MMOmuseums: A Proposal for the Creation of Experiential Memory Archives
Bibliographic record
Abstract
The visibility and longevity of popular and well-known massively multiplayer online (MMO) communities (World of Warcraft, Second Life) eclipse a greater number of virtual worlds that have been abandoned. While hundreds of inactive and closed-down massively multiplayer online role playing games (MMORPGs) have been documented, most online virtual worlds are not included in archival and preservation initiatives due to issues relating to intellectual property and proprietary technologies, and most MMORPG ghost towns are not even accessible online. Their evaporated geographies live on only in the memories and stories posted by players to archived message forums. What if these worlds could be booted up once again, not to play in, but to explore as virtual archaeology sites, sites redesigned to host stories and memories from the players that once inhabited and originally populated these architectures with action, conflict, cooperation, and event? Such virtual archive spaces would feature player experiences and emergent narratives, represented as embedded narratives in a simulated recreation of the computer-generated geographies that they took place in, so that visitors to such sites experience a sense of presence as they receive a combination of both experience and story that preserves these spaces as lived worlds. Using the now-defunct City of Heroes MMO as an example, this paper discusses ways of directly involving diasporic communities of players in the memorialization of virtual spaces that they once inhabited.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".