Developing a Value Dashboard for Tracking Value Alignment during Design
Bibliographic record
Abstract
Delivering value to the customer and to the internal and external stakeholders on construction projects is deemed essential for projects’ success. However, literature is limited in reference to the methods offered to track value alignment on projects. Defining and agreeing on what constitute project value early on would help in providing a clear starting point to capture value generation along project phases. Accordingly, this research advises first on the main steps to evaluate and classify project value, then proposes a design for a value dashboard (VDB) to visualize and track value on projects. The suggested dashboard acts as a visual management tool to guide project managers with the decision-making process considering the evolving understanding of value. The advocated value dashboard for the design phase is discussed and vetted with experts from the construction industry. Future research will further test the VDB on real life projects to demonstrates its applicability. This visual dashboard is a novel tool that provides tracking for value generation and delivery, and it is complementary to the existing project management dashboards that focus on schedule and cost considerations.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".