The influence of green building certifications in collaboration and innovation processes
Bibliographic record
Abstract
While the paradigm of sustainable development has largely influenced architecture projects worldwide, Green Building Certifications (GBCs) have become the new (increasingly mandatory) standard of project performance. Numerous studies have concentrated on the influence of sustainable development (SD) in the final product: the building. However, more research is still needed in order to understand how GBCs have influenced building processes, particularly collaboration and innovation within architecture projects. In order to fill this gap, this study presents results from 19 interviews with professionals in the built environment and examines three architecture projects conducted in Canada that received a widely popular GBC and were significantly influenced by SD principles during the design and building process. The research applies recent frameworks for exploring stakeholders’ interests on GBCs and the collaboration and innovation practices developed by them. Research results show that processes within these projects are shaped by at least four tensions that can either enhance or hinder collaboration and innovation: strategic–tactical, collaborative–competitive, participative–effective and individual–collective. The study highlights the importance of understanding GBC as a process and not only as a final outcome, and thus, to better manage these tensions so that they contribute to product and process performance.
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".