Analyzing relationships between project team compositions and green building certification in green building projects
Bibliographic record
Abstract
Purpose Literature on organizational analysis identified that project participants have a certain impact on the project outcome. However, there is no study that identifies the impact of project teams and individual project participants on a green building project. The purpose of this paper is to analyze the impact of green building project teams on green building certification. Design/methodology/approach Project information, project team information, and green building certification grade were collected using the Canadian green building database. Project team data were analyzed and organizations were ranked based on their green building project experience and collaborations with experienced green building organizations. The page rank algorithm is used to calculate the rank of organizations in order to identify the impact of organizational rank on the final green building certification grade of a project. Findings The results show a positive relationship between the green building certification grade and the number of organizations with more green building experience in a project team. The results also show that not having experienced key organizations such as owners, designers, and contractors will likely lead to a lower green building certification grade. Originality/value Impact of project teams on green building projects has not been studied before. This study used an innovative method to analyze green building project teams and to investigate the importance of green building project experience. The findings of this study provided evidence to support the influence of project team compositions in green building projects. The results presented in this paper can help project owners and managers during project team formation for successful execution of green building projects.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".