Confession et éloquence romantiques chez Alfred de Musset
Bibliographic record
Abstract
This thesis presents a rhetorical analysis of Alfred de Musset's La Confession d'un enfant du siecle. The methodology used for the purpose of the analysis gives a certain originality to this dissertation. It is quite unusual among the literary scholars to associate rhetoric to a text that is typically romantic by its form and content, rhetoric being traditionally used for discourse analysis only, not for a novel. What our research has shown us is that nobody has ever approached, with a rhetorical eye, that novel which, moreover, has almost never been studied nor commented. The main idea for this paper was that the literary genre of the confession, because of its religious and judicial origins, necessarily implies a desire of persuasion. To obtain total redemption of his crimes or sins, one must convince the other that he regrets what he has done, that he acted against his own will, that he suffers from what he did, that he acted without knowing his actions or words would hurt someone; one can even say that he is the victim of false accusations, etc. Rhetoric is then called to play an important part in this justification and persuasion enterprise. That is why we came with this hypothesis that there was in Musset's novel a persuasion mechanism working to gain the support of the reader toward the narrator's thesis. This dissertation tries to emphasize the persuasion mechanism by doing a rhetorical analysis of the text.
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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 | 0.002 | 0.005 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".