Chronique du cinéma 2 : De son vivant – apprivoiser le mourir
Bibliographic record
Abstract
Apprivoiser et penser le mourir par le biais du cinéma, c’est peut-être une des possibilités offertes par ce long métrage d’Emmanuelle Bercot mettant en vedette Benoît Magimel et Catherine Deneuve. Le film De son vivant aborde de manière frontale la question de la fatalité, au travers du récit de vie, et de mort, d’un jeune quadragénaire atteint d’une maladie dont le sombre pronostic ne fait aucun doute. Nous y suivons Benjamin, dans la dernière année de sa vie, alors que ce dernier affronte, l’inéluctable de sa finitude annoncée dans ce délai posé clairement : de six mois à un an. Se faisant, il se trouve plongé, ainsi que ses proches, de manière radicale, au coeur de ses questions existentielles, placé devant une série de choix qui se posent à lui, malgré – ou grâce à – la réalité de sa mort. Les nombreux enjeux éthiques rencontrés tout au long de son parcours sont exposés pour toutes les personnes impliquées : soignants, patient et proches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".