To Kill the False Woman: Annie Ernaux Autobiographical Writing in Happening
Bibliographic record
Abstract
Annie Ernaux’s L’événement (Happening) stands as a powerful piece of autobiographical discourse and incites discussion in both trauma literature and as an extension of Hélène Cixous’s The laugh of the Medusa. Ernaux’s text revolves around her clandestine abortion, as she writes of her trauma through the event. I will attempt to demonstrate that while the mere act of writing constitutes a form of overcoming traumatic events, Ernaux’s writing goes above and beyond these conventions. To do so, I will analyze how Ernaux’s autobiographical writing combines several discursive, narrative, and literary techniques to firstly meditate and reflect on the past and present, and secondly to reconcile and overcome the past and presents perspectives, or the “unspeakable” incited by rigid social and legal norms. I will further demonstrate how by writing her own body, that is by meditating on the numerous limitations placed on the female body and mind, her literary contribution is two-fold: Ernaux both pays homage to her own personal female experience and represents the collective female experience in a larger, historical context. The result is a re-framing of a feminine narrative into a human question through a validation of Ernaux’s experience.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| 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 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".