Facilitating team decision-making through reimbursable contracting strategies<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.
Bibliographic record
Abstract
Efforts to break down decision-making silos in the architecture, engineering, and construction (AEC) industry have resulted in the evolution of integrated project delivery (IPD). IPD brings together participants to make decisions early in the project cycle. But, anecdotal evidence indicates that IPD is not being implemented as effectively as envisioned. One potential barrier to implementation is the multi-party risk and reward agreement that is a hallmark of IPD. Many owners may be statutorily prohibited from entering into such a risk-sharing contractual arrangement, and countless other organizations “remain skeptical of its practicality.” However, recent case study research established an important link between reimbursable contracting strategies and greater collaboration and information-sharing among parties. The researchers found that more traditional contracting approaches, when combined with a cost reimbursable compensation structure, achieved positive outcomes by properly allocating risk, fostering teamwork, and bringing together diverse experts to address challenging design problems, thus providing a viable alternative to shared risk and reward approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".