Re-examining the Status of the English Language in Anglophone Western Africa: A Comparative Study of Ghana and Nigeria
Bibliographic record
Abstract
This paper re-examines the status of English as a Second Language (ESL) in Anglophone Western Africa by comparing its use in Nigeria and Ghana. The research is based on the premise that the medium of instruction impacts the quality of education (Ferguson, 2013). The significance of the research is that it is one of the first studies to compare the standard of English language usage in the two countries to establish whether there is a positive link between the quality of education and the language of instruction (Williams, 2011). Predicated on a critical literature review, some of the issues and perspectives analysed include educational language policies, the attitude of students, the quality of teachers and the prospects of the language in the two countries. Findings indicate that the implementation of educational language policies remains an important challenge in the two countries, as there has been a falling standard of English usage (though Ghana has a higher standard of English language usage) and a dearth of English specialists. In identifying the factors that impact on the quality of education in Nigeria and Ghana, the paper concludes that English has significant potential in both countries, and if relevant strategies for its improvement are adopted, both countries will benefit from the socio-economic gains inherent in its adoption and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".