Science-fiction et « innovations imaginaires » : essai typologique sur les technologies de mémoire totale
Bibliographic record
Abstract
Le texte présente une typologie de technologies de « mémoire totale » imaginées dans un corpus de sciencefiction audiovisuelle entre 1990 et 2022.Filant la métaphore de la mémoire comme base de données et du plan, les technologies des diégèses permettent d'encoder et de conserver la mémoire humaine dans des formats numériques, d'accéder aux informations enregistrées et également d'altérer la mémoire et l'identité des individus.Cet essai explore les types de machines de mémoire totale qui spéculent sur les innovations technologiques ayant trait aux possibilités d'intervenir sur la faculté mnésique de l'humain.ABSTRACT.This paper presents a classification of the various types of "total memory" technologies which were imagined in a corpus of audiovisual science fiction between 1990 and 2022.These technologies, which often use the metaphor of memory as a database or plan, are depicted as allowing for the digitization and storage of human memories, as well as the retrieval and even modification of these memories and the identities of individuals.Through this analysis, this paper aims to explore such technological innovations for the enhancement or alteration of human mnemonic abilities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".