Some Opportunities and Limitations of Cloud Computing Environments in Early Stage of Design Process
Bibliographic record
Abstract
Using a user experience approach, we identified certain opportunities provided and limitations faced by cloud computing applications in remote meetings of design teams. Fifteen designers participated in six teams of three or four members during a one and a half hour meeting. They worked on a task involving the conceptual design of a Web page or a Corporate identity for a research laboratory. The meeting took place in a distributed manner, using two cloud applications. We identified some factors corresponding to favorable and unfavorable aspects perceived by designers when working in a cloud computing environment. After six interviews, data were open-coded to synthesize emergent themes, and then categorized by frequencies into five fields: opportunities, organization of work, limitations, needs for other materials and design process. The results indicate a positive experience regarding the interaction and the coauthoring production, which constitute an opportunity presented by cloud computing technologies. Web task designers prefer working under non-noise disturbance conditions and using a clear whiteboard, while with graphic tasks, designers generally prefer more sophisticated drawing tools and easy graphic modifications when creating sketching concepts. The participants in this study were globally satisfied with the social exchange climate, i.e., the graphical remote sharing that allows an effective exchange of draft details among multiple users on-line. The cloud environment already presents technical problems, such as lags or leaps during information exchange; limitations which have been seen to disrupt communication flow in the early conceptualization stage.
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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 | 0.045 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".