KiSwahili: the lingua franca of Nakivale Refugee Settlement in Uganda
Bibliographic record
Abstract
This study examines the complex linguistic situation in Nakivale Refugee Settlement (Nakivale), and demonstrates that, for practical and pragmatic reasons, KiSwahili is emerging as the lingua franca, which needs to be actively promoted by the authorities. Nakivale is located in southwestern Uganda. The refugee settlement was started in 1959, and is of a more permanent nature. An estimated 80,000 people (refugees and Ugandans) reside in Nakivale, in the same settlement, sharing, among other things, health facilities, water, land, administrative services and educational facilities. The huge diversity of nationalities (nine, excluding one Liberian) translates into massive linguistic complexity, making communication across nationalities a nightmare. Lack of a common language breeds mistrust, tension, animosity and at times outright hostility. It is natural that a lingua franca is emerging in Nakivale; it is logical, practical and pragmatic that the lingua franca is KiSwahili. This paper recommends that the Government of Uganda, particularly the administrators of the settlement, together with humanitarian workers actively promote KiSwahili, by, for example, teaching it in schools in Nakivale and creating informal KiSwahili classes for adults. It is envisaged that a common language would bring harmony, stability and meaningful social interaction among the refugees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".