L’etude des jeux video en ligne : une analyse des processus communicationnels dans une perspective d’innovation sociale et technologique
Bibliographic record
Abstract
Resume : Notre etude concerne specifiquement les jeux en ligne massivement multijoueurs ( Massively-Multiplayer Online Games , connus sous l’acronyme MMOG). Notre hypothese est que ce type de jeu video est un media de socialisation, c’est-a-dire un dispositif de mediation permettant de partager de l’information a grande echelle, grâce a ses univers de rencontres et a son reseau d’echanges. Plus specifiquement, l’article traite d’un aspect particulier des MMOG, soit l’appropriation par les joueurs des moyens de communication et le developpement des additiels (ou add-ons ). Mots-cles : additiels, jeux video en ligne, innovation sociale et technologique, media de socialisation Abstract: Our study relates specifically massively multiplayer online games (known by the acronym MMOG). Our hypothesis is that this type of game is a medium of socialization, a mediation mechanism for sharing information on a large scale, because its universe of encounters and its network of exchange. More specifically, the present article raises a particular aspect of MMOGs, the appropriation by the players of the communication media and the development of addons. Keywords: add-ons, mediation, online video games, social and technological innovation, socialization
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".