Unraveling the Phenomenon of Construction Labor Productivity: A Cutting-Edge Bibliometric Analysis
Bibliographic record
Abstract
Construction Labor Productivity (CLP) is a critical factor in the construction industry that can significantly influence the success of a project. The lack of comprehensive knowledge and proficiency in CLP and its substantial impact on project outcomes is a significant concern. CLP is a highly effective concept that can significantly improve the overall efficiency of construction projects. While it has received considerable academic attention, it is still relatively underutilized in practice. This academic analysis aims to identify trends within the CLP as revealed by a comprehensive review of existing academic research. A bibliometric analysis, complemented by a descriptive quantitative statistical methodology, was utilized to evaluate the performance metrics and delineate the thematic landscape about the subject matter of CLP. Employing distinct keywords pertinent to the CLP phenomenon and applying specific criteria for metadata exploration facilitated a comprehensive search within the Scopus and Web of Science databases. The PRISMA flowchart was employed as a systematic approach for the identification, screening, and eligibility evaluation of metadata to be incorporated into this study. The study highlights growing CLP research, led by the US and the University of Alberta. Key trends include “improvement” and “construction equipment,” with future focus on “machine learning” and “artificial intelligence.” Despite their potential, data challenges limit their use. More research is needed in developing countries to enhance construction 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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.195 | 0.193 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".