Many a Slip between Cup and Lip: Navigating Noncitizenship and School-to-Work Transitions in Kakuma Refugee Camp
Bibliographic record
Abstract
Abstract This article draws from curricular analysis and ethnographic methods in school and community spaces where young people live, learn, and work in Kenya’s Kakuma Refugee Camp. We describe how formal citizenship education intended for Kenyan citizens is mediated by teachers working in refugee-serving schools. Our analysis shows how these messages, often scarce and decontextualized, orient refugees to project an imagined future of stability, obscuring the skills needed to navigate the uncertainty they will encounter as noncitizens enduring protracted exile. Examining refugee youth transitions after completing their schooling, we document ‘slips’ in the gaps between the civic knowledge, skills, and dispositions promoted in schools and those required within a limited opportunity structure dominated by a relief economy. Beyond school, we examine pathways that young refugees charted through apprenticeships within the informal economy, leveraging their social networks, gaining life skills, and enacting civic commitments while honing more sustainable livelihoods in exile. We argue that education’s value cannot be contingent on belonging or citizenship status and suggest that the contextualized nature of practice-based learning entailed through apprenticeships enables young refugees to create community through everyday participation, where social relationships both facilitate civic learning and are an outcome of that learning.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".