Bibliographic record
Abstract
Le jeu Town of Light propose, par sa narration et son gameplay, un moyen de médiatiser un patrimoine "marginalisé, déprécié" (Le Gallou, 2021) qu'est un hôpital psychiatrique abandonné des années 1980. L'archive et l'artéfact sont collectés comme lors d'un chasse aux trésors et contribuent à la narration. Toute l'histoire est une redécouverte du passé de notre personnage, Renée T.. Cette redécouverte se fait par des souvenirs déclenchés par la trouvaille d'archives et d'artéfacts au sein de l'hôpital. Cependant, plus qu'une chasse aux trésors, je propose l'hypothèse d'une imitation de la pratique de l'urbex (exploration urbaine) par le jeu vidéo Town of Light. En poursuivant l'analogie entre pratique de l'urbex et expérience du jeu Town of Light et en me basant sur la thèse de Aude Le Gallou (2021), le jeu semble proposer une reméditation d'un espace et d'une mémoire non patrimonialisés institutionnellement. Town of Light participerait donc à une patrimonialisation d'un espace et d'une mémoire: celles de femmes psychiatrisées au XXème siècle.
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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 | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".