Filiations abortives : l’avortement dans la littérature française de l’extrême contemporain
Bibliographic record
Abstract
If abortion remains a taboo experience anchored—in social, political and media discourse—in the notion of trauma, French literature of the last decades offers increasingly diversified narratives on the question. Wishing to move away from the binaries that have always animated debates on abortion, several authors reveal a much more varied spectrum of reactions towards this experience, which can be contradictory or ambiguous, combining relief with loss. This article focuses on this more complex vision of abortion through the analysis of three literary works of the last decade: Dix-sept ans by Colombe Schneck (2015); Ligne de partage des eaux by Fabienne Swiatly (2011); and Il fallait que je vous le dise by Aude Mermilliod (2019). After reviewing the discourses circulating around abortion, the article demonstrates how, in these works written in the first person, abortion registers as completion incompletion, leaving its marks in the women who might feel a connection to their foetus. Finally, these texts make it possible to read, see, and imagine a mosaic of abortion experiences. This gives rise to filiations made through the experience of abortion, bringing together women who underwent it and put it into words, and encouraging others to share their stories.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".