Méchanceté autofictionnelle dans <i>Folle</i> de Nelly Arcan
Bibliographic record
Abstract
Dans Folle (2004), Nelly Arcan prête à un personnage les traits d’un ancien amoureux : l’auteure rend ainsi publics les vices et les défauts de son ancien amant. La méchanceté d’Arcan est intrinsèquement liée au désir de vengeance du dominé humilié. Par contre, sa hargne ne s’arrête pas là : l’ironie d’Arcan écorche aussi son alter ego fictif. Elle se prend comme cible pour montrer les inégalités au sein du couple hétérosexuel et utilise l’auto-ironie afin de renverser les codes de domination entre les hommes et les femmes, les Français et les Québécois.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".