Language Use in the Multilingual Classroom Settings of West Africa: A Review of Selected Literature
Bibliographic record
Abstract
Linguistically, most Africans are multilingual entities. Extremely, the seventeen (17) West African states display this feature. Thus, in a typical L2 classroom in Africa, the learner is likely to come into contact with several languages. These languages are mostly the official languages(s), the second or third language(s), the international language, and the indigenous languages spoken by both the learners and the teachers. Sometimes, the official language(s) is/are selected indigenous languages (for example, Igbo, Yoruba, and Hausa, in the case of Nigeria). In some cases, the second language is the international language used for official engagements and international discourse. In Western Africa, Ghana is one such country that uses English as both the official and international language. When learners from diverse sociocultural backgrounds are exposed to several languages in a particular classroom setting, a lot of processes emerge. One of such processes is nativisation or indigenisation or localisation of the formal classroom language. This is the process where language learners use the formal classroom language in a manner that suits their communicative needs. This paper is a review of selected empirical studies on the use of language in the multilingual classrooms of selected African countries. The cases and papers were purposively sampled from five West African states of Cameroon, Ghana, Guinea-Bissau, Niger, and Nigeria. This paper argues that language contact processes such as localisation, pidginization, and creolization are not aberrant forms per se; and since they serve the informal linguistic needs of multilingual second language learners, they should be given a place in language use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".