Bibliographic record
Abstract
This paper discusses the effects of ‘Sheng ’ in the education institutions of Kenya, and gives a general overview of its development at the expense of the official languages, that is, Kiswahili and English. While some people have advocated the growth of ‘Sheng ’ as an indication of societal growth in Kenya, others, including scholars, researchers and educationists are on the opinion that the spread of this code impacts negatively on the learners in Kenyan schools and colleges. They base their arguments on the fact that other international languages did not achieve their sophistication through breaking their morpho-syntactic or grammatical rules at the pace in which ‘Sheng ’ is infiltrating Kiswahili. Indeed, according to some, this code should be left to hip hop musicians, public transport touts, drug peddlers and school drop outs. The paper recommends specific researches to be done on the language situation in Kenya especially as far as the spread of ‘Sheng ’ and its impacts on education are concerned. Overview The term Kiswahili here has been used to refer to the language which is widely spoken by the people of Eastern Africa and adjacent islands. Today, the language is spoken in many parts of the world including Africa and Arabia, and is taught in many institutions of learning in Europe, Japan, Korea, USA, England and Canada, among others. The term Swahili is used here to refer specifically to people who speak Kiswahili as their native language, who share a more or less common culture and who live along the eastern coast of Africa, including the islands of Comoro, Zanzibar, Pemba, Mombasa, Lamu and Pate. According to Chimerah (1995), the use of the term had a direct bearing on the advent and ultimate settlement of Arabs among the Bantu Swahili. It is not the purpose of this paper to delve into the origin and development of Kiswahili, but suffice it to say that Kiswahili is typically a Bantu language (about 40 % of its lexicon is Bantu) which borrowed and continue to borrow words and terminologies from other languages to enrich its lexicon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".