An Integrated Productivity-Practices Implementation Index for Planning the Execution of Infrastructure Projects
Bibliographic record
Abstract
Productivity and project performance can be improved through implementing best practices. This paper describes the development of a best productivity practices implementation index (BPPII) for infrastructure projects. The index is a checklist of practices that are considered to have a positive influence on labor productivity at the project level for infrastructure projects. These practices have been grouped together into a formalized set of categories, sections, and elements. Each practice and its planning and implementation levels were defined and assigned a relative weight on the basis of its importance in affecting labor productivity. The productivity factor (PF), defined as a ratio of estimated productivity and actual productivity, was used as a metric to collect information about labor productivity to validate the accuracy of the BPPII for infrastructure projects. Data were collected for infrastructure projects in regards to their planning and implementation level of practices in addition to their PF and project schedule performance. The statistical tests confirmed that the higher implementation of best practices as defined in the index have a strong positive relationship with the PF and project schedule performance. Projects that have a high level of implementation of practices experienced better productivity and schedule performance than those having low implementation. This research contributes new insight into the relationships between sets of practices and project performance as well as a tool for planning practice implementation on infrastructure projects.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".