Au seuil de la transgression : l’instance préfacielle dans les mystères urbains
Bibliographic record
Abstract
Cet article repose sur l’hypothèse que le paratexte (théorisé par Gérard Genette), revêt une pertinence particulière dans le contexte des mystères urbains, corpus important de romans populaires parus à la suite du succès fulgurant des Mystères de Paris d’Eugène Sue (1842-43). Si la notion d’une transgression des limites se trouve au cœur de la diégèse dans ces mystères urbains, je soutiens que la préface en particulier, dont la participation à cette diégèse s’avère insistante, constitue, elle aussi, un « espace de transgression. » À l’appui d’exemples tirés des Mystères de Nancy de Victor Verneuil (1845) et « Mystère-ville » de Jules Lermina (1904-05), parmi d’autres textes, je me penche sur la rhétorique d’initiation spécieuse soulignée dans ces préfaces, ainsi que sur les délais préfaciels et les préfaces fictionnelles. L’emploi caractéristique et ludique de la préface perpétue à la fois la formule lucrative des mystères urbains et l’association séductrice et typiquement moderniste de la ville et du texte.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".