Bibliographic record
Abstract
À travers l’étude de deux autopathographies, Petite de Geneviève Brisac et Hors de moi de Claire Marin, cet article propose de réfléchir à une écriture féministe de la maladie. Dans un premier temps, nous examinons les façons dont Brisac et Marin dévoilent et dénoncent une médecine hostile aux femmes. Nous réfléchissons ensuite aux contraintes éthiques que les auteures doivent affronter lorsqu’elles relatent leur souffrance. Ancrée dans le féminisme « rabat-joie » (concept articulé par Sara Ahmed et revisité par Erin Wunker), cette réflexion soutient la littérature fournit un lieu essentiel où les écrivaines malades ou guéries peuvent refuser un silence qui leur est imposé à la fois par l’établissement médical et par la société en général. En refusant une pression vers le bonheur et vers le silence, des auteures telles que Brisac et Marin peuvent entreprendre le devoir féministe de dévoiler et dénoncer les oppressions qui les font souffrir.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".