Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it