Civilising Kakuma: shared experience, refugee narratives and the constitution of a community
Bibliographic record
Abstract
Somalis refugees arriving in the late 1990s in Kakuma, a then mostly Sudanese camp, discovered a hostile and insecure environment. At that time they did not necessarily feel part of a united community. Although they spoke the same language and shared a common nationality, they came from different places and were members of diverse families and clans. Their narratives of arrival show how they faced isolation and adversity together, established successful businesses and became the majority group in the camp, turning Kakuma into a more ‘civilised place’. In this article, I argue that Somalis, and especially the younger generation, developed a broad sense of community by joining forces to tame the camp’s foreign environment, as well as creating a collective narrative of this experience. While the shared experience brought people together, the collective interpretation and shaping of that experience strengthened social bonds and the feeling of interdependence, which in turn elicited an evermore consistent and mythical collective narrative.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.034 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| 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".