Bibliographic record
Abstract
While Virtual Reality (VR) technology has rapidly matured into the consumer space, there exists a noticeable lag in user perceptual studies to empirically validate whether these new technologies genuinely enhance user experience or merely serve as marketing hype. This paper introduces StudyVR, a prototype system aimed at addressing this gap by streamlining the design and deployment of mixed-methods user experience research studies in VR. Our approach automates common administrative tasks, common study methodologies, and automatically synchronizes both the research design and the participant questionnaire in an online database. The goal is to enable researchers to rapidly move from the initial concept of research design to participant data collection and analysis, concentrating more on their research questions and hypotheses than on implementation details. StudyVR enables a rich range of research methodologies managed in a centralized database, simplifying data management, and facilitating interdisciplinary collaboration. Finally, we present a proof-of-concept study design using StudyVR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.150 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".