Augmented Focus Groups: On Leveraging the Peculiarities of Online Virtual Worlds when Conducting In-World Focus Groups
Bibliographic record
Abstract
Increasingly, academic researchers and practitioners have been using online 3D virtual worlds such as Second Life (SL) to conduct focus groups. When doing so, researchers and practitioners have copied and pasted as is, in this new environment, the qualitative methodologies commonly used in real-world focus groups. However, the relevance of using standard focus group methodologies within an online virtual environment has been neither tested, nor the focus of previous research. In addition, online virtual worlds may offer new methodological opportunities that, so far, have been left unexplored. To fill in this methodological gap, the authors have moderated various focus groups in Second Life. When doing so, they tested the limitations inherent to using real-world protocols in an online virtual environment. During the course of this project, it became clear that the usual focus group protocols should be adapted to the peculiar context, if one wants to fully leverage this new medium. As a result, new online qualitative methodologies (e.g., 3D collages) were developed and tested during this research project.
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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 | 0.162 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".