Bibliographic record
Abstract
Single Display Groupware (SDG) lets multiple co-located people, each with their own input device, interact simultaneously over a single communal display. While SDG is beneficial, there is risk of interference: when two people are interacting in close proximity, one person can raise an interface component (such as a menu, dialog box, or movable palette) over another person's working area, thus obscuring and hindering the other's actions. Consequently, researchers have developed special purpose interaction components to mitigate interference techniques. Yet is interference common in practice? If not, then SDG versions of conventional interface components could prove more suitable. We hypothesize that collaborators spatially separate their activities to the extent that they partition their workspace into distinct areas when working on particular tasks, thus reducing the potential for interference. We tested this hypothesis by observing co-located people performing a set of collaborative drawing exercises in an SDG workspace, where we paid particular attention to the locations of their simultaneous interactions. We saw that spatial separation and partitioning occurred consistently and naturally across all participants, rarely requiring any verbal negotiation. Particular divisions of the space varied, influenced by seating position and task semantics. These results suggest that people naturally avoid interfering with one another by spatially separating their actions. This has design implications for SDG interaction techniques, especially in how conventional widgets can be adapted to an SDG setting.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".