Beyond Basic Education: Exploring Opportunities for Higher Learning in Kenyan Refugee Camps
Bibliographic record
Abstract
This paper seeks to elucidate the socio-cultural and economic benefits of higher education in refugee contexts. NGO and UNHCR initiatives in Dadaab and Kakuma camps are used as a reference point for discussing the challenges, best practices, and potential of higher and adult learning in contexts of protracted exile. This small-scale, qualitative study seeks to understand what opportunities for higher education exist for those living in Kenyan refugee camps, and do existing opportunities yield “social benefits” beyond those accrued by the refugees themselves? Drawing upon interviews with practitioners, observation in schools and learning centres, and data from refugee-service providers, our findings are primarily descriptive in nature and explore the myriad ways in which opportunities for higher learning can strengthen refugee communities in countries of asylum. We contend that although Kenya’s encampment policies limit the potential economic and social benefits of refugee education on a national level, opportunities for refugees to pursue higher education are still immensely valuable in that they bolster refugee service provision in the camps and provide refugees with the skills and knowledge needed to increase the effectiveness of durable solutions at both an individual and societal level, be they repatriation, local integration, or third-country resettlement.
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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 | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".