Writing Literature in the Mother Tongue: Challenges and Complexities Facing Native Intellectuals
Bibliographic record
Abstract
Indigenous intellectuals have been facing a dilemma when discussing the issue of writing literature since the decolonization of their nations. In Africa, for example, this issue has been discussed since the 1960s until this time. Which language should be used to write African literature? While some intellectuals have proposed using the native language of the writer, other intellectuals have argued for the opposite position: African literature should be written in a European language. This paper examines the chronological evolution of this debate between African intellectuals since 1962. In this complex and lengthy debate about writing in indigenous languages or European languages, Chinua Achebe argues that using a European language unites the diverse components of the nation. On the other hand of this debate, Ngugi Wa Thiong’o argues that the use of European languages instead of an African language in literature is a form of subjugation.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".