Valuing Virtual Worlds: The Role of Categorization in Technology Assessment
Bibliographic record
Abstract
Virtual worlds offer great potential for supporting the collaborative work of geographically distributed teams. However, reports indicate the existence of substantial barriers to the acceptance and use of virtual worlds in business settings. In this paper, we explore how individuals’ interpretations of virtual worlds influence their judgments of the value of the technology. We conducted a qualitative analysis set in the context of a large computer and software company that was in the process of adopting virtual worlds for distributed collaboration. We identified interpretations of virtual worlds that suggest three mental categories: virtual worlds as a medium, virtual worlds as a place, and virtual worlds as an extension of reality. We associated these mental categories with different criteria for assessing the value of virtual worlds in a business setting. This study contributes particularly to the acceptance of virtual worlds but also more generally to the understanding of technology acceptance by demonstrating that the relative importance of the criteria for assessing a technology varies with potential users’ interpretations and mental categorizations.
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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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".