A research and development framework for integrated project delivery
Bibliographic record
Abstract
Integration and collaboration within construction projects are seen as responses to the construction industry’s inherent efficiency and performance issues. New project delivery methods, such as integrated project delivery (IPD), have emerged as potential solutions to enable this integration and collaboration to overcome the industry's inherent challenges. The number of studies published on IPD has increased rapidly in recent years, covering many aspects of this innovative approach. However, as IPD is still emerging, many questions remain around its components, their instantiation, and the areas of research and development needed to support their progression within academia and industry. This study aims to establish a comprehensive view of the current landscape of IPD research and identify the domains that are underrepresented through the development of an R&D framework. The framework aims to help both researchers and practitioners navigate the different components of IPD and guide research efforts to further their development. The R&D framework is built upon established frameworks and a systematic literature review of 175 papers from 2017 to 2022 and was validated using data from three case studies. The research findings identified a framework with six primary themes and 19 sub-topics that relate to both′ well-explored and under-researched aspects of IPD.
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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 | 0.097 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".