Communication networks of an integrated project delivery team for construction: relationships between formal and informal communication networks
Bibliographic record
Abstract
Abstract The current study modeled formal and informal communication networks of an integrated project delivery (IPD) team and examined the interplay between the two networks. The IPD format as an alternative method of building construction relies on its multiple stakeholders’ equal and active collaboration. Analyses of both endogenous and exogenous network variables found very distinctive tie formation dynamics between the formal and informal communication networks. While both networks were rather decentralized, a preferred structure for facilitating collaboration in IPD teams, reciprocal communication was identified only in formal (i.e., project-related information exchange), not in informal (i.e., social conversations) networks in valued exponential random graph modeling (VERGM). Ethnic heterophily, also a preferred structure for the IPD collaboration, was significant for formal, but not for informal communication networks. A small number of female members (4 out of 26) were more participating in formal, but less in informal conversations compared to males. Team members from designer and contractor organizations were active in project-related information exchange, but not as much in social conversations compared to the owner’s representatives. While a multiplexity effect was identified between formal and informal communication networks in VERGM, MR-QAP regressions revealed the cyclicality of each network significantly predicted the other type of communication frequency above and beyond its own structural configuration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".