Translanguaging as an Effective Tool for Promoting the Use and Contribution of African Languages to Formal Education: the Nigerian Case
Bibliographic record
Abstract
As in many sub-Saharan African countries, language policy in Nigeria is essentially a continuation of the legacy that was bequeathed to it from colonization, highly favouring the use of English in official domains, including in education. In practice, English remains the main language of instruction in Nigeria at all levels of formal education and is considered the 'language of success' because of the socio-economic opportunities it procures. This policy has however proved ineffective because it continues to marginalize a great number of the Nigerian population rather than equipping it to contribute to development. Since good and effective education remains the best means by which people are empowered to participate in their personal and national development and that this is better achieved through an efficient use of mother tongues, we argue in this paper for the adoption of translanguaging approach in formal education in Nigeria and offer country-specific implementation strategies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".