Entre thanatographie et pathographie, la mort médicalisée d'Hervé Guibert
Bibliographic record
Abstract
Dans À l’ami qui ne m’a pas sauvé la vie, la mort est omniprésente : celle des autres — notamment celle de Muzil/Foucault — comme celle du narrateur/auteur, conséquence du sida ou menace exercée par cette maladie. Cet article étudie, dans une approche épistémocritique, comment le savoir médical que l’œuvre mobilise contribue à l’écriture de la mort. Il analyse les différentes fonctions — éthique, réaliste, didactique, mystificatrice, esthétique, sémantique, heuristique, dénonciatrice — remplies par les références médicales. Il montre enfin dans quelle mesure l’écriture de la mort aboutit à s’en libérer.AbstractIn À l’ami qui ne m’a pas sauvé la vie, death is omnipresent: other people’s — Muzil/Foucault’s in particular — and the narrator/author’s death as a result or threat of AIDS. Through an epistemocritical approach, this article studies how the medical knowledge mobilized by the novel contributes to the depiction of death. It analyses the several functions — ethical, realist, didactic, mystifying, aesthetic, semantic, heuristic, denunciatory — performed by the medical references. Finally, it shows to what extent writing about death liberates the author.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".