The discursive pathway of two centuries of raciolinguistic stereotyping: ‘Africans as incapable of speaking French’
Bibliographic record
Abstract
Abstract This article is about the discursive pathway of grammatical structures such as y'a bon ‘there's good’, documenting how, in Hexagonal France, it has become an ‘enregistered emblem’ for indexing sub-Saharan Africans and, by extension, any African as allegedly incapable of speaking French competently. I argue that tracing pathways makes it possible to unveil the intricacy of the historicities of production, circulation, and interpretations of such racially based linguistic stereotypes. One of the central questions addressed in this article is: What are the sociohistorical conditions of the emergence and maintenance of these linguistic stereotypes? I show that these are grounded in long-standing linguistic ideologies of French as an exceptional language and of African languages and, therefore, their speakers, as primitive. I demonstrate how the rise of first age mass culture in the nineteenth century contributed to both the entextualization and the circulation of these stereotypical representations. (Stereotypes, mediatization, enregisterment, language ideology, France, Africa)*
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".