L’intertexte du 16<sup>e</sup> siècle de <i>L’île de la Demoiselle</i> d’Anne Hébert : Marguerite de Navarre, André Thevet et François de Belleforest
Bibliographic record
Abstract
Dans L’île de la Demoiselle, Anne Hébert s’inspire de la légende de Marguerite de Roberval, relatée par quatre textes de la Renaissance. L’originalité de la pièce d’Anne Hébert tient au fait qu’elle tire parti de l’ensemble de ces récits. La dramaturge se montre une lectrice attentive de Marguerite de Navarre, pour ce qui est du personnage de la femme forte, d’André Thevet, s’agissant des toponymes et du motif de l’exil, et de François de Belleforest, pour ce qui est de la cruauté de Roberval et des amours morganatiques de sa pupille. En combinant les éléments fournis par tous les textes à sa disposition, elle a signé la variante littéraire qui est assurément la plus cohérente sur le plan psychologique et la plus fouillée d’un point de vue historique.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".