Architecting tools to support transitions in workstyle
Bibliographic record
Abstract
Challenges in designing effective groupware include technical issues associated with concurrent and distributed work, as well as social issues associated with supporting group activities. These challenges are further complicated by limited experience with successful designs and insufficient understanding of group interaction dynamics. Although groupware applications must be well designed for individual users, they must also appropriately support the activities of interactive groups. Specifically, members of collaborative groups interact with each other in a variety of ways, so groupware tools must be designed to support a variety of collaborative working styles. Furthermore, members of collaborative groups move frequently between different styles of interaction throughout the course of their work, so groupware tools must be designed to support fluid transitions between different interaction styles. In order to focus our research, we have concentrated on the limited domain of collaborative software design tools. We have applied ethnographic study and activity analysis to demonstrate the importance of supporting different styles of interaction and transitions between them in collaborative software design, and to identify common interaction styles and transitions in this domain. We have developed design and analysis techniques that link requirements to architectural decisions in order enable systematic design of architecture to support different interaction styles and transitions between them. We have applied these techniques to the design of a prototype tools supporting collaborative software design. Although our investigation has been limited to the domain of collaborative software design, we believe these techniques to be widely applicable.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".