Developing a Framework for Assessing Team Alignment in Construction Using TVD
Bibliographic record
Abstract
Customers' value is crucial to the success of a construction project, and team alignment is required to steer projects toward their intended value.Alignment is when the right people work together on a project to generate and achieve values that are consistently communicated and accepted.In the architecture, engineering, and building industry, teamwork challenges are inevitable.The existence of a team does not guarantee the success of the project, and a dysfunctional team might result in project failure, wasting resources such as time, money, and energy.Target value design (TVD) is a lean approach that leads the design and construction phases to meet project objectives while adhering to team and project limits.Based on their values, each project has different conditions, facts, or impacts that help strengthen team alignment (factors).Additionally, a team that is aligned has particular qualities that are recognized as attributes.Measuring and assessing team performance based on TVD using factors is complex.This research fills the gap in the literature review concerning the measurement and assessment of team alignment.The process and its results could help construction project leaders regularly assess and identify team strengths and weaknesses to improve team alignment.A case study is also presented to apply the proposed framework to measure team alignment on a construction project, to improve team 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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".