Un jeu qui s’appelle la science. Pourquoi et comment les jeux vidéo peuvent être des objets scientifiques
Bibliographic record
Abstract
Une grande partie du travail de definition et de comprehension des jeux consiste encore a chercher a quels objets ou quelles attitudes ils s’opposent. Si certains travaux recents ont pu traiter de celle qui est communement envisagee entre jeu et travail ou jeu et serieux (Buzy-Christmann et al. 2016; Trepanier-Jobin 2016), il est une autre opposition dont on parle moins : celle de la demarche scientifique et du jeu, alors meme qu’un champ universitaire qui cherche a l’etudier (et plus largement le jeu video) se developpe. Est-il possible de porter un regard scientifique sur le jeu, sur les jeux et l’attitude qui l’accompagne ? Si oui, en quoi consisterait-il ? Cet article se propose d’evoquer comment le jeu, a la fois compris comme structure et comme attitude, peut etre aborde au regard de ce qu’est la demarche scientifique notamment dans l’acception populaire et parfois deformee du terme.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".