Calibrating CII RT283’s Modularization Critical Success Factor Accomplishments
Bibliographic record
Abstract
Modularization is a well-known construction technique where sections of the job are moved from on-site to a fabrication shop. Previously, the researchers identified the 21 most influential Critical Success Factors (CSFs) for successful modularization and quantified the degree of each CSF’s accomplishment by conducting a survey with 19 subject matter experts, and a case study with three modular projects. Through this methodology, the findings relied on the experience of experts. However, still missing from the understanding of modularization CSFs is a validation of the current degree of each CSF’s accomplishment by examining actual modular projects using a sufficient number of samples. This paper presents a comparison of CSF accomplishment degree between 25 actual sample projects (project based) and the industry experts’ survey results (experience-based estimation). The study results indicate that the industry experts made a good estimation of the current occurrence degree of each CSF. The study did identify, however, three significantly overestimated CSFs and three underestimated CSFs. The three most overestimated CSFs are Owner-Furnished/Long Lead Equipment Specification, Cost Savings Recognition, and O&M Provisions. The three most underestimated CSFs are Contractor Experience, Management of Execution Risks, and Transport Delay Avoidance. This study will help the industry to 1) better understand modularization CSFs and their accomplishment statuses; 2) achieve higher modular project performance by accomplishing the CSFs
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".