Bibliographic record
Abstract
As investment increases in capital projects, financial risks increase, and cash flow prediction and control become more paramount. Higher risks could hinder project performance and increase the chances of failure in multiple aspects of a project. While there are models that aim to assess and forecast risks in the construction industry, none present a technique to include the impact of risks on a project’s cash flow. Therefore, cash flow forecasts tend to exceed the actual cash flow of a project due to inaccurate risk assessment. Thus, this paper presents the Cash Flow Risk Index (CFRI) development process quantifying the impact of risks on a project’s cash flow from an owner’s perspective. To that end, the study explored the literature to identify the risk factors that might impact a construction projects’ cash flow and uncovered 44 factors. The study also validated and consolidated these factors to build a CFRI via a Delphi exercise, which reduced the factors from 44 to 36. In further iterations, the 36 factors were also shared with 32 construction industry professionals to rate their relative importance on a five-point Likert scale, from which relative importance index and weights were obtained. As a result, the CFRI was developed to measure the impact of different risk factors on a typical construction project’s cash flow.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".