Animating virtual worlds: Emergence and ecological animation of Ryzom’s living world of Atys
Bibliographic record
Abstract
Ryzom is a long-running (from 2004–present) science fantasy MMORPG (henceforth MMO) set in the science fantasy game world of the planet Atys, an entirely organic “rootball” teeming with alien life forms. The most oft-cited distinctive properties of Ryzom in the MMO world is the way creates not only an immersive sense of “worldness”, but a living, breathing, organic world. The game world is not only a richly animated “world” like all MMOs, but the aggregate of these animations also produce a sense of life, a “living world”. Following Silvio (2010) in particular, I ask how and when the properties of animation — understood in the narrow sense as a medium or media form — can produce a broader sense of “animacy” (Chen, 2012), a lively affect of “animatedness”: how and when animation (movement) is read as life; how an animated world becomes a living world. Specifically, why is it that in the animated world of Ryzom, as in animated cartoons, the animation of animality is central to this transition from animation to life: why the reading of animated “movement-as-life tends to settle on cartoon animals”. The “immersive” feeling of Atys as a ‘living world’ is displayed in the “emergent” animation of animals, particularly the ways that animals interact via “ecological” algorithms of predation and mutual care. animations which players explore as part of the emergent living worldness of Atys.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".