Bibliographic record
Abstract
Par transposition, on entend ici le glissement d’un monde à un autre, et notamment le transfert du non-fictif dans la fiction. Parce qu’il introduit des données référentielles dans l’univers du roman, le roman autobiographique est un cas exemplaire de ce processus d’hybridation de la fiction. Face au modèle théorique dominant - fictionnaliste -, qui se refuse à penser l’hybridité ontologique du roman, on s’attache donc à légitimer la notion de roman autobiographique, dont la pertinence théorique peut être établie aussi bien dans les faits (genèse des textes) qu’en droit (dans la perspective contractuelle des pactes). Le présent effacement, dans le discours critique, du roman autobiographique tenant, en partie, à la vogue de l’autofiction, on s’emploie à distinguer les deux genres (en insistant sur le critère onomastique) ; mais ce débat n’est pas seulement générique, il a des enjeux scientifiques, esthétiques, idéologiques et éthiques.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".