Assessment of collaborative decision-making in design development and coordination meetings
Bibliographic record
Abstract
Building design is a complex multi-disciplinary process that requires extensive collaboration to develop a coordinated design that satisfies the functional, aesthetic, and economic requirements of the owner. Today, design meetings typically take place in physical workspaces, such as conference rooms, where all the relevant members of the team work together in the same place and time. Although most project information is generated electronically, teams primarily communicate and share information using paper-based representations. Emerging technologies such as touch-sensitive large-screen displays, table-top displays, laptops, and tablet PCs offer great promise in enriching today’s paper-based workspaces to create what are known as interactive workspaces. 3D design tools are also gaining acceptance and providing significant benefits to the design coordination process. However, it remains unclear as to how such tools and technologies can be incorporated effectively in workspaces to support design coordination meetings. This research addresses this need by characterizing how people spend time performing collaborative decision-making tasks in design development meetings. I conducted a five-month field study of the design development process for the Centre for Interactive Research on Sustainability (CIRS) project being constructed near downtown Vancouver, British Columbia. I observed weekly design development meetings and performed a thorough post-meeting video analysis. During the meetings, I took detailed notes documenting the meeting activities, the kind of information sources and representations used, and the interactions meeting participants had with information. The video was analyzed to identify how time is spent on different decision-making tasks and to determine the effectiveness of those tasks. This research found that the majority of time in design development meetings is spent on descriptive (35%) and explanative (42%)a sks and very little time is spent on evaluative (12%)n d predictive (11%) tasks. However, resolution rates- percentage of tasks resolved- were still quite high in these meetings. with an average of 73%. Based on the analysis, paper-based meetings were compared and an initial set of requirements for accomplishing design development tasks in a computer-supported interactive workspace were identified. In summary, successful interactive workspaces should support physical as well as digital artefacts, digital records of meetings, and digital overlays of schematic diagrams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".