Bref essai d’interprétation de trois présences de la mort dans le jeu vidéo
Bibliographic record
Abstract
Cette étude analyse trois représentations de la mort dans les jeux de combat, de tir, de science-fiction, de courses automobiles, d’horreur ou de guerre. La première, qui retient le plus l’attention parce qu’elle est l’événement majeur de l’intrigue, est celle de la mort stricto sensu (partie 1). Avec sa cruauté, sa fréquence, l’énergie qu’elle mobilise chez le joueur, le bénéfice qu’elle lui apporte et les risques qu’elle lui fait courir, cette première mort est la « mort-reine » des jeux. La partie 2 traite du redoutable personnage de la mort, tantôt asexué (squelette armé d’une faux), tantôt sexué (femme vêtue de noir, guerrier). La partie 3 aborde la représentation des morts : simples revenants, morts-vivants, zombies ou autres créatures monstrueuses. L’étude montre que les jeux vidéo constituent un bon exemple du recyclage contemporain de certaines traditions mythologiques et eschatologiques de la civilisation occidentale.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 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".