A Second Life for Tourism and Economic Development?: A Look at Early Experiments in Using Virtual Worlds to Promote Real World Sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
EXECUTIVE SUMMARY Virtual worlds are exploding in popularity today, and Second Life - an unstructured virtual environment - has become a venue of great interest not just for individual users and entrepreneurs, but for corporations, educational institutions, and governmental agencies. One of most exciting possibilities for such virtual worlds is to use this new platform to promote one's local/national area as a site for tourism and/or economic development. In this article, we explore efforts made to date in this area by government agencies, both domestically and internationally, and provide an analysis of how to evaluate effectiveness of such in-world ventures. Keywords: Web 2.0, Virtual Worlds, Second Life, Tourism, Economic Development, ROI 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 & 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) - umbrella term that will be used in this report - can be defined as being: graphical two-dimensional (2-D) or threedimensional (3D) videogames played online, allowing individuals, through their self-created digital characters or 'avatars,' to interact not only with gaming software but with other players (Steinkuehler and Williams, 2006, n.p.). Writing in Harvard Business Review, Reeves, Malone, and O'Driscoll (2008) differentiated Second Life 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 & 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 man having a ready-made character witii a fixed purpose, one creates his or her own avatar witii 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 Life 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 Life has grown rapidly - to over 13 million in early 2008 (Linden Lab, 2008). Second Life 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 Life 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 - 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). …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it