Bibliographic record
Abstract
This is a case study of how the knowledge and practice of translation can be put to the service of history. The study addresses in particular the efforts of a renowned African scholar, Cheikh Anta Diop, in tracing the African antecedents of the Ancient Egyptian civilization. The focus is on Cheikh Anta Diop’s mastery and translation (or deciphering) of Ancient Egyptian hieroglyphs and Meroitic script into a modern written language script. Diop’s ultimate goal was to establish a historical and cultural connection between ancient Egypt and Black Africa, through a systematic translation of certain keywords and a comparative linguistic study of Ancient Egyptian and African languages. Diop was also interested in refuting arguments or hypotheses regarding the untranslatability of cultures, particularly between so-called primitive languages and modern, highly scientific languages. Although the debate about the link between Black Africa and Ancient Egypt had lost steam by the end of the 20th century, Diop’s work still carries weight in some scholarly circles, especially given the contemporary ideological importance of issues related to ethnicity and “identities” in disciplines such as postcolonialism and cultural studies. Whatever position one chooses to take on the debate on the subject of a ‘Black Egypt’, one cannot deny the considerable impact of Diop’s scholarship and, from a translation studies perspective, his role as an agent of translation in the writing of history.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".