Ethnography of Online Role-Playing Games: The Role of Virtual and Real Contest in the Construction of the Field
Bibliographic record
Abstract
This paper invites the reader into the world of MUDs (Multi User Domains). Its underlying goal is to analyse certain social challenges associated with computer mediated communication (CMC), specifically with respect to the concept of the game; the process involved in the construction of the online Self or personality, potentially perceived as the final culmination of the frequent "comings and goings" between the game and reality; the concept of community that develops between two different frames—the virtual world and the real one; and, finally, the concept of both online and offline "experience". The empirical research, focusing on a comparison between an Italian and a Canadian MUDs interactive game, used online ethnography as the basic premise of study and biographical interviews with the players themselves, as further validation of the phenomenon. A fundamental question faces a researcher when conducting the study of a MUD—is the online game the only realm to consider? What is the impact of a multitude of other media (Instant messaging, boards, e-mails, SMS etc.) used by mudders to communicate in order to organize the game and become familiar with each other? Is it necessary for a researcher to totally abandon the players' social premise even if s/he is focusing her/his research on online relationships? These are some of the questions this paper endeavours to answer, while also being cognizant of the methodological problems researchers encounter when studying the Internet, both as a medium (of communication) and as a research framework. URN: urn:nbn:de:0114-fqs0703367
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".