Bibliographic record
Abstract
The world is entering a new normal of hybrid organisations, in which it will be common that some members are co-located and others are remote. Hybridity is rife with asymmetries that affect our sense of belonging in an organisational space. This paper reports a study of an XR Telepresence technology probe to explore how remote workers might present themselves and be perceived as an equal and unique embodied being in a workplace. VROOM (Virtual Robot Overlay for Online Meetings) augments a standard Mobile Robotic Telepresence experience by (1) adding a virtual avatar overlay of the remote person to the local space, viewable through a HoloLens worn by the local user, through which the remote user can gesture and express themselves, and (2) giving the remote user an immersive 360° view of the local space, captured by a 360° camera on the robot, which they can view through a VR headset. We ran a study to understand how pairs of participants (one local and one remote) collaborate using VROOM in a search and word-guessing game. Our findings illustrate that there is much potential for a system like VROOM to support dynamic collaborative activities in which embodiment, gesturing, mobility, spatial awareness, and non-verbal expressions are important. However, there are also challenges to be addressed, specifically around proprioception, the mixing of a physical robot body with a virtual human avatar, uncertainties of others' views and capabilities, fidelity of expressions, and the appearance of the avatar. We conclude with further design suggestions and recommendations for future work.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.173 | 0.046 |
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".