Prioritizing Preproject Planning Activities Using Value of Information Analysis
Bibliographic record
Abstract
Preproject planning is becoming a widespread best management practice. Potentially, it can be an extremely time and resource intensive practice and, as such, presents challenges in the management, allocation, and prioritization of the resources applied to it. This research presents a novel solution to this challenge. The solution prioritizes preproject planning activities using the value-of-information analysis and simple optimization methods applied to a modified project definition rating index (PDRI). First, scope definition elements are identified from a PDRI, and expected cost-to-benefit ratios for each element are quantified. Then, an optimized resource allocation is performed to prioritize the elements in the scope definition improvement process. We demonstrate this framework in a case study for adaptive building reuse because these are complex projects whose overall success can be directly linked to effective preproject planning using constrained resources. Results of this case study find that optimizing preproject planning using the proposed methodology resulted in approximately $127,000 of cost-savings, representing 5% of the total project cost.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 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".