Architecture for Spatially Just Food System Planning with and for Urban Youth South Sudanese Refugees in Kenya
Bibliographic record
Abstract
Challenges to the health and wellbeing of youth refugees in Kenya are well documented, particularly in refugee camps. However, amid protracted crises in the region, changes in refugee camp legislation and reduced funding are driving the further urbanization of refugees, necessitating a greater focus on understanding the hardships, movements, and challenges young urban refugees face. The focus of this paper is to document research on mapping the food supply chains, including points of intersection for young South Sudanese urban refugees in Kenya, and to identify barriers, constraints, and opportunities for procuring, growing, and selling food. This youth-led study, a follow-up to previous findings, included 40 participants aged 19 to 32. Youth food-resilience stories highlight critical areas for strategic intervention and provide insights into the design of spatially just and economically inclusive urban spaces. Applying a multimethod approach, including food diaries, food maps, and survey tools embedded in a learning platform, the paper weaves a narrative that highlights youth ingenuity in food security and provides insights for governments, policymakers, community leaders, and donors to support responsive, economically inclusive community design in addressing social challenges. Our findings indicate that improving the quality of life and food security of refugee youth is complex and requires a holistic approach. Without education and improvements in livelihoods, including urban agricultural opportunities, refugee youth’s health and wellbeing will continue to be affected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".