Translation, multilingualism and power differential incontemporary African literature
Bibliographic record
Abstract
Abstract Contemporary African literature is, by its very nature, a fertile ground for elucidating the rather symbiotic relation between translation and power differential, given the inherent multilingualism and the implied language hierarchy characteristic of the African postcolonial context. Asymmetry here begins with the unequal power relations between orality and literacy, between oral tradition and writing, between indigenous languages and the languages of colonization. This power differential is enhanced further by the ever-increasing gap between languages of officialdom and the evolving and rapidly assertive languages of creolization. To the extent that African literature is a window into life in contemporary African society, the aesthetic representation of Africanity in writing as well as in colonial or global languages involves translating asymmetry and negotiating, redressing or rewriting power inequalities. This underlying characteristic of African literature dovetails with literary practices in the diaspora whereby migration and identitarian politics draw heavily from the notion of translation as a mechanism for expressing discourses of resistance to oppression and asymmetrical power relations. This chapter seeks to lay bare the underpinnings of power differentials in contemporary African literature and to highlight the role of translation in resisting asymmetry and rewriting power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".