Patterns of interaction in construction team meetings
Bibliographic record
Abstract
Sir John Egan's 1998 report on the construction industry (Construction Task Force 1998) noted its confrontational and adversarial nature. Both the original report and its subsequent endorsement in Accelerating Change (Strategic Forum 2002) called for improved working relationships—so-called ‘integration’—within and between both design and construction aspects. In this paper, we report on our observations of on-site team meetings for a major UK project during its construction phase. We attended a series of team meetings and recorded the patterns of verbal interaction that took place within them. In reporting our findings, we have deliberately used a graphical method for presenting the results, in the expectation that this will make them more readily accessible to designers. Our diagrams of these interaction patterns have already proved to be intuitively and quickly understood, and have generated interest and discussion among both those we observed and others who have seen them. We noted that different patterns of communication occurred in different types of meetings. Specifically, in the problem-solving meeting, there was a richness of interaction that was largely missing from progress meetings and technical meetings. Team members expressed greater satisfaction with this problem-solving meeting where these enriched exchanges took place. By making comparisons between the different patterns, we are also able to explore functional roles and their interactions. From this and other published evidence, we conclude that good teamworking practices depend on a complex interplay of relations and dependencies embedded within the team.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".