Bibliographic record
Abstract
Since it’s introduction in November 2004, World of Warcraft (WoW) has exploded in popularity within the sphere of Massively-Multiplayer Online Role-Playing Games (MMOs), dominating the field with over 11.5 million monthly subscribers, an order of magnitude larger than its nearest competitor (Woodcock, 2008). It has become a pop-culture phenomenon, parodied in South Park, promoted by William Shatner, and fiercely defended by its proponents. However, much of the current analysis of the game itself has been on the activities and functions that occur within its virtual space (Ducheneaut, et. al., 2006). The exogenous processes by which WoW came to dominate in its sphere have been under-explored, and the effect their marketplace entry had on established groups within that sphere has been neglected. In this paper, I propose that similarities to what WoW has accomplished in the MMO market can be found in the rise of America Online (AOL) in the early 1990’s, and its effect on the existing service providers and systems of the nascent internet. Exemplifying this is the opening of UseNet to its users in 1993, the infamous “September That Never Ended.”
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".