Physicality As an Anchor for Coordination: Examining Collocated Collaboration in Physical and Mobile Augmented Reality Settings
Bibliographic record
Abstract
Design and co-creation activities around 3D artifacts often require close collocated coordination between multiple users. Augmented reality (AR) technology can support collocated work enabling users to flexibly work with digital objects while still being able to use the physical space for coordination. With most of current research focusing on remote AR collaboration, less is known about collocated collaboration in AR, particularly in relation to interpersonal dynamics between the collocated collaborators. Our study aims at understanding how shared augmented reality facilitated by mobile devices (mobile augmented reality or MAR) affects collocated users' coordination. We compare the coordination behaviors that emerged in a MAR setting with those in a comparable fully physical setting by simulating the same task -of the shared physical dimension for participants' ability to coordinate in the context of collaborative co-creation. Namely, participants working in a fully physical setting were better able to leverage the work artifact itself for their coordination needs, working in a mode that we term artifact-oriented coordination. Conversely, participants collaborating around an AR artifact leveraged the shared physical workspace for their coordination needs, working in what we refer to as space-oriented coordination. We discuss implications for a AR-based collaboration and propose directions for designers of AR tools.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".