Repositioning French Language Education for National Integration and Development
Bibliographic record
Abstract
Nigeria's linguistic diversity rather than being a blessing as it was at Pentecost has been a bane of national development and integration as it was at Babel. Nigeria parades over four hundred indigenous languages (Crozier and Blench, 1992) and two official foreign languages (English and French). Of these various local languages, three stand out for national prominence. While most developed countries are monolingual with the significant exception of Canada that is bilingual, with English and French spoken with equal degree of proficiency, most developing countries are bilingual or multilingual. This is to say that monolingualism is not the only yardstick for development. Therefore, if the French language is well repositioned and restrategised, it may definitely take Nigeria to an enviable height. This paper therefore defines the concepts of language, national integration and development. It highlights the apparatus on ground to make the French Language more functional in an Anglophone Nigeria. The relevance of French to National Development is discussed, and strategies towards making it more functional for national development are pointed out in concrete terms. Keywords: Language, national integration, national development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".