Key Success Factors and Guidance for International Collaborative Design Projects
Bibliographic record
Abstract
In the built environment (BE) sector, the co-creation process of design demands understanding of requirements (as viewed by parties involved), mobilisation of tacit knowledge, negotiation, and complex exchange of information. The need to collaborate over distance has further exacerbated the complexity of the process, and, in itself, represents a significant challenge for BE professionals who are increasingly expected to undertake this process within globally distributed virtual teams. The research aims to identify key success factors and develop guidance for international collaborative design projects, via the implementation of collaborative design courses in UK and Canadian universities over three academic years. Questionnaire surveys, focus groups, observation of online meetings, personal reflections provided data for the analysis. The findings reveal the significance of the perceived risk of collaboration and a difference in preferred communication mode between architects and civil/structural engineers. These findings suggest the impact of training in the subject discipline, and that the opportunity for co-located working has helped the development of trust. The guidance is aimed at BE educators who wish to implement this activity in their courses.
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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 | 0.020 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".