A Conceptual Model to Develop a Worker Performance Measurement Tool to Improve Construction Productivity
Bibliographic record
Abstract
Construction performance and productivity improvement are key focus areas in construction industry for any nation. However many researchers have observed a considerable decline in construction productivity during the last two decades. Hence there is a strong need to find innovative methods to improve construction productivity, in terms of both labor and management issues. The purpose of the paper is to develop a Worker Performance Index (WPI) to enhance construction productivity. Based on site observations, interviews and examining of the current worker performance rating systems, initial list of factors were identified. Motivation, technical skills and management factors have been explored to define and evaluate the worker performance index. In addition to that, this paper considers the supervisor's overall assessment of the worker as an additional critical input. WPI is expected to assist the supervisors to assess the performance of the workers quantitatively for pre-construction planning and continuous improvement of the labor productivity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".