SketchBoard: Conceptualizing Interactive Communication Media for the Design Review Process
Bibliographic record
Abstract
As a synthesis, this doctoral thesis offers the opportunity to rethink the status of current technologies within the design review process. It suggests the potential for transforming the complex participatory, communicative, and technical nuances of the design review process to coexist with the affordances of the new genre of digital media. Furthermore, the goal of this research is to create an interactive communication medium, called SketchBoard, that embodies intelligent and intelligible behavior to remedy the vagueness of visualization. This thus renders an insight into improving participatory communication within the design review process using mobile interactive surfaces. Moreover, this research provides a means of characterizing the existing urban planning desktop computer applications and more recent interactive surfaces. The research findings also suggest the possibility of supplementing existing urban planning desktop computer applications with mobile interactive surfaces, while interacting in a participatory design review process. This thesis thus contributes to the intersection of the design review process with interactive surfaces while presenting a four-part empirical and design research study. The first study undertook an initial exploration of urban planners’ current work practices with their typical tools and technologies. The second, third, and fourth studies were conducted with urban planners at the City of Calgary’s (Canada) urban planning department. The second study explored the design review process, and as such comprises the zoning ordinance process that dictates the ultimate form of a building through which technological systems are constituted. The third study further explored these aspects by investigating the design specifications of SketchBoard. In the fourth study, the design and evaluation of SketchBoard for the design review process is presented. On the methodological level, a postmodernist, qualitative perspective combined with the methodology of ethnography and contextual design as empirical approaches and minimalism as a design technique were exploited to address the multidisciplinary notion of the research. The research methods used were semi-structured interviews, participant and non-participant observations, structured surveys, and experimental digital paper prototypes. Finally, overall, this four-part research study foresees urban planners’ experience and expectations with interactive surfaces that will provide an impression for the directions of future research.
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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 | 0.001 | 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 | 0.001 | 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".