Potentials of Information Technology in Building Virtual Communities
Bibliographic record
Abstract
In recent years, virtual communities have become the topic of countless books, journal articles and television shows, but what are they, and where did they come from? According to Preece, Maloney-Krichmar, and Abras (2003), the roots of virtual communities date back to as early as 1971 when e-mail first made its appearance on the Advanced Research Projects Agency Network (ARPANET), which was created by the United State’s Department of Defense. This network would lead to the development of dial-up bulletin board systems (BBSs) which would allow people to use their modems to connect to remote computers and participate in the exchange of e-mail and the first discussion boards. From these beginnings a host of multi user domains (MUDs) and multi-user object oriented domains (MOOs) would spring up all over the wired world. These multi-user environments would allow people to explore an imaginary space and would allow them to interact both with the electronic environment and other users. Additionally, listservs (or mailing lists) sprang up in 1986, and now, almost two decades later, they are still in use as the major method of communication among groups of people sharing common personal or professional interests (L-Soft, 2003). Since then the Internet has exploded due to the development of Web browsers as well as the development of communications technologies such as broadband, digital subscriber line (DSL), and satellite communications. Groups of people from as few as two and reaching to many thousands now communicate via email, chat, and online communities such as the Whole Earth ‘Lectronic Link (WELL) and such services as MSN, Friendster, America Online (AoL), Geocities, and Yahoo! Groups. Other examples of online communities are collaborative encyclopedias like Wikipedia. Web logs (Blogs) like Slashdot.com and LiveJournal allow users to create their own content and also to comment on the content of others. They also allow the users to create identities and to make virtual “friends” with other users. The definition of virtual community itself becomes as convoluted as the multitude of technologies that drives it. Are e-mail lists, message boards, and chat rooms online communities or are they virtual communities? Virtual communities might be persistent worlds as those found in popular online games (Everquest, 2004, Ultima Online, 2004) or virtual worlds (such as MUDs and MOOs) where the user is able to explore a simulated world or to take on a digital “physicality” in the form of an avatar. It becomes clear from the literature that the terms are still used interchangeably.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".