Bibliographic record
Abstract
INTRODUCTION In age of Web 2.0, Gibson (2007) observed that it is important to remember newness of Web and living online, stating: The Internet is a new human activity in, I imagine, way cities were once a new human activity. And we're still coming up with novel things to do in cities. So Internet has some ongoing novelty value (n.p.). Today, as never before, people from around world are becoming connected in whole new, novel ways, most notably in virtual reality of virtual worlds, which have been categorized as being the next great information frontiers (Bush and Kisiel, 2007, p. 1). They are known rather synonymously as: MMOGs (massively multiplayer online games); MMORPGs (massively multi-player online role playing games); MUVEs (multi-user online virtual environments); or NVEs (networked virtual environments). Massively Multiplayer Online Games (MMOGs)--the umbrella term that be used in this report--can be defined as being: graphical two-dimensional (2-D) or three-dimensional (3D) videogames played online, allowing individuals, through their self-created digital characters or 'avatars,' to interact not only with gaming software but with other (Steinkuehler and Williams, 2006, n.p.). Writing in Harvard Business Review, Reeves, Malone, and O'Driscoll (2008) differentiated Second from MMOGs in following manner: unlike online games, virtual social worlds lack structured, mission-oriented narratives; defined character roles; and explicit goals (p. 62). In virtual social world of Second Life, there are no quests, no scripted play and no top down game plan (Sharp and Salomon, 2008). There is no embedded objective or narrative to follow. There are no levels, no targets, and no dragons to slay. It has been hailed as nothing less than evolution of computer game, as rather than having a ready-made character with a fixed purpose, one creates his or her own avatar with an open-ended existence (Hutchinson, 2007, n.p.). Thus, rather than being a Star Wars-like character or an armed, rogue warrior whose mission it is to shoot as many other characters as possible or to collect enough points or tokens to advance to next level, Second avatar traverses a virtual world--often flying teleporting from virtual place to virtual place. Virtual worlds are fast becoming an environment of choice for millions of individuals--and a very big business. Since its launch in January 2004, number of residents in Second has grown rapidly--to over 13 million in early 2008 (Linden Lab, 2008). Second is, in truth, but one slice--albeit a tremendously important one--of overall virtual worlds' marketplace. In fact, both in terms of population and revenue, Second is dwarfed in size by what Sellers (2007) aptly termed men in tights games, medieval-styled fantasy games such as--World of Warcraft, Runescape, Lineage, Ragnarok, and Everquest. In fact, in January 2008, World of Warcraft--the largest MMOG--surpassed astonishing mark of having 10 million active subscribers--at least a quarter of which are based in U.S. and Canada (Smith, 2008) and almost half of whom are based in China (Au, 2008a). MMOGs are fastest growing category of online gaming, with total number of MMOG players has been estimated to be in excess of 150 million worldwide (Varkey, 2008). Indeed, Jeff Jonas, who is Chief Scientist for IBM Entity Analytic Solutions, recently observed that: As virtual worlds create more and more immersive experiences and as global accessibility to computers increases, I can envision a scenario in which hundreds of millions of people become engaged almost overnight (quoted in O'Harrow, 2008, n.p.). While Second is not largest or first virtual world, it has gained general acceptance as a platform that has drawn most attention (Rollyson, 2007). In late 2007, Gartner predicted that by end of 2011, fully 80 percent of all active Internet users will have a 'second life,' but not necessarily in Second Life in developing sphere of virtual worlds (n. …
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".