Le deuil dans le roman et dans l'autobiographie : du ressassement à la réparation
Bibliographic record
Abstract
Nous nous proposons de montrer à travers des exemples d’oeuvres françaises contemporaines que le choix du genre — autobiographie ou roman — a une incidence sur le traitement du deuil. En effet, le roman présente un « deuil sans fin » où la perte est ressassée et ne semble pas pouvoir être surmontée. En revanche, l’autobiographie prend le parti de la vie en s’attachant au portrait du défunt afin de le ressusciter, de sauver la partie du passé qui pourrait être engloutie avec sa mort et, ainsi, de faire le deuil. Cette différence nous semble reposer sur les spécificités de chacun de ces deux genres littéraires, le roman se révélant finalement et paradoxalement plus narcissique que l’autobiographie.AbstractBy analyzing contemporary French works, we will show how the choice of genre affects the way bereavement is treated. Indeed, novels present an "ever-lasting mourning" when loss is dwelled on and seems not to be surmountable. However, autobiographies side with life by portraying the deceased in order to raise them from the dead, to save the moments of the past which could be swallowed up with their death, and thus to mourn for them. According to us, this difference seems to be based on the characteristics of each of these two literary genres; in fact and paradoxically, novels seem to be more narcissistic than autobiographies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".