“– Centrale de la police, quelle est votre urgence ?” : Le style américain du best-seller francophone contemporain
Bibliographic record
Abstract
À partir d’un corpus d’une grosse quinzaine de romans parus de 2000 à 2018, et écrits par Maxime Chattam, Joël Dicker, Marc Levy et Guillaume Musso, cet article entend étudier le « style américain » de ces best-sellers francophones dont les intrigues se déroulent, au moins en partie, aux États-Unis. Il s’agit de mettre en lumière divers procédés d’américanisation du texte, qui à la lecture tendent à produire un « effet traduit », analysé ici sur le plan de la langue. L’effet traduit résulte d’abord, involontairement et marginalement, d’un emploi parfois approximatif de la langue française, qui renforce l’impression de lire une traduction hâtive de l’américain. Mais surtout, le roman regorge d’anglicismes, mots, expressions, voire calques. L’auteur les emploie sans discours d’accompagnement, ou les traduit, et parfois en donne une explication dans le corps du texte ou en note de bas de page. La narration se double ainsi d’un discours explicatif voire touristique, empruntant son genre de discours au dictionnaire et au guide de voyage.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".