THE SYNERGY OF OPTIMIZING PROJECT MANAGEMENT PRACTICES FOR QUALITY MANAGEMENT IN RESIDENTIAL BUILDINGS
Bibliographic record
Abstract
This paper provides a bibliometric study based on data from Scopus and the VOS Viewer program to investigate the relationship between optimization strategies, project management, and quality management in the context of residential constructions. With an emphasis on a number of factors, including publishing nations, years, fields, authors, citations, and keywords, the analysis encompasses 85 articles from 1995 to 2023. The main conclusions show that the top four nations in terms of the number of publications on this subject are China, the US, Australia, and Canada. The research reveals noteworthy patterns in the disciplines that have made contributions to this topic, suggesting that optimization in construction and management techniques is studied from a multidisciplinary perspective. In order to improve the performance of residential building projects, the paper highlights the growing significance of combining optimization techniques with efficient project management and quality assurance. Additionally, it lists key works and prominent writers who have influenced the conversation in this field. All things considered, this bibliometric study offers insightful information to scholars and professionals who are interested in the nexus of these important fields, indicating that interdisciplinary cooperation might result in better residential building project outcomes.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.060 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".