Genre, heteroglossic performances, and new identity: Stand-up comedy in modern French society
Bibliographic record
Abstract
Abstract This article analyses the ways in which stand-up comedy has been taken up by French comics of North and sub-Saharan African origins as a space of visibility and hearability. Following Bakhtin (1986), who argues that a genre reflects the social changes taking place in a society, I argue that such an appropriation should be considered as an important sociolinguistic fact that gives us privileged access to Hexagonal France's contemporary sociopolitical dynamics. I show that through their display of heteroglossic repertoires (viz. Maghrebi Arabic, several varieties of vernacular French, Hexagonal standard French, mesolectal African French, stylized chunks of English) comics challenge, at least symbolically, France's monoglot and highly centralized linguistic ideology. They also contribute to unsettling France's Republican model, which is marked by the institutional denial of the social and cultural diversity of the French population. The comics use heteroglossic resources to align with and disalign from multiple chronotopes associated with different social personae. From this emerges a new identity,urban,which both encompasses and transcends racial and ethnic categories. By contrast, I show that this identity is constructed through and received by the nonratified audience with ambivalence. (France, stand-up comedy, genre, urban, identity, chronotope, intertextuality.)*
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".