose SDGs and Who’s Making Them Happen?: Insights from Women in Uganda
Bibliographic record
Abstract
This Feminist Participatory Action Research project with a cohort of women in Uganda explored how they understood the SDGs in relationship to their lived realities. A postcolonial feminist lens was used to engage with critical ethnographic policy theoretical perspective to consider the research questions: 1) Which SDGs are the most important to you? 2) What do unrealized SDGs look like in your context? 3) What would realize goals look like and what would it take to achieve them?; 4) Who is responsible for achieving the SDGs? Participants had had no prior knowledge of the SDGs but once introduced to them the participants ranked SDG1: No Poverty and SDG4: Quality Education as the highest in importance to them, followed by SDG 3: Good Health and Well-Being, SDG 8: Decent Work and Economic Growth, SDG: 10: Reduced Inequalities, and SDG 12: Responsible Consumption and Production. Participants expressed the implications of unrealized SDGs in their lives as well as the transformative change realized SDGs would bring. They also shared their thoughts on how the SDGs could be achieved in their context. The study recommends that those who are meant to benefit most from the SDGs be consulted on how to achieve them.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".