Effects of arm embodiment on implicit coordination, co-presence, and awareness in mixed-focus distributed tabletop tasks
Bibliographic record
Abstract
Mixed-focus collaboration occurs when people work on individual tasks in a shared space -- and although their tasks may not be directly linked, they still need to maintain awareness and manage access to shared resources. This kind of collaboration is common on tables, where people often use the same space to carry out work that is only loosely coupled. At physical tables, people easily manage to coordinate access to the table surface and the artifacts on it, because people have years of experience interacting around other physical bodies. At distributed digital tabletops, however, where there is no physical body for the remote person, many of the natural cues used to manage mixed-focus collaboration are missing. To compensate, distributed groupware often uses digital embodiments. On digital touch tables, however, we know little about how these embodiments affect coordination and awareness. We carried out an empirical study of how four factors in an arm embodiment (transparency, input technique, visual fidelity, and tactile feedback) affected implicit coordination, awareness, and co-presence. We found that although some embodiments affected subjective feelings of co-presence or awareness, there were no changes in table behavior -- people acted as if the other person did not exist. These findings show the possibilities and limitations of digital arm embodiments, and suggest that the natural advantages of tables for collaboration may not extend to distributed tables.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".