South Sudanese Urban Youth Refugees in Kenya: Policy Response
Bibliographic record
Abstract
This study analyzes the experiences of urban refugee communities, precisely the challenges young South Sudanese refugees living in Kenya face. We divert from the comprehensive examination of refugees in camps to focus on urban youth amidst Kenya’s refugee policy changes and the COVID-19 pandemic. With the support of South Sudanese community leaders, our study engaged 41 participants (aged 19-32) who had recently relocated to Nairobi or Nakuru, Kenya. Participants engaged in semi-structured discussions about food security and other daily challenges related to their urban refugee experience. This study draws on postcolonial feminist theory to contextualize the gender-specific dimensions of food insecurity, centering analysis in discussing historical power structures, migration patterns, urbanism theory and geopolitical influences contributing to the experiences of South Sudanese urban youth refugees in Kenya. Study participants, irrespective of location, encountered corruption, limiting policies, and conflicting identity formation, with women specifically highlighting self-identity, dignity, and family as critical to supporting their resilience. Participants emphasized the impact of COVID-19 on community cohesion, particularly in shared meals. However, their agency was hindered by movement restrictions, invisible fences, or barriers exacerbated by unequal support and aid distribution. The research advocates for the formulation of clear African contextualized urban-based policies and migration systems that prioritize the needs of urban refugees to safeguard their rights and uphold human dignity. Collaborative engagement with all stakeholders within local communities—especially youth refugees—is necessary to develop effective urban policies that promote stability, economic advancement, and social integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".