Bibliographic record
Abstract
A lot of research has been done around the emergence of Sheng, the new street language in Kenya. Researchers have debated on its status as a language, a creole or a pidgin (Samper: 2002), on its function as an identity-creator (Nyairo & Ogude: 2003), or again on the effects it might have on other vernacular languages (Fink: 2002). Our aim in this article is to consider Sheng in the context of literary creation in Kenya. The Kenyan literary scene has developed tremendously over the last couple of years, notably thanks to the creation, in 2003 of the Kwani? Trust. The Kwani? magazines, that publish pieces from both reknown and aspiring authors, has offered, along with reading events, a space for new voices to be heard, especially among the youth. We will examine in detail two pieces written by Jambazi Fulani in the second issue of Kwani? published in 2003. In those pieces, Sheng is intertwined with English and Kiswahili, as they reflect the frustrations and experiences of today's youth in Nairobi. The aim of our analysis is to focus on the language used, and on how it is a tool that serves the definition of a certain community, the Youth, while enabling the author to play with code-switching and references that make the texts more palimpsestic than they might seem at first. Their uncertain status as pieces of literature will also lead us to reflect on their place in the recent developments in the Kenyan literary scene.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.036 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".