Évolution du verlan, marqueur social et identitaire, dans les films: <i>La Haine</i> (1995) et <i>L’Esquive</i> (2004)
Bibliographic record
Abstract
Résumé Vieil argot des malfaiteurs, le verlan est devenu aujourd’hui un trait distinctif du langage des jeunes français. Comme il a été mentionné dans un précédent article « Le verlan, phénomène langagier et social : récapitulatif. » (The French Review, Vol. 82, 2 : 308-324, 2008), cette pratique langagière se rencontre largement dans la banlieue, plus précisément dans les cités parisiennes. Cet article analysera la façon dont la jeunesse ethnique minoritaire se sert de cette variété de parler pour se révolter contre l’isolation socioculturelle et promulguer sa position d’identité. Nous essayerons de mettre en évidence la correspondance entre le fonctionnement du codage du verlan et son usage comme véhicule d’expression d’une culture distincte par rapport à la culture française traditionnelle. Mots clés : Verlan, identité, banlieues, culture distincte, assignation sociale Abstract Old slang of robbers and the lawless, verlan has become a very distinctive trait of the language spoken today by French youth. As mentioned in a previous article (« Le verlan, phénomène langagier et social: récapitulatif. » in The French Review, Vol. 82, 2: 308-324, 2008) this so called language seems to be prevalent in the Parisian suburbs, better known as the cités. This article will examine how members of ethnic minorities use it in order to rebel against their cultural isolation and to affirm their own identity. We will try to show the relationship between the encoding and the use of this language as an expression of a distinct culture in relation to traditional French culture. Key words: Verlan, identity, Parisian suburbs, distinct culture and social tagging
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".