Contractual Risks in Fast-Track Projects
Bibliographic record
Abstract
Fast-tracking strategies are used to achieve a shorter project duration; however, these strategies may negatively impact project performance by imposing additional risks, uncertainties, and costs. Rework, change orders and site modifications are almost inevitable in fast-tracked projects. Although these problems are not specific to fast-tracking, their frequency is relatively higher in this approach. Contracts should deal with these extra risks and the responsibilities associated with them, and assign them reasonably among project stakeholders as well. Currently, no contractual framework specific to fast-track projects is available; therefore, risks may not be allocated equitably to stakeholders. The usual consequence of the inequitable risk allocation is additional contingencies and premiums added by designers and contractors to their bid price which will end with greater overall project cost. In this paper, particular legal risks and challenges in fast-track projects are identified through a literature review. In addition, contractual aspects of fast-tracking are briefly reviewed at three levels: contract language; contract type; and project delivery method. The study shows that inaccurate cost estimating and cost overrun risk liability, liability for design errors and omissions, delay damages, change orders, construction rework and modifications, as well as risk liability for overlooked work are among the most common reasons for disputes in fast-tracking. The main purpose of this paper is to provide a better understanding of the contractual risks in fast-track projects and help to develop contract strategies and minimize the associated legal problems.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".