Embodiments for Mixed Presence Groupware
Bibliographic record
Abstract
Large surfaces such as tabletop and whiteboard displays naturally afford collocated collaboration, where multiple people work together over the shared workspace. As large digital displays become more ubiquitous, it becomes increasingly important to examine their role in supporting groups of distributed collaborators working over the digital work surface. In particular, Mixed Presence Groupware (MPG) is software that connects both collocated and distributed collaborators and their disparate displays via a common shared virtual workspace. We have built several MPG systems by connecting several distributed displays, each with multiple input devices, thereby connecting both collocated and distributed collaborators. By observing how these systems are used, we found that MPG presents a unique problem called presenc1e disparity: collaborators focus their energies on collocated collaborators at the expense of their distributed counterparts. Presence disparity arises because the physical presence of collaborators varies across the MPG workgroup: physically collocated collaborators are seen in full fidelity, while remote participants are represented by only virtual embodiments. Consequently, we propose four design principles for MPG systems that we believe will help mitigate the problem of presence disparity in MPG. We then introduce how these principles are realized in VideoArms, an embodiment technique that digitally captures people s arms as they work over large work surfaces, and redisplays them as digital overlays on remote displays. Our evaluation of VideoArms validates its use in principle as an effective embodiment technique for MPG systems.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".