Exploring User Placement for VR Remote Collaboration in a Constrained Passenger Space
Bibliographic record
Abstract
Extended Reality (XR) offers the potential to transform the passenger experience by allowing users to inhabit varied virtual spaces for entertainment, work or social interaction, whilst escaping the constrained transit environment. XR allows remote collaborators to feel like they are together and enables them to perform complex 3D tasks. However, the social and physical constraints of the passenger space pose unique challenges to productive and socially acceptable collaboration. Using a collaborative VR puzzle task, we examined the effects of five different f-formations of collaborator placement and orientation in an interactive workspace on social presence, task workload, and implications for social acceptability. Our quantitative and qualitative results showed that face-to-face formations were preferred for tasks with a high need for verbal communication but may lead to social collisions, such as inadvertently staring at a neighbouring passenger, or physical intrusions, such as gesturing in another passenger’s personal space. More restrictive f-formations, however, were preferred for passenger use as they caused fewer intrusions on other passengers’ visual and physical space.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".