Bibliographic record
Abstract
Cet article a pour but de dresser l’esquisse d’une histoire de la traduction en Afrique subsaharienne qui couvre les périodes-clé de son histoire ainsi que les principales régions du continent. De l’époque précoloniale à l’époque néocoloniale actuelle, la traduction et l’interprétation ont toujours aidé à faciliter la communication entre divers groupes, que ce soit pour faire le lien entre les souverains et leurs sujets, entre les colonisateurs et les colonisés ou encore, aujourd’hui, entre les communautés linguistiques d’une Afrique hautement multilingue et multiculturelle. La traduction a touché tous les secteurs d’activité en Afrique au cours des siècles, tant sur le plan politique qu’administratif, culturel et religieux. Dans ce contexte, la traduction s’est faite entre diverses combinaisons de langues : arabe, langues africaines et langues européennes. On peut compter aussi des formes traditionnelles de traduction intersémiotique. Tracer une histoire de la traduction en Afrique c’est présenter l’histoire riche et complexe de ce continent, de tous les échanges et contacts qui ont forgé son identité et défini son destin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".