Chronique du cinéma 3 : Tu te souviendras de moi – quand le récit de soi s’étiole
Bibliographic record
Abstract
Édouard perd la mémoire. Le fil du récit de sa vie se dissout, le laissant confus, livré à une forme de solitude qui, graduellement, le coupe d’un monde dans lequel, on le comprend rapidement, il avait plutôt l’habitude d’occuper l’avant-scène. Intellectuel invité régulièrement à prendre parole dans l’espace public, Édouard, l’historien émérite, est ici confronté à une maladie qui, ironiquement, atteint la mémoire. Lui qui, toute sa vie, a réfléchi sa société en la mettant en lien avec son passé, perd peu à peu la capacité à placer sa propre vie sur le fil d’un récit continu. Comment ses proches composent-ils avec la disparition d’une part identitaire d’Edouard? La maladie n’est-elle que l’inverse de la santé, pour lui, ou aussi, un terreau fertile à une transformation qui le rendrait, paradoxalement, plus vivant, au sens ou le philosophe l’entend, dans son expression “vivant jusqu’à a sa mort”? Que reste-t-il de nous lors que notre récit qu’on se fait de nous-mêmes nous échappe? Analyse du film de 2022 de François Archambault : Tu te souviendras de moi.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".