Knowledge Sharing Strategies for Smart Architectural Development: Facilitating Interdisciplinary Collaborative Design
Bibliographic record
Abstract
The emergence of smart building technologies designed to detect and respond to changes in the built environment have inspired architects to consider the relationship between occupants and responsive environments (Di Christina, 2001;Walter, 2015).Presently, architectural project teams have grown to incorporate specialized technologists to help integrate sensing, actuation, and data collection into the architectural program, yet differences in disciplinary understanding of technologies and design processes (Bektas, 2013) often lead to the failure of smart architectural features (Meagher, 2014) and prevent smart buildings from achieving the full scope of established project objectives.This research presents an inquiry into how collaboration and knowledge sharing might be best facilitated among interdisciplinary smart architecture project team members through a series of design charrettes and card sorting exercises hosted using online collaboration board tools.Results demonstrate that while online collaboration platforms allowed for a rapid exchange, evolution, and refinement of design ideas -that knowledge sharing and acquisition were mitigated by the knowledge sharing habits of charrette team members and a lack of familiarity with collaboration board tools.Recommendations are made surrounding the organizational approach and implementation of knowledge sharing strategies to overcome interdisciplinary knowledge gaps, improve collaborative efficiency, and generate informed responses to smart architectural design problems.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".