Bibliographic record
Abstract
L’objet de cet article est l’analyse d’un phénomène banal mais troublant : la confusion que l’énonciation d’un métatexte portant sur un texte de fiction crée entre le (monde du) texte et le (monde du) métatexte, entre (énoncé) fictif et (commentaire) réel. Cette confusion énonciative réalise une transgression de même nature que celle qu’opère la métalepse narrative, mais c’est le lecteur qui, dans son commentaire, en est le responsable : c’est pourquoi l’on peut parler d’une métalepse du lecteur. L’article propose une définition de cette métalepse du lecteur en traçant sa généalogie théorique, puis ouvre des perspectives en montrant comment la métalepse du lecteur est liée à toute lecture de textes de fiction et en identifiant certaines de ses caractéristiques qui sont communes à tout discours métatextuel.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".