A Framework for Identifying and Measuring Competencies and Performance Indicators for Construction Projects
Bibliographic record
Abstract
In contemporary construction environments, employees and managers alike are faced with numerous pressures to carry out work to meet corporate expectations of performance. Continuous change and adaptation in organizational structures, practices, and technologies are conducive to successful management and execution of construction projects. Construction projects tend to measure how well they perform against a set of predefined performance indicators. These performance indicators are based on the ability of construction projects to attain necessary sets of "competencies" that enable successful execution of work. This paper identifies and classifies the different competencies and performance indicators that are used in construction projects and proposes a framework and methodology to identify and measure them. Appropriate measurement scales for the different competencies and performance indicators are developed, and a survey structure is proposed to collect data on the competencies and performance indicators from experts in the construction domain. A data aggregation method is introduced to combine experts' evaluation of construction projects' competencies and performance indicators. Last, the paper discusses future work pertaining to the development of a cascade fuzzy neural network that can predict different performance indicators for construction projects based on the identified competencies.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".