Optimum risk allocation model for construction contracts: fuzzy TOPSIS approach
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
Risk allocation, a responsibility-sharing scheme for each party in a risk management process, is an important decision-making process for project success. Although previous studies have discussed risk allocation extensively, no comprehensive quantitative modeling approach for risk allocation exists. Such a model, specific for contract negotiation, would conduct authorities through the risk allocation process to determine the best risk-bearing participant. The purpose of this paper is to provide a quantitative model for the risk allocation process. This model should support decision making in risk management in a way that addresses the concerns of inappropriate risk allocation. Because linguistic principles and qualitative expert knowledge are the essential ingredients of any risk allocation process, the modeling scheme utilizes fuzzy set theory, which incorporates the quantification and reasoning of natural language.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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