The Next Era of IPD Research: A Systematic Literature Review of The IPD Research Trends 2017-2020
Bibliographic record
Abstract
Abstract Questions around integration, innovation, and collaboration within project teams have been the focus of increasing research in recent years. This is in response to the notorious fragmentation and the inherent performance issues that face the construction industry. Among the solutions identified, new contractual approaches, namely, integrated project delivery (IPD), have been investigated to understand how they can help overcome the industry’s issues. This paper aims to contribute to the previous efforts that studied the research trends of the IPD from 2001 to 2016 by synthesizing the last four years from 2017 to 2020. This period accounts for more than 70% of research on IPD compared to the previous 16 years combined. A systematic literature review was conducted to understand the trends in IPD research over the last four years. The results allowed the research team to understand better where such research was being conducted, what type of projects were being studied, and most importantly, the topics being covered within this research and its results. This paper proposes an initial review that will be further developed in a full-length journal paper.
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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 | 0.041 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.034 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".